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In this study, a validated mathematical model for a composite air conditioning system was established based on onsite test data. The system consists of a refrigerant pump driven two-phase cooling loop (RPTCL) and vapor compression refrigeration (VCR) cycles. The system can be switched to refrigerant pump (RP) mode, compressor mode, or hybrid mode, according to the outdoor temperature. By analyzing, the inadequacy of the existing Internet Data center (IDC) air conditioning system operation strategy was confirmed, and subsequently, five optimization strategies were proposed. The optimization strategies took two identical composite air conditioning systems as optimization unit, and under the premise of meeting the demand for cooling capacity, maximizing the operating time of RP and hybrid mode, which with higher energy efficiency, through complementary cooling capacity, to reduce the energy consumption. Meanwhile, when the cooling supply was higher than the demand, paired with optimization unit's ON/OFF ratio control, the unit's energy consumption was further reduced. The most optimization strategy provided 525.6 MWh of cooling energy to meet demand while consuming a minimum of 182.5 MWh. The optimization strategy also achieved a minimum of 12.1% and a maximum of 52.5% energy savings for IDCs in other climate zones of China. [ABSTRACT FROM AUTHOR]

The Hewlett Packard Enterprise–Cray EX Frontier is the world's first and fastest exascale supercomputer, hosted at the Oak Ridge Leadership Computing Facility in Tennessee, United States. Frontier is a significant electricity consumer, drawing 8–30 MW; this massive energy demand produces significant waste heat, requiring extensive cooling measures. Although harnessing this waste heat for campus heating is a sustainability goal at Oak Ridge National Laboratory (ORNL), the 30 °C–38 °C waste heat temperature poses compatibility issues with standard HVAC systems. Heat pump systems, prevalent in residential settings and some industries, can efficiently upgrade low-quality heat to usable energy for buildings. Thus, heat pump technology powered by renewable electricity offers an efficient, cost-effective solution for substantial waste heat recovery. However, a major challenge is the absence of benchmark data on high-performance computing (HPC) heat generation and waste heat profiles. This paper reports power demand and waste heat measurements from an ORNL HPC data centre, aiming to guide future research on optimizing waste heat recovery in large-scale data centres, especially those of HPC calibre. [ABSTRACT FROM AUTHOR]

We use Dark Energy Survey Year 3 (DES Y3) clusters with archival XMM–Newton and Chandra X-ray data to assess the centring performance of the redMaPPer cluster finder and to measure key richness observable scaling relations. We find that 10–20 per cent of redMaPPer clusters are miscentred, both when comparing to the X-ray peak position and to the visually identified central cluster galaxy. We find no significant difference in miscentring in bins of low versus high richness or redshift. The dominant reasons for miscentring include masked or missing data and the presence of other bright galaxies in the cluster. For half of the miscentred clusters, the correct central was one of the possible centrals identified by redMaPPer, while for ∼40 per cent of miscentred clusters, the correct central is not a redMaPPer member mostly due to masking. Additionally, we fit scaling relations of X-ray temperature and luminosity with richness. We find a T X–λ scatter of |$0.21\pm 0.01$|⁠. While the scatter in T X–λ is consistent in redshift bins, we find modestly different slopes, with high-redshift clusters displaying a somewhat shallower relation. Splitting based on richness, we find a marginally larger scatter for our lowest richness bin, 20 < λ < 40. We note that the robustness of the scaling relations at lower richnesses is limited by the unknown selection function, but at λ > 75, we detect nearly all of the clusters falling within existing X-ray pointings. The X-ray properties of detected, serendipitous clusters are generally consistent with those of targeted clusters. [ABSTRACT FROM AUTHOR]

