Search

The n -dimensional HSDC with the logic graph H n is one of the most attractive server-centric data center networks for high incremental scalability. The routing design in network topology is very important for information transmission. In particular, the application of disjoint path covers can solve various problems such as program code optimization and mapping parallel programs to parallel structures. H n consists of 2 n cliques and each two vertices in a clique are adjacent. For any vertex sets { x , u } and { y , v } in W and B , respectively, and any two disjoint vertex sets A 1 , A 2 in some clique, where (W , B) is a bipartition of H n , in this paper, we prove that, when | A i | ≠ 1 and A i ∩ { x , y , u , v } = ∅ for i = 1 , 2 , if | A 1 ∪ A 2 | ≤ n − 3 , then there exists an (x , y) -path P 1 and a (u , v) -path P 2 in H n passing through A 1 and A 2 , respectively, such that P 1 and P 2 have no common vertices and P 1 ∪ P 2 contains all vertices of H n. Then P 1 ∪ P 2 is called the 2-disjoint path cover in H n and we propose the efficient algorithms for the path design. Furthermore, we prove that our main result is optimal. • In the problem of data harvest and information transmission, it is very important to design the disjoint path cover in the logic graph of Data Center Network. • We consider the construction of 2-disjoint path cover such that each path should contain all prescribe vertices which have low probability of failure. • The efficient algorithms have been given and we prove that the number of prescribed vertices is not more than n − 3 and explain that this upper bound is sharp. [ABSTRACT FROM AUTHOR]

The rapid increasing of data centers calls for an efficient method to reduce high operation costs and carbon emissions. This paper proposes a cooperative online schedule framework for local interconnected data centers considering shared energy storage. A time-average optimization problem is built to reduce the overall operation cost with essential operational constraints. Multiple randomness and the temporal coupling constraints of energy system levels make it challenging to solve the stochastic problem. Adapting the Lyapunov optimization theory with relaxation and perturbation technique, an online algorithm is proposed to find an acceptable solution to the problem to achieve both good service coordination and efficient energy coordination. The algorithm does not require prediction of random parameters, and has a fast convergence rate. Especially, the theoretical conditions for convergence and the distance between the acceptable and real optimal solution are derived in a closed form. Simulation studies show that the algorithm can effectively reduce operation cost of data centers, carbon emissions, and dependence on the main grid. • A new microgrid consisting of local data centers and shared ESS is investigated. • A cooperative framework to realize service and energy coordination is provided. • The proposed online algorithm finds an acceptable solution without predictions. • The algorithm is compared with forward-looking algorithm and rolling algorithm. [ABSTRACT FROM AUTHOR]

This study presents a new data mining strategy to discover the performance and operational patterns of a shared energy recovery (SER) system with a data centre and a district heating network. Multidimensional clustering incorporated with a composite performance metric was first used to evaluate the typical performance of the system and reveal the interactions among different performance indicators. Decision tree analysis was then used to identify distinct system performances under different external conditions. Temporal clustering analysis was lastly used to identify the impact of recovered waste heat on the variations in heat supply from the district heating substation. The strategy was evaluated through a case study SER system at a university campus located in Norway. It was found that the most frequent performance accounted for 34 % of the total operational period with the instantaneous waste heat recovery rate of 572.9 kW, the temperature of waste heat of 57.2 °C, and the coefficient of performance of the heat pumps of 2.0. The outdoor air temperature and supply water temperature from the main district heating substation to the campus buildings showed a significant impact on the SER system performance. Moreover, the results showed that the SER system can help reduce the energy use of the district heating networks while increasing the fluctuations of heat supply from the main district heating substation. • A two-step clustering strategy is proposed to study shared energy recovery systems. • A new performance metric considering multiple indicators is presented. • The performance under different external conditions is quantified. • Unsupervised and supervised data mining are combined for performance evaluation. [ABSTRACT FROM AUTHOR]

As the demand for data centers rises, efficient cooling systems become increasingly vital to ensure uninterrupted operations and adaptability to IT service changes. The research focuses on quantitatively comparing the thermal performance and energy efficiency of cooling systems capable of handling high-density IT power. It aims to provide objective criteria for selecting suitable cooling solutions, including an assessment of three 150 kW-class IT and facility modules, a novel contribution not extensively explored in previous studies. The study evaluates room-based, row-based, and rack-based cooling options based on the configured ITE power density, ensuring that all three solutions meet ASHRAE's recommended IT operating environment through CFD analysis. The research reveals energy efficiency improvements, with PUE cooling decreasing from 1.33 for room-based cooling to 1.28 for rack-based cooling. These improvements are attributed to reduced fan power by CRAC/H type and decreased primary plant energy consumption by increasing COP through the supply chilled water temperature. The findings offer valuable insights for designing cooling solutions tailored to ITE power density and considering factors such as space requirements, thermal performance, energy efficiency, and cost for new hyperscale data centers. Overall, this research constitutes a significant academic contribution in the field of data center cooling solutions. • Efficient cooling systems to meet growing data center demand. • Comprehensive evaluation of room-based, row-based, and rack-based cooling options. • Quantitative comparison of thermal performance and energy efficiency in cooling solutions. • Compliance with ASHRAE's IT operating environment using CFD analysis. • Energy efficiency improvements, PUE cooling : 1.33 (room-based) to 1.28 (rack-based) [ABSTRACT FROM AUTHOR]

To better make use of the waste heat in distributed data centers, a new design of integrated waste heat recovery system that can meet different needs including heating for users and data center cooling is introduced. The system can switch among different operation modes including waste heat recovery mode, natural cooling mode, composite cooling mode and refrigeration mode based on the ambient temperature. The integrated waste heat recovery system is experimented in the enthalpy-difference laboratory, and the experimental results show well consistency compared with the modeling results. The operation strategies to switch among different modes are determined based on the experimental and modeling results. The paper introduced PUE (Power Usage Effectiveness) and ERE (Energy Reuse effectiveness) to comprehensively evaluate the cooling and energy reutilization effectiveness of the system. According to the evaluation result, the integrated waste heat recovery system has well performed energy effectiveness, environmental and economic effect. The ERE values of using the integrated waste heat recovery system in Chinese typical climate cities including Harbin, Beijing, Xi'an, Shanghai and Guangzhou are 0.87, 1.02, 1.04, 1.17 and 1.29 respectively. Using this system at 96 kW distributed data center instead of CRAC (computer room air conditioning) system can save 152.7 MWh electricity, 56.4 tons of standard coal, and 244.7 tons of CO 2 emission in Beijing annually. The payback periods of this new system in different climate regions in China are all within 3 years. • An integrated data center cooling and waste heat recovery system is proposed. • Operation performance of the system is verified by experiment and modeling. • Energy Reuse Factor in Beijing is 1.02, and payback period is within 3 years. [ABSTRACT FROM AUTHOR]

Data centers (DCs) uninterruptedly run 24/24 h, 365 days per year with much huge operating scale, and have the characteristics of high operation safety requirement, high heat flux density, high energy consumption and high carbon emission. They are influential energy consumers and carbon emitters in building or even global energy sectors (around 3% of global energy consumption), who are also significant waste heat producer (e.g., waste heat from year-round uninterrupted operation of IT equipment and cooling system). Huge energy consumption has increased the burden on the global energy industry, while carbon and direct waste heat emissions have also caused great damages to the outdoor environment. Thus, it is critical to improve the energy efficiency in DCs and to realize the energy conservations and environmental deterioration alleviation. Waste heat recovery technology is considered as a promising approach to improve energy efficiency, achieve energy and energy cost savings, and mitigate environmental impacts (caused by both carbon emission and waste heat discharge) at the same time. This article conducts a comprehensive review on recovering waste heat from all kind of sources (e.g., exhaust air, circulating water, and coolants) in DCs for various energy uses (e.g., heating supply, district heating supplement, cooling and electricity productions, and industrial/agricultural production process) and different application scenarios (e.g., office buildings, comprehensive energy community and residential buildings), while the future research and development proposals for DC waste heat recoveries are given through technical, energy, environmental and economic analysis. • Conduct a comprehensive review on waste heat recoveries in data centers • Consider various waste heat sources (exhaust air, circulating water, and coolants) • Regard various waste heat uses (heating, DH, cooling and power generation, etc.) • Regard various applications (office, residential buildings, and energy community) • Give recommendations for future study through technical and 3 E analysis [ABSTRACT FROM AUTHOR]

• Proposing a low-energy consumption data center matching with multi-energy sources. • Recovering PEMFC's discharged heat to produce cooling capacity. • The exergy efficiency of the proposed system was increased by 9.78%. • The conventional and advanced exergy analysis was carried out. With the rapidly development of information service and artificial intelligence technology, the demand for data centers has dramatically increased, resulting in an increase of energy consumption and carbon emissions. This study proposed a power and cooling combined system with waste heat recovery, flexible cooling and hydrogen energy utilization used in data center to achieve the carbon peak and carbon neutralization. The integrated system mainly included the low temperature-proton exchange membrane fuel cell (LT-PEMFC) and the half-effect absorption refrigeration system (HE-ABS). The lower-grade heat discharged from LT-PEMFC was utilized by HE-ABS system to provide the cooling power. The performance modeling and advanced exergy analysis of the combined system were carried out. It was showed that the total exergy efficiency of the integrated system achieved 60.55 % and the exergy efficiency of LT-PEMFC subsystem was increased by 9.78 % due to the waste heat recovery of LT-PEMFC. In order to investigate the improvement potential and the interaction between subsystems, the advanced exergy analysis of the combined systems was conducted. The results showed that the avoidable exergy destruction of high-pressure absorber and low-pressure absorber of HE-ABS accounted for 80.9 % and 68.95 % of the total exergy destruction of components, respectively. The endogenous exergy destruction of the PEMFC was greater than its exogenic exergy destruction, which indicated that the exergy destruction of PEMFC was caused by its irreversibility. The parameter analysis showed, as the operating temperature of LT-PEMFC was increased, the exergy efficiency of PEMFC first increased and then decreased, and the maximum exergy efficiency of PEMFC achieved 70.44 % when the operating temperature of LT-PEMFC was 363 K. [ABSTRACT FROM AUTHOR]

Loop heat pipe has the advantages of large heat transfer coefficient without extra power input, therefore it has a good application potential in chip cooling of data centers. In this paper, a compact flat-evaporator loop heat pipe is designed and the effects of key structural parameters and working conditions on the dynamic and steady-state performance are studied. The results show that increasing heat load or the filling ratio, arranging vapor channels in the middle of the wick (rather than at the bottom) or lengthening the condenser can all reduce the temperature fluctuation; A small inclination angle of 5° and 10° can reduce the temperature fluctuation and the stable temperature, while large inclination angle of 15° will instead bring higher stable temperature; When the heat load increases step by step, the stable temperature (average temperature for fluctuation conditions) decreases and then increases for all filling ratios and structures; Minimized thermal resistance is achieved by optimizing wick and condenser structure under low and high heat load, respectively. • Several structures and conditions of compact FLHP are compared for the first time. • Wick with vapor channels in the middle of it reduces the temperature fluctuation. • Wick with vapor channels in the middle of it obtains lower thermal resistance. • Small inclination angles reduce the stable temperature while opposite for large ones. • Optimized structure of FLHP varies with the heat load. [ABSTRACT FROM AUTHOR]

Road condition is an important environmental factor for autonomous vehicle control. A dramatic change in the road condition from the nominal status is a source of uncertainty that can lead to a system failure. Once the vehicle encounters an uncertain environment, such as hitting an ice patch, it is too late to reduce the speed, and the vehicle can lose control. To cope with unforeseen uncertainties in advance, we study a proactive robust adaptive control architecture for autonomous vehicles' lane-keeping control problems. In the proposed framework, the data center generates a prior environmental uncertainty estimate with a quantified uncertainty by combining weather forecasts and measurements from anonymous vehicles through a spatio-temporal filter. The prior estimate and quantified uncertainty contribute to designing a robust heading controller and nominal longitudinal velocity for proactive adaptation to each new abnormal condition. Then the control parameters are updated based on posterior information fusion with on-board measurements. [ABSTRACT FROM AUTHOR]

For the enhancement of the International Data Centre's products, specifically the Standard Screened Radionuclide Event Bulletin, an important step is to establish methods to associate the detections of the Comprehensive Nuclear-Test-Ban Treaty-relevant nuclides in different atmospheric radioactivity samples with the same radionuclide release to characterize its source for the purpose of nuclear explosion monitoring. Episodes of anomalously high activity concentrations in samples at the International Monitoring System radionuclide stations are used as the primary assumption for being related to the same release. For multiple isotope observations, the consistency of their isotopic ratios in subsequent samples with radioactive decay is another plausible hint for one unique release. The radioxenon observations that are associated with the nuclear test announced by the Democratic People's Republic of Korea in 2013 serve as case study to demonstrate the effectiveness of this basic approach and how the additionally associated samples improve the source location. We use two distinct puff releases, both of short duration, for the atmospheric transport modelling simulations to gain further evidence and confidence in our sample association study by identifying the air masses that link the releases to multiple samples. This basic approach will support the definition of analysis procedures and criteria for automatic sample association to be implemented in the Standard Screened Radionuclide Event Bulletin, which is of relevance for an expert technical analysis. • Consistency of isotopic ratios in subsequent samples with radioactive decay can be used for sample association check. • Bayesian statistics are useful for estimation of isotopic ratios and their uncertainties. • Atmospheric transport modelling simulations give further confidence for sample association check. [ABSTRACT FROM AUTHOR]

The French National Data Center (NDC) uses an automated simulation of the 133Xe worldwide atmospheric background as one of the means to categorize the radionuclide measurements of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) International Monitoring System (IMS). These simulations take into account 133Xe releases from the known or assumed major industrial emitters in the world and global-scale meteorological data. However, a quantification of the simulation uncertainties in this operational set up is yet to be addressed. This work discusses the benefits of meteorological ensemble data as available from National Centers for Environmental Prediction (NCEP) for that purpose. For this study, the daily dispersion of releases from the Institute for Radio Elements (IRE), a medical isotope production facility located in Fleurus (Belgium), was calculated over one year with emissions measured in-site and ensemble meteorological data. The ensemble contains 31 members, which resulted in as many predictions of activity concentration for any given time and place. The resulting distribution statistics (mean, median and spread), and the control run, were confronted to the deterministic run and to measurements at one IMS-like station near Paris (France) and one IMS station in Freiburg (Germany). Overall, the ensemble results have decreased the simulation performance, as expected given the use of meteorological analyses only. However, contrasting patterns were found with a detailed analysis of daily activity concentration over two one-month-and-a-half periods. Noticeably, outlier results were found to carry the best forecast in some significant detections, proving their relevance for the measurement categorization, despite their isolated character. Importantly, the ensemble has allowed the quantification of meteorological uncertainties, which was beneficial in all cases. It either has improved the confidence of IMS data categorization or has pointed to low confidence predictions. A criterion to identify the latter is suggested, based on information provided by the ensemble distributions. In addition, maps of probability of detections and of relative spread are suggested to show additional benefits of ensemble meteorology. • Ensemble versus deterministic meteorology in the global-scale simulation of the 133Xe industrial background. • Quantification of meteorological uncertainties ("error bars") and benefits for IMS noble gas measurement categorization. • Suggestion of a criterion to identify possible confidence in the simulation results at a specific location and time. [ABSTRACT FROM AUTHOR]