High-performance computing (HPC) in data centers increases energy use and operational costs. Therefore, it is necessary to efficiently manage resources for the sustainability of and reduction in the carbon footprint. This research analyzes and optimizes ENEA HPC data centers, particularly the CRESCO6 cluster. The study starts by gathering and cleaning extensive datasets consisting of job schedules, environmental conditions, cooling systems, and sensors. Descriptive statistics accompanied with visualizations provide deep insight into collated data. Inferential statistics are then used to investigate relationships between various operational variables. Finally, machine learning models predict the average hot-aisle temperature based on cooling parameters, which can be used to determine optimal cooling settings. Furthermore, idle periods for computing nodes are analyzed to estimate wasted energy, as well as for evaluating the effect that idle node shutdown will have on the thermal characteristics of the data center under consideration. It closes with a discussion on how statistical and machine learning techniques can improve operations in a data center by focusing on important variables that determine consumption patterns. [ABSTRACT FROM AUTHOR]

Most applications use Transport Layer Security (TLS) as a default to encrypt network traffic to increase privacy and security. The adversary, however, also used LS encryption to conceal attacks or command-and-control communications. Encryption is essential for deciphering and assessing risks associated with communications. This issue should be resolved in a way that provides effective and covert decryption without compromising overall security. As a result of server consolidation, virtualization has emerged as a key technique for increasing data center energy efficiency. Consolidation of servers relies on the location of virtual machines. The placement of virtual machines has been approached from many different perspectives in recent years. Virtual machine placement approaches, however, only take into account the energy consumption of the physical machines within a data center, not the energy consumption of the communication network. Through the application domain, CVM offers a model-driven, user-centered approach to design, synthesis, and communication solutions by utilizing a fundamentally new method for handling these issues. This proposal discusses the concept, architecture, model languages, design prototypes, and implementation of CVM, arguing that it is a significantly more effective approach to technical communication solutions. [ABSTRACT FROM AUTHOR]

Under the background of "dual carbon" development goals, the rapid expansion of internet data centers driven by advancements in 5G technology has led to increased energy consumption and elevated heat densities within the server rooms in these facilities. In this study, the modular data center is taken as the research object for the purpose of figuring out a way to improve the thermal environment of the computer room, reduce power consumption, and ensure the safe and stable running of servers. To this end, this study established an airflow organization model for the modular data center and verified this model through experimental methods. Computational Fluid Dynamics (CFD) simulations were employed to investigate the effects of raised floor height, floor opening rate, and cold/hot air channel closure on airflow organization. Furthermore, the efficiency of airflow organization was evaluated using entransy loss metrics. The results show that optimal airflow conditions are achieved when the height of the raised floor is 600–800 mm, the opening rate is 40%, and the combined opening is 40%. Additionally, the closure of either the cold channel or both the cold and hot channels significantly improves airflow performance. Specifically, cold channel closure is recommended for new data centers with underfloor air supply systems, while combined cold and hot channel closure is suitable for data centers with high power density and extended air supply distances. [ABSTRACT FROM AUTHOR]

With the continuous expansion of network scale, fault tolerance and security in network operation have become increasingly important. The high vertex connectivity of the interconnection network indicates excellent fault tolerance. In order to address the drawbacks of traditional connectivity, the g-extra connectivity has been proposed to better reflect the fault tolerance of the network. RRect is a newly proposed server-centered data center network with good interconnection structure, and it has excellent flexibility and energy efficiency. In this paper, we focus on the logical structure of RRect, denoted by R R n , m , k . Firstly, we investigate the extra connectivity of R R n , m , k , and, in detail, we prove that for n ≥ 4 , m ≥ 1 , and k ≥ 4 , the { 1 , 2 , 3 } -extra connectivity of R R n , m , k is 2 k m (n - 1) + m n - 2 , 3 k m (n - 1) + m n - 3 , and 4 k m (n - 1) - 4 m , respectively, which are about 2, 3, and 4 times of its traditional connectivity. Secondly, we conduct numerical simulation experiments on the extra connectivity of R R n , m , k and evaluate the experimental performance. The result can provide a more comprehensive measurement of its reliability. [ABSTRACT FROM AUTHOR]

With the rapid growth of the digital economy, data centers have emerged as significant consumers of electricity. This presents challenges due to their high energy demand but also brings opportunities for utilizing waste heat. This paper introduces an operation optimization method for multi-energy systems with data centers, leveraging the information gap decision theory (IGDT) to consider various uncertainties from data requests and the environment. First, a model is established for the operation of a multi-energy system within data centers, considering the integration of server waste heat recovery technology. Second, IGDT is employed to address uncertainties of photovoltaic output and data load requests, thereby formulating an optimal energy management strategy for the data center park. Case studies demonstrate that the electricity purchase cost increased by 5.3%, but the total cost decreased by 30.4%, amounting to 5.17 thousand USD after optimization. It indicates that the operational strategy effectively ensures both efficient and cost-effective power supply for the data center and the park. Moreover, it successfully mitigates the risks associated with fluctuations in data load, thus minimizing the possibility of data load abandonment during uncertain periods. [ABSTRACT FROM AUTHOR]

With the growth of the cloud computing market, the number and scale of cloud data centers are expanding rapidly. While cloud data centers provide a large amount of computing power, generating tremendous energy consumption has become a fundamental issue in the financial and environmental fields. Improving quality of service and reducing energy costs are fundamental challenges for next-generation cloud data centers. Task scheduling in cloud data centers grows increasingly complex due to the heterogeneity of computing resources, intricate dependencies of jobs and rising expenses resulting from high energy consumption. Efficiently utilizing computing resources is crucial, so it is necessary to develop optimal strategies for job scheduling. This paper proposes a reinforcement learning-based task scheduler (E2DSched) for online scheduling of randomly arriving directed acyclic graph jobs in cloud data centers. E2DSched divides the scheduling process into three layers: task selection layer, server selection layer and frequency control layer. It achieves joint optimization of energy consumption and quality of service through three-layer cooperation. Finally, we compare E2DSched with various other algorithms, and the results show that E2DSched can provide excellent service with less energy consumption. [ABSTRACT FROM AUTHOR]

Cloud service providers deliver computing services on demand using the Infrastructure as a Service (IaaS) model. In a cloud data center, several virtual machines (VMs) can be hosted on a single physical machine (PM) with the help of virtualization. The virtual machine placement (VMP) involves assigning VMs across various physical machines, which is a crucial process impacting energy draw and resource usage in the cloud data center. Nonetheless, finding an effective settlement is challenging owing to factors like hardware heterogeneity and the scalability of cloud data centers. This paper proposes an efficient algorithm named VMP-ER aimed at optimizing power consumption and reducing resource wastage. Our algorithm achieves this by decreasing the number of running physical machines, and it gives priority to energy-efficient servers. Additionally, it improves resource utilization across physical machines, thus minimizing wastage and ensuring balanced resource allocation. [ABSTRACT FROM AUTHOR]

To face with the surge of cloud computing services, energy problem and carbon mission constraint, more and more geo-distributed hybrid powered data centers are build over the world. Geo-distributed hybrid powered data centers are located in different locations equipped with different types of green energy source. In this work, we propose a periodic requests dispatcher, including a periodic model and a spatial balance algorithm, to minimize the brown energy use. The periodic model divides the long-term period into several time slots, and the spatial balance algorithm achieves energy optimization by balancing user requests in each time slot. Besides, a research case is simulated by Cloudsim platform. We conduct several comparative analysis in aspect of energy utilization, electricity cost and carbon emission. The experiment results show that the dispatcher can effectively reduce the electricity cost, and reasonably achieve the request distribution balance in geo-distributed hybrid powered data centers. [ABSTRACT FROM AUTHOR]

The energy consumption of Cloud–Edge systems is becoming a critical concern economically, environmentally, and societally; some studies suggest data centers and networks will collectively consume 18% of global electrical power by 2030. New methods are needed to mitigate this consumption, e.g. energy-aware workload scheduling, improved usage of renewable energy sources, etc. These schemes need to understand the interaction between energy considerations and Cloud–Edge components. Model-based approaches are an effective way to do this; however, current theoretical Cloud–Edge models are limited, and few consider energy factors. This paper analyses all relevant models proposed between 2016 and 2023, discovers key omissions, and identifies the major energy considerations that need to be addressed for Green Cloud–Edge systems (including interaction with energy providers). We investigate how these can be integrated into existing and aggregated models, and conclude with the high-level architecture of our proposed solution to integrate energy and Cloud–Edge models together. [ABSTRACT FROM AUTHOR]