Accurate and effective wave height prediction plays a crucial role in harnessing marine energy, ensuring navigation safety, mitigating and preventing disasters, advancing scientific research, and so on. A hybrid Artificial Intelligence (AI) model Prophet_LSTM was proposed for effective wave height prediction in this study. First, the Prophet model is used to smooth the outliers in the original time series samples, and a large number of potential features were deeply mined. Then, the extracted information was used as a valuable source of augmented data, enhancing the training quality of the LSTM to improve its overall performance. Next, a sequence of procedures, encompassing normalization, grid search, and random search, was employed to identify the optimal parameters for the hybrid model in the wave height prediction. Finally, experiments were carried out based on seven years of data from station 44,025, 44,013, and 51,101 of the National Data Buoy Center (NDBC). The results show that the evaluation metrics of the Prophet_LSTM hybrid model are significantly improved compared with the traditional 1-h predictions technique and model. • A hybrid Prophet_LSTM model for ocean wave height prediction was proposed. • The Prophet model was employed to analyze the trend characteristics of the raw data. • Optimal parameters were determined through a combination of grid and random parameter searches. • The Prophet_LSTM outperforms other models in predicting performance across various input feature combinations. [ABSTRACT FROM AUTHOR]

In a highly technology-dependent society, processing large amounts of data has become essential, resulting in a growing number of data centers. The global data center industry accounts for a significant proportion of the world's energy consumption. A partial or total transition of the energy sources that power the operation of data centers to cleaner energy, particularly onsite renewable technologies, will help to achieve the greenhouse gas emission reduction goals established in international agreements. However, integrating such technologies is difficult due to the intermittency and availability of renewable sources. Nevertheless, temporal and geographical flexibility for processing part of the data centers' workload provides an opportunity to efficiently integrate these renewable energy sources. In this paper, we develop an operational scheme for a network of interconnected data centers with a significant electricity supply from onsite technologies consisting of (i) an optimal job migration and scheduling policy for data centers to match their operation and the availability of renewable energy under an emissions cap-and-trade program; (ii) an energy management system to control the data centers' onsite renewable and grid energy resources; and (iii) a robust optimization approach to account for the intermittency of the renewables and to identify the optimal migration and scheduling policies under the worst-case scenario. A case study is carried out to assess the economic and environmental impacts. The results indicate that the installation of onsite technologies and an optimal migration policy reduce the operational costs and the greenhouse gas emissions of the networks of data centers. • Job migration and scheduling for a data center network considering carbon emissions. • Energy management systems manage the data centers' onsite and grid energy resources. • Robust optimization is applied to integrate the intermittency of renewables. • Numerical experiments demonstrate the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Scheduling workflows in cloud computing is to find the most appropriate mapping from a series of dependent tasks to a set of available virtual resources so as to minimize or maximize some user concerned objectives, which is very significant to a sustainable and high-efficient cloud data center. However, it still faces challenges since its NP-hardness and the diversified requirements of both the cloud service consumers and cloud service providers need to be satisfied at the same time, especially in addressing multiple applications requested simultaneously. This work proposes a dual-mutation mechanism-driven snake optimizer for scheduling multiple workflows in the cloud for minimizing the makespan of each workflow under user pre-defined budget constraints. Firstly, a continuous optimization algorithm, namely Snake Optimizer (SO) is adopted into discrete optimization, i.e., workflow scheduling. Secondly, a task execution order aware fitness function is designed to reduce the gaps or waiting time between parent and child tasks within a workflow and thus reduce the total execution time of the workflow. Besides, we analyze the existing snake optimizer and adjust the parameters corresponding to different evolutionary stages to adapt to our considered problem. Finally, a dual-mutation mechanism is developed by introducing a non-improvement iteration number for each snake and applying a standard bit mutation operation to prematurely converging snakes and the individuals randomly selected from the remaining snakes so that the population diversity can be enhanced and more potential solutions can be explored. Experimental results on a set of real-world scientific workflows show that our proposed algorithm is of great superiority in constraint satisfiability, meaning that compared with its peers, it is always the first to find feasible solutions. [Display omitted] • Adopting Snake Optimizer into budget constrained workflow scheduling problems. • Designing a task execution order aware fitness function to better estimate makespan. • Modifying Snake Optimizer's parameters to adaptive ones at each evolutionary stage. • Proposing a dual-mutation mechanism to enhance the search diversity. [ABSTRACT FROM AUTHOR]

• Performances of present water source heat pump (WSHP) unit were compared. • Three types of WSHP heating system using data center waste heat are proposed. • Thermal and energy characteristics of three proposed systems were studied. • Matching rule of data center waste heat, ending user and WSHP system is obtained. The number and scale of data centers (DCs) in China are constantly expanding with the advancement of networking and informatization. A large amount of waste heat is dissipated at its end. If this waste heat can be recovered, then achieving carbon neutrality in DCs can be effectively accelerated. At present, water source heat pump (WSHP) are widely used in air-cooled and liquid-cooled DCs of different sizes for waste heat recovery. This study analyzes the equipment type and heating capacity of different brands of WSHP units. Three WSHP heating systems are proposed according to the different locations of waste heat enhancement (SE, TDE, UE). The coupling and matching relationship between user-side heat load demand, DC waste heat load, and energy efficiency of WSHP units under different systems is also investigated. Moreover, the law of energy consumption and energy efficiency changes is analyzed. Thus, this study provides a reference for the DC waste heat recovery system design and equipment development. The results show that the coupling matching relationship between user-side heat load demand, DC waste heat load, and heating COP of WSHP units is Q. N / Q. S = C O P ′ / C O P ′ - 1 = φ , where φ ranges from 1.1962 to 1.3642. The heating COP of the SE system is the greatest when user-side heat load demand is between 0 and 120 kW. The heating COP of the TDE system is the highest when the user-side heat load demand is between 1750 and 70000 kW. Furthermore, the heating COP of the three WSHP heating system types is related to unit brand when user-side heat load demand is between 120 and 1750 kW. [ABSTRACT FROM AUTHOR]

This process simulation study investigates the utilisation of cold energy from Liquefied Natural Gas (LNG) regasification for meeting the cooling and electrical demands of a data centre with 5000 racks. The study proposes a cogeneration system based on an organic Rankine cycle (ORC) to generate power and chilled water at 5 °C by using the cold energy released from a 5 MMTPA LNG regasification plant. The simulation results demonstrate that a double-stage ORC system, which has ethane and propane as working fluids, can fully satisfy the data centre's cooling requirement of 34.6 MW. Additionally, it produces 15.8 MW of power that can serve 46% of the data centre's electrical demand. The maximum energy and exergy efficiencies are 58% and 33%, respectively, when the system is optimised for the maximum work output. Economic analysis reveals a levelized cost of electricity of 0.04 US$/kWh and an internal rate of return (IRR) of 29%. Moreover, the cogeneration system leads to an annual reduction of 210 kilotons of CO 2 emissions, which makes it a promising and environmentally-friendly alternative. • Cogeneration system proposed to use cold energy in LNG as it is regasified. • Two stage organic Rankine cycle system with ethane and propane as working fluids. • Complete cooling load and partial electrical load (46%) of a data centre are met. • Power generation with estimated LCOE of 0.04 US$/kWh. • Proposed cogeneration system avoids 210 kilotons of CO 2 emissions annually. [ABSTRACT FROM AUTHOR]

Multiple geographically dispersed data center buildings have been increasingly positioned in renewable-rich areas due to their high energy consumption. This paper proposes an optimal multi-energy portfolio for zero carbon data center buildings in the presence of proactive demand response programs. In the data center buildings, the complementarities of hydro-solar-wind hybrid renewable energy sources (RESs) are fully exploited via multi-energy conversion and storage devices, which are modeled as 100% renewable energy hubs for zero carbon multi-energy supplies. In order to exploit the hub-internal operational dispatchability and flexibility, a comprehensive energy consumption model of data center buildings is proposed to facilitate the demand response in terms of available serves, while the variable speed pumped storage (VSPS) is modeled to capture its dynamic operational characteristics. The energy‑carbon production, conversion, storage, and consumption are formulated as an energy‑carbon matrix, which are solved via a two-stage method to obtain the optimal portfolio. Case studies on southwest China data center buildings are implemented to demonstrate the effectiveness and superiority of the proposed methodology on cost-effective accommodation of RESs. Simulations results show that the system portfolio cost can be reduced by at most 17.8% with a higher operational flexibility. • A cost-effective data center building is proposed to accommodate zero carbon targets. • Hydro-solar-wind 100% renewables are formulated for multi-energy supplies. • VSPS and data center energy consumption are modeled to exploit inherent flexibility. • The energy-carbon production, conversion, storage, and consumption are optimized. [ABSTRACT FROM AUTHOR]

Recently, waste heat recovery has enabled data centers to serve as energy prosumers with flexible features that play essential roles in the coordination operation of integrated electricity–heat systems (IEHSs). The energy hub, as a critical component of IEHSs, provides a promising opportunity to improve energy efficiency. In this paper, the decentralized coordination optimization for IEHSs with data centers is explored to protect the confidential information of different entities. First, a novel data center based energy hub (DCEH) model is developed, where energy conversion, consumption and storage present high flexibility. Especially, in addition to heat recovery, several other controllable operational characteristics of a single data center are considered, involving servers, workloads and indoor temperature. Then, the coordination optimization model of IEHSs with DCEH (IEHSs-DCEH) is constructed by incorporating energy consumption cost and carbon emission, where the energy networks of IEHSs are considered. Moreover, a learning-aided relaxed alternating direction method of multipliers (LR-ADMM) algorithm is proposed to solve the dispatching model of IEHSs-DCEH considering communication packet loss. The proposed LR-ADMM algorithm embeds a momentum extrapolation based prediction technique, which can obtain the predicted value of missing boundary information without adding computational burden, even in continuous packet losses. Simulation results demonstrate that the developed coordination dispatching model can achieve higher economic and environmental benefits than the reference one that ignores data centers' flexibility. Additionally, the proposed LR-ADMM algorithm with proper prediction factors exhibits a faster convergence rate and robustness hedging against packet losses compared to the ADMM and relaxed ADMM approaches. • A highly flexible data center based energy hub model is developed. • Impacts of the flexibility in the data center based energy hub are investigated. • The coordination of integrated energy systems with data centers is optimized. • A learning-aided decentralized algorithm is proposed hedging against packet loss. [ABSTRACT FROM AUTHOR]

The energy demand of the data center (DC) industry has accounted for 2% of the total global energy consumption, and its operating power consumption has reached 50 times that of the commercial buildings. Therefore, improving the thermal environment performance to reduce the energy cost is major trend to realize the energy savings of DC. It has become a positive measure to integrate smart technologies to improve the operating efficiency of HVAC systems, especially due to the development of informatization and intelligence. This study provides detailed overview of the dynamic thermal environment management technologies of the DCs. The requirements of thermal environment in DCs were reviewed by different levels, which are the basis of the design and operation. Accordingly, various dynamic prediction technologies were presented concerning the theoretical mechanics, the computation efficiency and the applicable circumstances. The performance of the PID, the model predictive control and the reinforcement learning in the dynamic thermal environment control of DC were analyzed. Moreover, the evaluation systems based on cooling capacity, exergy analysis and entransy analysis were introduced respectively to measure the performance of the thermal environment. By investigating the knowledge related to the "requirement, prediction, control, and evaluation" of the DC dynamic thermal environment, this study aims to provide reference and guidance for energy conservation, airflow organization and smart operation of DCs. [Display omitted] • Studies related to DC dynamic thermal environment management are reviewed. • The requirements of DC indoor environment of different countries are summarized. • Pros and cons of various prediction and control technologies are analyzed. • The evaluation system based on energy, exergy and entransy is introduced. • Systematical guidance for improving the modernized DC performance is provided. [ABSTRACT FROM AUTHOR]

Depletion of fossil fuel reservoirs, greenhouse gas emissions' impact on global warming, and rising energy costs are pushing the data center sector to reduce energy use. This paper reviews strategies for improving the energy performance of air-cooled systems in datacom facilities and enhancing temperature and flow distribution in white space by analyzing different airflow delivery architectures (hard floor and raised floor designs), eliminating cold and hot air mixing by incorporating cold/hot aisle containment or exhaust chimneys, and potential energy savings achievable by utilizing evaporative cooling systems. It was found that the optimal ventilation system is hard-floor architecture with locally-ducted supply and return air. Hard floor is less complicated than raised floor design, and overhead supply air can minimize hot spots at the top of racks. Airflow management strategies including cold and hot aisle formation, aisle containment, and exhaust chimneys can reduce annual cooling energy usage by 10–50% in conjunction with air-side and water-side economizers, minimize hot spots, and enhance thermal performance during cooling system failure. Hot-Aisle Containment or exhaust chimneys provide better thermal and energy performance than open and/or cold-aisle containment, but they require new ducting in traditional data centers. Depending on the climate and geographical location, evaporative cooling can reduce annual cooling energy usage by 20–70% and lead to Power Usage Effectiveness as low as 1.06. Evaporative coolers are more suitable for dry climates, but this limitation can be ameliorated by incorporating desiccant wheels and thermal energy storage. [Display omitted] • A hard-floor layout with locally ducted supply and return air is optimal. • Aisle containment can lower annual cooling load factor by 40–50% and PUE by 16%. • Evaporative cooling can lower cooling energy by 20–70% and PUE to as low as 1.06. • Hot-Water cooling and desiccant-assisted EC can reduce demand by 95%, cooling energy by 84%. [ABSTRACT FROM AUTHOR]

Emergency cooling systems are an essential part of data centers. A water tank is usually used as an emergency cooling source to provide cold thermal energy; however, tanks are bulky and additional uninterrupted power supplies (UPSs) are needed. For flexible emergency cooling, a novel thermosyphon integrated with a latent thermal energy storage condenser (TLTESC) is developed and experimentally studied. The effects of the refrigerant filling ratio and inlet conditions on the dynamic thermal performance are analyzed. With an increase in the refrigerant filling ratio from 48.7% to 82.5%, the cooling capacity decreases; the maximum cooling capacity decreases from 4.68 to 2.03 kW. The superheating temperatures for all cases are always zero, indicative of the two-phase refrigerant being at the evaporator outlet. During the entire operating period, the refrigerant temperatures at the vapor line are considerably higher than those at the liquid line. Moreover, the refrigerant pressure at the evaporator inlet is the highest. Under the optimal filling ratio, the outlet air temperature increases and the maximum cooling capacity increases from 3.6 to 4.8 kW with the inlet air temperature increasing from 30 to 40 °C. The cooling capacity increases with air flow rate during the first half, after which the situation is reversed. The accumulated energy increases slightly as the air flow rate increases. The thermal performance is investigated to promote the application of TLTESC in data center. • Thermosyphon integrated latent thermal energy storage is proposed and experimentally studied. • The maximum heat transfer rate decreases with refrigerant filling ratio ranging from 48.7% to 82.5%. • With the increase of refrigerant filling ratio, the refrigerant mass flow is larger and more stable. • The accumulated thermal energy increases with inlet temperature, air flow rate, and refrigerant filling ratio. [ABSTRACT FROM AUTHOR]