In the modern era, large-scale computing systems distribute tasks into smaller units, allowing them to be executed simultaneously, accelerating job completion, and reducing energy usage. However, cloud computing systems face a significant challenge: the Long Tail problem. This problem arises when a small subset of slow-performing tasks impedes the overall progress of parallel job execution, resulting in longer service response times and decreased system efficiency. To reduce task execution time and energy consumption, we propose an efficient straggler task management framework for cloud data centers in this paper. A neural network-based resource predictor is initially developed and tuned with the Stochastic Gradient Descent with Momentum mechanism to analyze and classify heterogeneous tasks into stragglers and non-stragglers. Then, after identifying the straggler tasks, they are further classified into two categories: Resource Hunters and Long-Tail stragglers, based on their specific resource requirements. A task management policy is implemented to achieve parallelism and enhance sustainability in the cloud infrastructure. Considering the task category, this policy effectively schedules and allocates resources among user job requests. To evaluate the effectiveness of the proposed work, extensive simulations are performed using the Google Cluster Dataset (GCD). The results obtained from these simulations are subsequently compared to state-of-the-art techniques for a comprehensive analysis. The experimental results reveal substantial improvements in various metrics, including power consumption and active servers showing reductions of up to 55.16% and 35%, respectively. Furthermore, there has been a reduction in execution time of up to 67.74%. [ABSTRACT FROM AUTHOR]

In the past twenty years, the IT industry has moved away from using physical servers for workload management to workloads consolidated via virtualization and, in the next iteration, further consolidated into containers. Later, container workloads based on Docker and Podman were orchestrated via Kubernetes or OpenShift. On the other hand, high-performance computing (HPC) environments have been lagging in this process, as much work is still needed to figure out how to apply containerization platforms for HPC. Containers have many advantages, as they tend to have less overhead while providing flexibility, modularity, and maintenance benefits. This makes them well-suited for tasks requiring a lot of computing power that are latency- or bandwidth-sensitive. But they are complex to manage, and many daily operations are based on command-line procedures that take years to master. This paper proposes a different architecture based on seamless hardware integration and a user-friendly UI (User Interface). It also offers dynamic workload placement based on real-time performance analysis and prediction and Machine Learning-based scheduling. This solves a prevalent issue in Kubernetes: the suboptimal placement of workloads without needing individual workload schedulers, as they are challenging to write and require much time to debug and test properly. It also enables us to focus on one of the key HPC issues—energy efficiency. Furthermore, the application we developed that implements this architecture helps with the Kubernetes installation process, which is fully automated, no matter which hardware platform we use—x86, ARM, and soon, RISC-V. The results we achieved using this architecture and application are very promising in two areas—the speed of workload scheduling and workload placement on a correct node. This also enables us to focus on one of the key HPC issues—energy efficiency. [ABSTRACT FROM AUTHOR]

Cloud data centers play a vital role in modern computing infrastructure, offering scalable resources for diverse applications. However, managing costs and resources efficiently in these centers has become a crucial concern due to the exponential growth of cloud computing. User applications exhibit complex behavior, leading to fluctuations in system performance and increased power usage. To tackle these obstacles, we introduce the Modified Feeding Birds Algorithm (ModAFBA) as an innovative solution for virtual machine (VM) consolidation in cloud environments. The primary objective is to enhance resource management and operational efficiency in cloud data centers. ModAFBA incorporates adaptive position update rules and strategies specifically designed to minimize VM migrations, addressing the unique challenges of VM consolidation. The experimental findings demonstrated substantial improvements in key performance metrics. Specifically, the ModAFBA method exhibited significant enhancements in energy usage, SLA compliance, and the number of VM migrations compared to benchmark algorithms such as TOPSIS, SVMP, and PVMP methods. Notably, the ModAFBA method achieved reductions in energy usage of 49.16%, 55.76%, and 65.13% compared to the TOPSIS, SVMP, and PVMP methods, respectively. Moreover, the ModAFBA method resulted in decreases of around 83.80%, 22.65%, and 89.82% in the quantity of VM migrations in contrast to the aforementioned benchmark techniques. The results demonstrate that ModAFBA outperforms these benchmarks by significantly reducing energy consumption, operational costs, and SLA violations. These findings highlight the effectiveness of ModAFBA in optimizing VM placement and consolidation, offering a robust and scalable approach to improving the performance and sustainability of cloud data centers. [ABSTRACT FROM AUTHOR]