• Comparative study among CO 2 , R134a and R410A TPTLs was conducted. • Operation state, optimal filling ratio, and thermal performance. • CO 2 TPTL was more energy efficient used in data centers. • CO 2 TPTL has a slightly larger normal working load range than the others. The two-phase thermosyphon loop (TPTL) is an effective cooling technology used in data centers. TPTLs filled with different working fluids typically have various operating characteristics that influence their practical applications. A comparative experiment was conducted on CO 2 , R134a, and R410A TPTLs, and differences in their operating states and thermal performances were analyzed. The optimal filling ratio of the CO 2 TPTL was 45%, while that of the R134a and R410A TPTLs was 35%. At their optimal filling ratios, all the three TPTLs successively underwent the pre-start, oscillatory, and stable operation stages as the heat transfer quantity increased. The oscillations of the CO 2 TPTL were periodic, whereas those of the R134a and R410A TPTLs were irregular. The normal working load ranges of the CO 2 , R134a, and R410A TPTLs were 550–1200, 760–1160, and 790–1300 W, respectively. When the heat transfer quantity was 1000 W, the driving temperature difference of CO 2 TPTL was 2.4 °C lower than that of R410A TPTL and 6.0 °C lower than that of R134a TPTL. [ABSTRACT FROM AUTHOR]

The network security issue of university laboratories needs to be taken seriously, as there is a large amount of academic data stored in the laboratory. In order to ensure the network security of university laboratories, a series of measures need to be taken. This article uses anomaly intrusion detection technology to optimize the network of university laboratories and designs a network management system for university laboratories. The system adopts cloud computing technology and data center technology, supports software clusters and hardware clusters, and adopts an advanced SOA system framework. The design and implementation of this system effectively improves the security and stability of the university laboratory network. Using anomaly intrusion detection technology, we can effectively identify and prevent abnormal network intrusion, and protect the security of Laboratory equipment and data. [ABSTRACT FROM AUTHOR]

Cloud computing has evolved as a new paradigm in Internet computing, offering services to the end-users and large-organizations, on-demand and pay-per-the-usage basis with high availability, elasticity, scalability and resiliency. In order to improve the performance of the Cloud system, handling multiple heterogeneous tasks concurrently, an appropriate task scheduler is required. To meet the user's requirements in terms of Quality of Service (QoS) parameters, the task scheduling algorithm should identify the order in which tasks should be executed. Energy efficiency is the significant challenge in today's task scheduling to meet the prerequisite for green computing. By increasing resource utilization at the data centers, virtual machine (VM) Consolidation is also recognized as the most widely used and promising approach in terms of energy consumption and system performance. However, excessive VM Consolidation could constitute a violation of the Service Level Agreement (SLA). The paper makes a contribution by outlining the numerous approaches that researchers have used thus far to achieve the goals of scheduling and VM Consolidation, assuring energy efficiency, and maintaining system performance. This would give readers a better understanding of the problems and the potential for improvement while assisting them in selecting the ideal scheduling algorithm with Consolidation technique. Additionally, the techniques are divided into three categories: those that primarily focus on task scheduling; those that target Consolidation; and complete computation, integrating task scheduling with VM Consolidation. Further classification for the scheduling algorithms include heuristic, meta-heuristic, greedy, and hybrid task scheduling algorithms. In addition to a summary of the benefits and drawbacks of the suggested algorithms, prospective research directions and recent developments in this area is also covered in this paper. [ABSTRACT FROM AUTHOR]

Integrating evaporative cooling with loop thermosyphon can significantly improve the free cooling ability. In this paper, a loop thermosyphon with evaporative condenser is investigated experimentally. The mist water flow out of a single nozzle is observed and analyzed. The performance and annual free cooling potential of the system are investigated, compared with conventional loop thermosyphon. The results show that the evaporative cooling effect of the locations below the horizontal level of the nozzle is more significant due to gravity. With the increase of horizontal distance from the nozzle, the temperature decreases and then increases and the optimal distance is 200–400 mm. The heat capacity of LTEC increases with the increase of indoor and outdoor temperature difference while it decreases with the increase of humidity. Evaporative cooling can enhance the heat transfer of LTEC by 7%–33% compared with loop thermosyphon with conventional condenser, and this value is larger for smaller indoor and outdoor temperature difference and higher indoor temperature. LTEC can expand the annual free cooling time by 7%–14% compared with LTCC and the effect is more significant in regions with drier weather. • Loop thermosyphon with evaporative condenser (LTEC) is studied experimentally. • Temperature field and flow field of the water spray out of a single nozzle are analyzed. • The distance with the best evaporative cooling effect is 200–400 mm from the nozzle. • The heat transfer of LTEC is 7%–33% larger than LTCC. • The annual free cooling time of LTEC is 7%–14% larger than LTCC. [ABSTRACT FROM AUTHOR]

Accurate refrigerant charge fault estimation is important to ensure the efficient operation of air conditioning systems. This paper presents a novel hybrid model based refrigerant charge estimation approach. Firstly, an improved gray box model is presented, which integrates the key characteristic variables of subcooling temperature, superheat temperature, quality and pressure drop. Secondly, three extra variables having highest maximal information coefficients with the prediction residual are used to extend the gray box, and the robust machine learning model is developed using the gradient boosting decision tree algorithm. Then, a hybrid model is presented by combining the improved gray box and machine learning models. Finally, the prediction and generalization capacities of the proposed models under various operation conditions are validated using the experimental data. The results show that the hybrid charge fault estimation model has the best performance. Its overall prediction and generalization MREs are 2.53% and 3.09%, respectively. [ABSTRACT FROM AUTHOR]

The service life, safety, and capacity of lithium-ion power battery packs relies heavily on the consistency among battery cells. Grouping is an effective procedure to improve consistency by screening cells with similar performance and assembling them into an identical group. Battery grouping can be achieved via clustering techniques based on characteristics like static capacity, internal resistance etc. The dynamic characteristics-based method considers the battery performance during the entire charging-discharging process and has become one of the most promising grouping method. However, it suffers from high computational complexity. Nowadays, facing stricter quality standards and the increasing demand for battery products, existing dynamic characteristics-based grouping scheme cannot meet the performance and time requirement of the modern high-quality, large-scale battery manufacturing. In this paper, a novel grouping scheme based on distributed time-series clustering is proposed to match the need of both efficiency and consistency improvement. The proposed scheme designs an effective "cloud-edge" mode and utilizes an innovative two-stage trick to achieve parallel processing, which split the original centralized clustering approach into local clustering and global merging. The host computers for data acquisition and battery control are regarded as distributed edge computing resources to implement local clustering on the acquired battery discharging voltage sequence set. Cluster contours are extracted via a denoising contour extraction algorithm considering the irregularity of the discharging voltage sequences. The results of the above preliminary processing are uploaded to a cloud data center. A pragmatic merging scheme based on an integrated merging indicator is established to solve the decision-making problem of global merging on the cloud data center. The final global cluster set is transmitted back to host computers to instruct cell unloading operation. Experimental results based on real battery discharging voltage sequence data suggest that the proposed scheme can reduce the inconsistency rate by 43.56% and the time cost by 92.87%. The computing efficiency and resource utilization rate of the distributed scheme is much higher than the centralized scheme. Meanwhile, compared with three existing advanced grouping approaches, our scheme perform the best in reducing inconsistency rate. [ABSTRACT FROM AUTHOR]

Timeliness and reliability are two major requirements of industrial applications. Hence, these two requirements should be carefully taken into account in design of a smart factory. Using a hybrid Cloud (combining a public Cloud with a private Cloud) is a well-known method to enable Cloud computing to provide reliability and timeliness. However, for strict time-sensitive applications using a hybrid Cloud is not enough to guarantee meeting their hard-deadlines. Supplying an intermediate computing layer –called Fog – between the factory and the Cloud is a promising solution to deal with reliability and latency requirements of strict time-sensitive applications. Integration of a Cloud data center, a private local Cloud, Fog nodes, and edge nodes constitute a very complex multilayer computing model in a smart factory. The goal of this paper is to provide a resource provisioning scheme for partitioning of a given workload among these multiple computing layers subject to reliability and real-time requirements. Partitioning of the workload can provide us prominent design decisions specifying how much computing resources are required to develop a local private Cloud in cooperating with Fog nodes like networking devices, how large should the minimum communication bandwidth be between the Fog and the public Cloud data center, and how many replicas for each application are required to satisfy the reliability requirement of the considered application. To evaluate the proposed method, we have conducted a set of experiments ranging from small scale to large scale scenarios. The results indicate that in 86 percent of the small scale experiments, the proposed algorithm exactly found the optimal solution achieved by a Branch and Bound (BB) based exhaustive search algorithm, while there is an improvement of around 85 percent on the execution time of our proposed method compared to the exhaustive search method. [ABSTRACT FROM AUTHOR]

Allocating insufficient resources to the cloud applications, leading to the loss of revenues, consumers and, Quality-of-Services (QoS). Another side, allocating resources more than is needed leading to the wastage of energy and cost to maintain the resources like servers, datacenters and, network bandwidth etc. So, this problem can be solved with predictive scaling methods by using machine learning approaches, which can estimate the future needed resources and, performing scaling operations in advance. To perform accurate scaling operations, authors presented an enhanced prediction model based on a neural networks, which is a combination of a classification and prediction model to predict the accurate utilization of resources. At the first stage, the proposed model categorizes the resources into three classes like over, normal and, under adaptively based on the utilization of resources. In the next stage, it predicts the future utilization of resources by using neural network regression model based on the classification results. The experimental results showed that the proposed model achieved accurate results. [ABSTRACT FROM AUTHOR]

Through large scale deployment of cloud data centers, burning up of energy by data center is very high and violations of service level agreements (SLA) are increasing. Implementation of Virtual machine consolidation techniques direct to physical host over exploitation which degrades the performance of physical host and Virtual machine. It has been a momentous confronts to get better exploitation of resources and energy effectiveness in cloud data center. This paper proposed knowledge based reduction method for Virtual machine assignment and consolidation. This method chooses the Virtual machines from the overloaded host and migrate the Virtual machines to other physical host to diminish energy burning up and get better quality of service. To provide evidence and effectiveness of proposed method, the experiment is conducted with diverse workload circumstances of real system. The experimental consequences exhibits that the proposed method diminishes energy burning up and optimizes infringement of service level agreements in achieving better performance. [ABSTRACT FROM AUTHOR]

Cloud computing is an application of the storage of items used as network services. On their virtual machines, cloud users can access hacker technology that used to emphasize back doors in cloud security. For their ability to replicate hardware environments and share resources, virtual machines are quickly replacing personal computer infrastructures. The hacking and denial of service attacks were revealed to physical devices. On their Virtual Machines, Cloud users can download vulnerable programs, leading to cloud security violations. There are many safety issues that need to be addressed in a partial security environment. There is a data centre in the existing system, where system administrators have full control over the host computers, which can be monitored in a hierarchical way by the system administrator. Patching known cloud safety holes data centre where cloud users are usually entitled to control the software installed on their virtual machines. Essentially it may not operate and may breach the Service Level agreement. We can't find the detection of intrusion. Virtual machines are vulnerable to denial of service attack cloud users can set up susceptible software on their Virtual Machines which actually leads to cloud security violations. Network intrusion detection and countermeasure choice combines intrusion detection processes with attack map analytical procedures. If an attacker targets the server using a user account, the attacker can add multiple levels of malware to the database if and only if he can access the server, but because of the server cloud service it is difficult to detect the intruder in the existing system. In the proposed system, If the attacker's server with the attack user account finds the attacker and administrators to warn the user's attempts (zombie attack) to access another user account to install malware and attempts to manage the largest multi-level wait, then administrators will stop it. [ABSTRACT FROM AUTHOR]

We study scheduling on multiple two-stage flowshops in which each job has to pass through an R -operation and a T -operation. Motivated by the current research in data centers, we consider two restricted versions of the problem in which the jobs are inclined: one restricts that for each job, the R -operation consumes no less time than the T -operation, while the other assumes that the T -operation consumes no less time than the R -operation for each job. For the first case, we present an online 2-competitive algorithm and an offline 11/6-approximation algorithm. For the second case, we give an online 5/2-competitive algorithm, and prove, for the offline setting, that the problem can be reduced to the problem in the first case. • This paper studies the problem of scheduling two-stage jobs on m multiple two-stage flowshops to minimize the makespan, where m is part of the input. • Motivated by practical applications in data centers, we study two restricted versions of the problem where both versions are strongly NP-hard. • For the case where each job has its R -time no smaller than its T -time, we present a 2-competitive algorithm and an 11/6-approximation algorithm. • For the case where each job has its T -time no smaller than its R -time, we give a 5/2-competitive algorithm and an 11/6-approximation algorithm. • To the best of our knowledge, this paper is the first that studies the scheduling problem on m two-stage flowshops where m is part of the input. [ABSTRACT FROM AUTHOR]

Almost thirty percent of the power consumed by data centers (DCs) is attributable to the cooling of IT equipment (ITE). There are opportunities to reduce a DC's energy budget by considering alternatives to traditional cooling methods, which experience inherent airflow deficiencies due to hot air recirculation and cold air bypass. Minimizing these two air distribution problems results in more effective cooling, but the two effects are manifest differently in the three conventional DC cooling architectures, i.e., (a) room-based, (b) row-based, and (c) rack-based cooling. Despite the intuitive logic that predicts improved cooling air distribution within row- and rack-based architectures that include shorter airflow pathlengths compared to room-based systems that have longer paths, the mechanism through which improvements translate into energy savings is not well understood. Therefore, we present methodologies that resolve the characteristic airflow and temperature distributions for three cooling architectures using computational fluid dynamics. These results inform thermodynamics models of the power consumptions that are required to cool these three architectures. The analysis reveals that row- and rack-based architectures reduce cooling power by much as 29% over a room-based architecture. Adding an enclosure within row- and rack-based architectures to separate the hot and cold airflows provides further 18% reduction in cooling power. This analysis facilitates better DC design from a cooling power consumption perspective. • Row and rack-based systems cool data centers at higher supply air temperatures. • Row and rack-based systems cool data center with lower cold air flowrates. • Recirculation R and B bypass are examined through dimensionless numbers. • Shorter airflow path lengths between the cooling unit and servers reduce R and B. • Enclosed row and rack-based cooling systems are most energy efficient. [ABSTRACT FROM AUTHOR]

Data centers are estimated to have the fastest growing carbon footprint from across the whole information and communication technology (ICT) sector. Evaluating the performance of data centers in terms of energy efficiency and sustainability is becoming an increasingly important matter for organizations and governments (e.g., for regulation or reputation purposes). It nonetheless remains difficult to achieve such evaluation, as data centers imply to take into consideration a wide range of dimensions and stakeholders. Even though a wide range of sustainability performance indicators exist in the literature, there is still a lack of frameworks to help data center stakeholders (spanning from data center owners, governmental regulators to engineers/field operators) to evaluate and understand how a data center performs in terms of sustainable development/behavior. Our research work proposes such a framework, whose originality lies in the combination of state-of-the-art sustainability metrics with the biomimicry commandments of eco-mature system, which enables holistic sustainability assessment of data centres. From a theoretical perspective, the proposed model is designed based on a benefit-cost analysis using the Analytic Hierarchy Process (AHP) technique. This approach allows data center stakeholders for specifying their own preferences and/or expertise in the comparison process, whose practicability is demonstrated in this paper considering three data center candidates, which are respectively located in France, Germany and Sweden. • A new model for comparing data center sustainability performance is proposed. • The model makes use of state-of-the-art sustainability metrics together with biomimicry lessons. • The model is designed based on a benefit-cost approach using the AHP technique. • A comparison study is carried out with three distinct data center candidates. [ABSTRACT FROM AUTHOR]