Cloud data centers present a challenge to environmental sustainability because of their significant energy consumption. Additionally, performance degradation resulting from energy management solutions, such as virtual machine (VM) consolidation, impacts service level agreements (SLAs) between cloud service providers and users. Thus, to achieve a balance between efficient energy consumption and avoiding SLA violations, we propose a novel VM consolidation algorithm. Conventional algorithms result in unnecessary migrations when improperly selecting VMs. Therefore, our proposed E2SVM algorithm addresses this issue by selecting VMs with high load fluctuations and minimal resource usage from overloaded servers. These selected VMs are then placed on normally loaded servers, considering their stability index. Moreover, our approach prevents server underutilization by either applying all or no VM migrations. Simulation results demonstrate a 12.9% decrease in maximum energy consumption compared with the minimum migration time VM selection policy. In addition, a 47% reduction in SLA violations was observed when using the medium absolute deviation as the overload detection policy. Therefore, this approach holds promise for real-world data centers because it minimizes energy waste and maintains low SLA violations. [ABSTRACT FROM AUTHOR]

While cloud computing is crucial in processing data from devices with low computational power, the latency introduced by the Internet backhaul limits real-time applications. By situating computing resources at the network's edge, edge computing offers low-latency services by offloading computations from high-performance computing (HPC) data centers to the edge servers, reducing wide Area network (WAN) strain. As a result, edge computing has unlocked opportunities for innovative applications that were previously unfeasible, such as connected vehicles or medical robotics. Nonetheless, deploying the infrastructure required to support edge computing services raises sustainability and energy consumption concerns. Consequently, the development of tools enabling researchers to explore innovative approaches to reducing the energy impact of edge computing is crucial. In this work, we present MintEDGE, a network simulator focused on the energy consumption of edge computing. Our simulator allows testing energy-saving approaches and task placement algorithms in realistic large-scale scenarios encompassing entire regions. [ABSTRACT FROM AUTHOR]

Cloud data centers face various challenges, such as high energy consumption, environmental impact, and quality of service (QoS) requirements. Dynamic virtual machine (VM) consolidation is an effective approach to address these challenges, but it is a complex optimization problem that involves trade-offs between energy efficiency and QoS satisfaction. Moreover, the workload patterns in cloud data centers are often non-stationary and unpredictable, which makes it difficult to model them. In this paper, we propose a new method for dynamic VM consolidation that optimizes both energy efficiency and QoS objectives. Our approach is based on Markov chains and the artificial feeding birds (AFB) algorithm. Markov chains are used to model the resource utilization of each individual VM and PM based on the changes that happen in workload data. AFB algorithm is a metaheuristic optimization technique that mimics the behavior of birds in nature. We modify the AFB algorithm to suit the characteristics of the VM placement problem and to provide QoS-aware and energy-efficient solutions. Our approach also employs an online step detection method to capture variations in workload patterns. Furthermore, we introduce a new policy for VM selection from overloaded hosts, which considers the abrupt changes in the utilization processes of the VMs. The proposed algorithms are evaluated extensively using the CloudSim Toolkit with real workload data. The proposed system outperforms evaluation policies in multiple metrics, including energy consumption, SLA violations, and other essential metrics. [ABSTRACT FROM AUTHOR]

With the development of port automation and artificial intelligence, coordination with multi-geographic data centers (Geo-DCs) has become a viable solution to address the issue of limited port computing resources. This study proposes a distributed energy dispatch method for the port microgrid coordinated with Geo-DCs (Geo-DCPM), aimed at reducing port carbon emissions and operational costs. Consider the single point of failure problem and high construction costs of centralized data centers. Geo-DCs are first introduced to solve the problem of insufficient computing resources in ports. An energy consumption calculation model for Geo-DCs is established, considering the data load delay constraint and the data space transfer constraint caused by specific delay-sensitive loads in the port microgrid. Then, an energy dispatch model (EDM) is constructed for the Geo-DCPM, taking into account carbon capture costs. Moreover, based on mixed-integer linear programming, a distributed algorithm is proposed to solve the EDM problem. Finally, the simulation results verify the effectiveness of the proposed method. Compared with the centralized algorithm, the packet loss rate of the distributed algorithm combined with Geo-DCs is significantly lower, reduced by about 70%. [ABSTRACT FROM AUTHOR]

Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure, thereby revolutionizing computer processes. However, the rising energy consumption in cloud centers poses a significant challenge, especially with the escalating energy costs. This paper tackles this issue by introducing efficient solutions for data placement and node management, with a clear emphasis on the crucial role of the Internet of Things (IoT) throughout the research process. The IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around data centers. These sensors continuously monitor vital parameters such as energy usage and temperature, thereby providing a comprehensive dataset for analysis. The data generated by the IoT is seamlessly integrated into the Hybrid TCN-GRU-NBeat (NGT) model, enabling a dynamic and accurate representation of the current state of the data center environment. Through the incorporation of the Seagull Optimization Algorithm (SOA), the NGT model optimizes storage migration strategies based on the latest information provided by IoT sensors. The model is trained using 80% of the available dataset and subsequently tested on the remaining 20%. The results demonstrate the effectiveness of the proposed approach, with a Mean Squared Error (MSE) of 5.33% and a Mean Absolute Error (MAE) of 2.83%, accurately estimating power prices and leading to an average reduction of 23.88% in power costs. Furthermore, the integration of IoT data significantly enhances the accuracy of the NGT model, outperforming benchmark algorithms such as DenseNet, Support Vector Machine (SVM), Decision Trees, and AlexNet. The NGT model achieves an impressive accuracy rate of 97.9%, surpassing the rates of 87%, 83%, 80%, and 79%, respectively, for the benchmark algorithms. These findings underscore the effectiveness of the proposed method in optimizing energy efficiency and enhancing the predictive capabilities of cloud computing systems. The IoT plays a critical role in driving these advancements by providing real-time data insights into the operational aspects of data centers. [ABSTRACT FROM AUTHOR]

Cloud computing is a dynamic and rapidly evolving field, where the demand for resources fluctuates continuously. This paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing environments. The motivation stems from the pressing issue of accommodating fluctuating levels of user demand efficiently. By adhering to the proposed resource allocation method, we aim to achieve a substantial reduction in energy consumption. This reduction hinges on the precise and efficient allocation of resources to the tasks that require those most, aligning with the broader goal of sustainable and eco-friendly cloud computing systems. To enhance the resource allocation process, we introduce a novel knowledge-based optimization algorithm. In this study, we rigorously evaluate its efficacy by comparing it to existing algorithms, including the Flower Pollination Algorithm (FPA), Spark Lion Whale Optimization (SLWO), and Firefly Algorithm. Our findings reveal that our proposed algorithm, Knowledge Based Flower Pollination Algorithm (KBFPA), consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption reduction. This paper underscores the profound significance of resource allocation in the realm of cloud computing. By addressing the critical issue of adaptability and energy efficiency, it lays the groundwork for a more sustainable future in cloud computing systems. Our contribution to the field lies in the introduction of a new resource allocation strategy, offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures. [ABSTRACT FROM AUTHOR]