• Equation-based object-oriented Modelica models for data center cooling systems are built and released in the open-source Modelica Buildings library. • Together with the Electrical package in the Modelica Buildings library, the introduced package can simulate the thermal dynamics as well as the electrical dynamics for conventional and renewable data centers. • New strategies toward emergency operations for the data center cooled by chillers and waterside economizers are investigated. In this paper, we introduce a newly developed open source data center package in the Modelica Buildings library to support modeling and simulation of cooling and control systems of data centers. The data center package contains major thermal and control component models, such as Computer Room Air Handler, Computer Room Air Conditioner, models of different subsystem configurations such as chillers with differently configured waterside economizers, as well as templates for different systems. Two case studies based on this package are performed to investigate the performances of the cooling and electrical system under normal conditions and emergency situations such as a blackout: one is for a data center powered by conventional energy, and the other is for a data center powered by renewable energy. Simulation results show that the dynamic modeling and multi-domain simulation in the Modelica-based tool make it convenient for users to investigate both normal and emergent operations for conventional and renewable data centers. [ABSTRACT FROM AUTHOR]

Objective: To determine social factors associated with advanced stone disease (defined as unilateral stone burden >2 cm) at time of presentation to a regional stone referral center. Little is known about social determinants of urolithiasis. We hypothesize that socioeconomic factors impact kidney stone severity at intake to referral centers.Methods: A retrospective review of the prospectively collected data from the Registry for Stones of the Kidney and Ureter from 2015 to 2018 was conducted to evaluate patient characteristics predictive of having a large (>2 cm) unilateral kidney stone. Data on patient age, gender, body mass index, diabetes, race, language, education level, infection, distance, income, referring regional urologist density, American Society of Anesthesiologists score, and stone analysis were evaluated.Results: Complete imaging and patient variable data was present in 650 of 1142 patients including 197 patients with unilateral stone burden >2 cm. On multivariate analysis, obesity, lower education level, increased distance from the referral center, and symptoms of infection predicted for unilateral stone burden greater than 2 cm. Among 191 patients with stone analysis data present, stone type, income, and urologist density predicted for unilateral stone burden greater than 2 cm.Conclusion: In addition to known biological risk factors, patients with lower education levels and from regions of lower mean income were found to be more likely to present to our tertiary care center with stone burden greater than 2 cm. More research is needed to elucidate the social and societal determinants of advanced stone disease and the impact this has on population costs for stone treatment. [ABSTRACT FROM AUTHOR]

• Many servers and network devices are idle or light-loaded, which causes energy waste. • It is necessary to carry out energy saving in green data center (GDC). • The work proposed an energy-saving strategy based on artificial bee colony (ABC). With the development of data center around the world, its energy consumption becomes an increasingly urgent issue, and green data center (GDC) emerges. Most modern data centers are operated in terms of peak load, with large scales. However, many servers and network devices are idle or light-loaded for a long time, which causes energy waste. Therefore, it is necessary to carry out energy saving in GDC. The work studied the optimization of energy consumption of switches, proposing an energy-saving strategy based on artificial bee colony (ABC) for Fat Tree. The simulation results showed the proposed algorithm has obvious effects on energy saving and completion time in large and small traffic, which embodies the superiority and effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

• An attractive option for reducing energy consumption is consolidating the virtual machines in minimum numbers of physical machines and switching the unused ones off. • A survey of the various algorithms and techniques are proposed to improve energy efficiency via VM consolidation. • These techniques are focused upon and compared with regards of different performance criteria. • We argue the need for future work that overcomes the major problems faced by the cited approaches. The rapid growth of Cloud computing is accompanied by a significant increase in the consumed energy by data centers. This huge increase in energy consumption has become a major concern because of both its costs and environment impact. Consolidating virtual machines (VMs) in minimum numbers of physical machines (PMs) is an effective way to reduce energy consumption. Various algorithms and techniques have been proposed to improve energy efficiency via VM consolidation. However, recent findings of Green Peace showed that energy demands of data centers are continuing to grow annually. This continuous increase queries the area of reducing energy consumption of data centers. As an attempt to figure out the reasons behind the gap between Green Peace recent findings on energy consumption and the proposed VM consolidation techniques and to bridge this gap, we undertake this survey. We discuss these techniques by highlighting their strengths and limitations and we justify the need for taking into consideration the energy overhead created by both the VM migrations and PMs transitions in and out of the low power mode. [ABSTRACT FROM AUTHOR]

• The paper presents Self-organizing Green Virtual Network Embedding (SoGVNE) method as a embedding algorithm based on supervised self-organizing map (SOM) neural network. • Proposed SoGVNE uses virtual nodes historical data to learn the mapping process in the future. • The paper uses SOM connected lattice network to be aware of data center network topology. • The paper updates the SOM weight matrix in VNE process to be self-organized. • The paper uses renewable energy resources whenever they are available. Virtualization in the data center network is used to overcome the resistance of Internet ossification. Node mapping in virtual network embedding (VNE) process is more important than link mapping because each node mapping has a lot of choices with high probability. Historical data always have valuable information which displays how to be mapped virtual networks with special features. Previous works use physical network historical information, and they do not pay attention to the characteristics of virtual networks during the embedding process. To address this problem, this paper proposes SoGVNE (Self-organized Green Virtual Network Embedding) which uses previous successful virtual node mapping information to train a self-organizing map (SOM) neural network. Then, it will respond to future requests quickly and with fewer resources. SoGVNE uses virtual networks historical data and is able to embed more than one virtual network concurrently. Using supervised SOM makes the SoGVNE aware of resources with renewable energy and uses them to manage energy consumption. This paper evaluates SoGVNE with reinforcement-learning, Monte-Carlo and Presto methods. The results indicate the high efficiency of the proposed method in learning the node mapping process and achieving high profit and acceptance ratio with low cost. [ABSTRACT FROM AUTHOR]

A kind of generalization of diagnosability for a network G is g -good-neighbor diagnosability which is denoted by t g (G). Let κ g (G) be the R g -connectivity. Lin et al. (2016) [17] and Xu et al. (2017) [29] gave the same problem independently that: the relationship between the R g -connectivity κ g (G) and t g (G) of a general graph G needs to be studied in the future. In this paper, this open problem is solved for general regular graphs. We firstly establish the relationship of κ g (G) and t g (G) , and obtain that t g (G) = κ g (G) + g under some conditions. Secondly, we obtain the g -good-neighbor diagnosability of data center network D k , n which are t g (D k , n) = (g + 1) (k − 1) + n + g for 1 ≤ g ≤ n − 1 under the PMC model and the MM* model, respectively. Furthermore, we show that D k , n is tightly super (n + k − 1) -connected for n ≥ 2 and k ≥ 2 and we also prove that the largest connected component of the survival graph contains almost all of the remaining vertices in D k , n when n + 2 k − 2 vertices removed. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of reducing energy consumption within high-performance computing data centers, especially for those with a large portion of "small size" jobs. Different from previous works, the efficiency of job scheduling and processing is made as the first priority. To reduce energy from servers while maintaining the processing efficiency of jobs, a new hysteresis computing resource-provisioning algorithm is proposed to adjust the total computing resource reactively. A dynamical thermal model is presented to reflect the relationship between the computational system and cooling system. The proposed model is used to formulate constrained optimal control problems to minimize the energy consumption of the cooling system. Then, a two-step solution is proposed. Firstly, a thermal-aware resource allocation optimizer is developed to decide where the resource should be increased or decreased. Secondly, an economic model predictive controller is designed to adjust the cooling temperature predictively along with the variation of the rack power. Performance of the proposed method is studied through simulations with real job trace. The results show that significant energy saving can be achieved with guaranteed service quality. • On the basis of scheduling and processing jobs efficiently, a control framework is proposed for HPC data centers to coordinate hysteresis resource provisioning, thermal-aware allocation and dynamic cooling management. • An economic model predictive control based technique for the modeling and managing the thermal environment. • Comparison of proposed method is made with existing techniques. The proposed methodology achieves significant energy saving, and low performance loss in service quality. [ABSTRACT FROM AUTHOR]

Intensive workflows require a lot of computational resources and massive data movement between Storage and Computing servers hosting these datasets and tasks. Moving these datasets among these servers may increase the execution time, generate a high energy consumption by communication devices and a significant data movement cost. Thus, we need a good data placement strategy (DPS) to minimize the data movement between these servers, the communication energy consumption and the workflow execution time and cost. In this paper, we propose a data placement strategy based on the Formal Concept Analysis approach that considers the original datasets, the different communication levels (switches, routers) and the granularity of used resources in data center. It aims at grouping the maximum of datasets and tasks in a minimal number of Storage computing Servers (SC) as close as possible to each other. Simulations show that our strategy can greatly reduce the energy consumed by communication devices, the volume of data movement as well as the execution time and cost of the workflow. [ABSTRACT FROM AUTHOR]

Exponential growth of traffic and bandwidth demands in current data center networks requires low-latency high-throughput interconnection networks, considering power consumption. By considering growth of both multicast and unicast applications, power efficient communication becomes one of the main design challenges in today's data center networks. Addressing these demands, optical networks suggest several benefits as well as circumventing most disadvantages of electrical networks. In this paper, we propose an all-optical scalable architecture, named as OMUX, for communicating intra-data centers. This architecture utilizes passive optical devices and enables optical circuit switching without any switch configuration or path reservation. In addition, it is customized for both unicast and multicast transmissions. It can replace aggregation and core switches in the network, to reduce power consumptions. Simulation results verify its lower latency, higher throughput, and reduced power consumption, compared to conventional electrical or alternative optical solutions. [ABSTRACT FROM AUTHOR]

Highlights • Data-Driven Modeling (DDM) for near real-time prediction of temperatures within data centers for steady and transient scenarios. • Performance evaluation and comparison of ANN, SVR, GPR and POD based data-driven models (DDMs). • For all DDMs, the prediction accuracy decreases when the size of the training data set is less than 300 samples. • For the steady state scenario, the GPR model had smallest overall average prediction error of 0.6 °C. Abstract Energy-efficient thermal management of data centers based on dynamic optimization and provisioning of cooling resources requires rapid (nearly real-time) predictions of temperatures within data centers. This work for the first time compares multiple Data-Driven Models (DDMs) to achieve such rapid temperature predictions. DDM typically employs statistical or machine learning-based tools, in combination with physics-based modeling and/or experimental data to predict system behavior. In general, DDM approaches are well-suited to systems that have multiple operational states based on interactions between the many electrical, mechanical and control parameters typical of data centers. This study compares the performance of three different DDM methods, namely Artificial Neural Networks (ANN), Support Vector Regression (SVR), Gaussian Process Regression (GPR) in predicting both steady-state and transient rack inlet air temperature distributions in data centers. Additionally, Proper Orthogonal Decomposition (POD) was considered for transient modeling. The data used for training and analysis were obtained by performing 300 offline numerical simulations with a room-level, experimentally validated computational fluid dynamics/heat transfer (CFD/HT) model. The performance of the four data-driven models was evaluated based on the absolute mean error for interpolation and extrapolation, and the adaptability of the models to changes in physical domain (data center room) configuration. Additionally, the impact of the size of the training data set on prediction accuracy is also compared for the four models. For the steady-state case study, the predictions for ANN, SVR and GPR models are in good agreement with CFD/HT simulations, with the GPR model having the smallest overall average prediction error of 0.6 °C in rack inlet air temperature, corresponding to a relative error of 2.7% with respect to rack inlet temperature measured in °C. It was found that for all the frameworks the prediction error increases when the size of training data set was less than 300 samples. The GPR model had the best accuracy for smaller training data sets compared with the other models, with an average prediction error for rack inlet temperatures <1 °C when trained on only 50 simulations. For the transient case study, the interpolative prediction error for all the models is very low (<0.3 °C); however, the extrapolative prediction errors are much greater, and appear to be directly proportional to the (here, temporal) "distance" from the interrogation point to the input parameter space. [ABSTRACT FROM AUTHOR]

• We model the joint optimization of server and network element energy consumption during VM scheduling and routing as an integer programming problem. • Two metaheuristic algorithms based on ant colony optimization are proposed as a solution to the problem. • The proposed solution is tested for three standard data center network topologies namely 3-Tier, B-Cube, and Hyper-Tree of different sizes and its effectiveness is compared with two standard heuristic solutions, first-fit and round robin. Cloud data center hosting many composite applications of multiple tenants consumes a massive amount of energy. Developing energy efficient mechanisms for data center management has become a vital issue for the cloud providers. Most of the works that deal with mechanisms for energy-efficient resource provisioning focus either on reducing the energy consumption of servers or reducing the energy consumption of network elements, but not both. We have formulated the problem of jointly optimizing the energy consumption of servers and network elements by optimal VM scheduling and routing as an integer programming problem. To solve it a phase-wise optimization approach with two ant colony based meta-heuristic algorithms is proposed. The topology features of the data center network and the communication patterns of the applications are considered in the construction of the solution. The solution is tested for three standard data center networks, 3-tier, B-Cube and Hyper-tree of different sizes and compared against two standard algorithms, first-fit and round-robin algorithms. The results showed that the proposed solution improves energy savings by 15% and 20% on an average when compared with first-fit and round-robin respectively. [ABSTRACT FROM AUTHOR]

• Performance of data center network declines with the power management. • Controllers or SDN approaches can contribute to the better trade offs between power and performance. • There are limitations in simulators for advanced energy management approaches. • Currently the decision making approaches by the controllers are computationally expensive, needs to be lightweight in manners. In this paper, we present a coordinated power management technique for data center networks (DCN) which we have implemented in the popular network simulator NS3. The proposed mechanism includes three key entities: (a) a local controller (LC) that manages traffic at each DCN node (switches and routers), (b) a global controller (GC) that has a global view of the DCN and provides hints to the LC's based on this knowledge, and (c) a topology aware user request assignment controller (RAC) that controls placement of the external requests on the endpoint hosts based on hints from the GC. The main goal of the coordination from the energy management perspective is to properly direct and consolidate network traffic to maximize low power (or "sleep") opportunities for the network interfaces while avoiding link congestion. In the process of testing out our mechanisms, we have enhanced the popular NS3 package for network modeling. The key enhancements are in the accounting of energy consumption at various levels and enhanced traffic routing mechanisms. We show that these mechanisms can reduce the power consumption by up to ∼40% in the common fat-tree based DCNs using the low power idle (LPI) feature of the Ethernet. We also apply our mechanism to the Hypercube network and show that it too can save a significant amount of energy. [ABSTRACT FROM AUTHOR]

• We design a novel mechanism to solve "uncoordinated relationship" in colocations. • The truthfulness and feasibility of the designed mechanism can be guaranteed. • We develop a (1 + ϵ)-approximation algorithm for a NP-hard problem in the mechanism. • The developed algorithm can satisfy the truthfulness constraint of the mechanism. • We validate the designed mechanism though simulations based on real traces. Rapidly developing colocation data centers (or colocations, for short) have become important participants in emergency demand response (EDR) programs. Different from traditional data centers, in colocations, tenants control their own servers; thus, they may not coordinate to reduce their power consumption. In this paper, to encourage tenants to join EDR programs, we propose a market-oriented incentive mechanism, MicDR , which can effectively reduce energy costs. MicDR includes a local incentive mechanism (LiMec), a global incentive mechanism (GiMec) and a server-sharing incentive mechanism (SiMec). LiMec motivates tenants to improve their energy efficiency locally. GiMec encourages tenants to improve their energy efficiency by requesting public server resources. To support the requests sparked by GiMec , SiMec encourages tenants to share idle server resources. A (1 + ϵ)-approximation algorithm is proposed to achieve an asymptotic optimal energy-saving scheme. The performance of the proposed algorithm is evaluated, and trace-driven simulations verify the effectiveness and feasibility of MicDR. [ABSTRACT FROM AUTHOR]