Scheduling workload is a demand response strategy for data centers to reshape electricity usage, which provides an opportunity for them to utilize renewable energy. Enhancing the flexibility of workload scheduling would promote the data center to integrate renewable energy. Considering that the improvement of flexibility in workload scheduling is tightly related to the Quality of Service (QoS) required by IT consumers (ITCs), it becomes desirable for the data center to collaborate with them to further facilitate renewable energy integration in demand response programs. This paper proposes a QoS‐based bi‐level demand response model for the data center, where game theory is adopted to resolve the conflict of interest in the collaboration between the data center and ITCs. At the upper level, the data center is a leader who provides differential monetary incentives to ITCs while dispatching workload to integrate renewable energy. At the lower level, ITCs act as followers who adjust the workload's QoS to enhance the flexibility of workload scheduling. Simulation results show that the proposed model can decrease the operating cost of the data center while increasing the utilization of renewable energy. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The proliferation of data centers is driving increased energy consumption, leading to environmentally unacceptable carbon emissions. As the use of Internet-of-Things (IoT) techniques for extensive data collection in data centers continues to grow, deep learning-based solutions have emerged as attractive alternatives to suboptimal traditional methods. However, existing approaches suffer from unsatisfactory performance, unrealistic assumptions, and an inability to address practical data center optimization. In this paper, we focus on power usage effectiveness (PUE) optimization in IoT-enabled data centers using deep learning algorithms. We first develop a deep learning-based PUE optimization framework tailored to IoT-enabled data centers. We then formulate the general PUE optimization problem, simplifying and specifying it for the minimization of long-term energy consumption in chiller cooling systems. Additionally, we introduce a transformer-based prediction network designed for energy consumption forecasting. Subsequently, we transform this formulation into a Markov decision process (MDP) and present the branching double dueling deep Q-network. This approach effectively tackles the challenges posed by enormous action spaces within MDP by branching actions into sub-actions. Extensive experiments conducted on real-world datasets demonstrate the exceptional performance of our algorithms, excelling in prediction precision, optimization convergence, and optimality while effectively managing a substantial number of actions on the order of 10 13 . [ABSTRACT FROM AUTHOR]

Objective: Energy usage in has been increased due to the rising demand of cloud infrastructure. The government policy has been focused on building the green IT data center. The energy data need to be collected in order to monitor the energy usage. However, in an old typical data center, the building has been built with no support of such data collection. In this research, we aim to design the energy data collection system for our existing data center, a case study of data center in Thailand at the university. Based on the collected data, an energy usage monitoring system and prediction can be developed. Methods: In the case study of Kasetsart University data center, the building and electric layouts were predetermined. The building layout and existing IT hardware were investigated. We designed the meter types and the number of meters to be installed for the building the energy data collection system. The corresponding database system was also designed for data logging, data visualization and analysis purpose. Results: As a result, 25 installed meters along with the add-on network system were installed for logging data. A data usage example was demonstrated by building the data visualization and analysis. The presented 1 year dataset collected showed the changes of energy usages which can be used to compare with real activities happening in the campus. This encourages the integration of other related environment data such as outside temperature which may affect the electric billing cost. The dataset can be used for prediction of the electric usage; thus, the policy for reducing the electric billing cost could be established. In this paper, as a data note, we focus on the methodology of data collection required for data center. [ABSTRACT FROM AUTHOR]

• To improve data center performance, it must be efficiently cooled. • This paper undertakes a detailed examination of the most recent cooling optimization techniques. • Several methods are discussed like regular air conditioning, free cooling, and liquid cooling. • An assessment of overhead and underfloor air delivery technologies is presented. • The effects of baffles, variable airflow control, and server placement are investigated. In order to increase data centers' efficiency and performance, a proper cooling system should be applied. This article provides a comprehensive assessment which explores current cooling optimization technologies for data centers. Standard traditional technologies like air conditioning, free cooling, and liquid cooling are investigated. Their limitations as well as proposed improvements are thoroughly discussed. The article also delves into underfloor and overhead air supply technologies, presenting their advantages and drawbacks. The review introduces novel approaches at the rack level cooling, including baffles, variable airflow management, and optimal server placement. Key findings stress the efficacy of optimized airflow systems and innovative rack-level cooling, underlining their role in reducing energy consumption and enhancing overall performance. Notably, potential energy savings of up to 67.2 % compared to traditional methods are demonstrated. The adoption of advanced cooling technologies, such as direct and indirect natural cooling, liquid-cooling cold plates, submersion, heat pipe, and thermosiphon-based cooling, exhibits promising energy efficiency gains and reduced Power Usage Effectiveness (PUE) values. Research into content-specific efficiency enhancements, adaptable energy-saving tactics, and all-encompassing server power consumption models is necessary. To work toward future sustainable, energy-efficient data centers, researchers should investigate how CRACs and server fans interact, find ways to integrate thermal and power management, and how artificial intelligence can be used to cool servers in an eco-friendly way. [ABSTRACT FROM AUTHOR]

• Advantage of hot water cooling was proven by entropy and entranspy theory. • Thermodynamic model of a waste heat recovery-free cooling data center was established. • Heat transfer efficiency of waste heat recovery-free cooling data centers was proven by entranspy theory. • Performance indicators of waste heat recovery-free cooling data center was predicted. This study investigated the potential of integrating separate heat pipe and adsorption refrigeration systems as a data center efficient cooling system. Compared to water-cooled data centers, this approach demonstrates improved heat transfer efficiency and reduced energy consumption. The effectiveness of heat transfer in this approach is illustrated by applying exergy and entranspy theories. By conducting numerical simulations across five representative Chinese cities with different climates, both Power Usage Effectiveness (PUE) and key cooling system performance indicators are assessed. The analysis reveals that all cities can achieve a PUE below 1.095, realized a 65.87 % and 13.60 % reduction in cooling system energy consumption and entransy dissipation, respectively. Data centers using mechanical chiller cooling cause a high energy consumption. This paper indicates the benefits of integrating heat pipe and adsorption refrigeration systems in reducing energy consumption and optimizing overall data center performance. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we study the task offloading optimization problem in satellite edge computing environments to reduce the whole communication latency and energy consumption so as to enhance the offloading success rate. A three‐tier machine learning framework consisting of collaborative edge devices, edge data centers, and cloud data centers has been proposed to ensure an efficient task execution. To accomplish this goal, we also propose a Q‐learning‐based reinforcement learning offloading strategy in which both the time‐sensitive constraints and data requirements of the computation‐intensive tasks are taken into account. It enables various types of tasks to select the most suitable satellite nodes for the computing deployment. Simulation results show that our algorithm outperforms other baseline algorithms in terms of latency, energy consumption, and successful execution efficiency. [ABSTRACT FROM AUTHOR]

A workflow is sent from the internet of things (IoT) in the real world to a distributed edge micro data centre (EMDC), which is assigned to various resources for the necessary computation process. Since optimising energy consumption and other computing processes are crucial, a significant issue arises involving energy consumption and task execution time in an EMDC-integrated computing system. This study presented a task scheduling and allocation method with two phases in optimising the energy consumption and execution time of scientific workflow processing. The first phase assigned the workflow as a Directed Acyclic Graph (DAG) to an EMDC with the least latency. A selection algorithm then considered location routing problems (LRP), EMDC queue waiting time to initiate scheduling a DAG task and the EMDC capacity. The second phase demonstrated the allocation and schedule of DAG tasks to the resources of selected EMDCs. Subsequently, a critical path (CP) was proposed using the Multi-Objective Firefly Optimisation with Dynamic Voltage and Frequency Scaling (CP-MOFFO-DVFS) algorithm. This study demonstrated a 20 to 37% improvement in energy savings than the state-of-the-art method, thus substantially reducing execution time. [ABSTRACT FROM AUTHOR]

In the era of big data, data centers with high energy consumption, interconnectivity, and load flexibility have developed rapidly. However, due to data privacy issues, the traditional power-system operational reliability assessment (ORA) struggles to precisely consider the load flexibility of data centers, leading to inaccurate evaluation. To this end, this article proposes an ORA method considering the load flexibility of data centers via the energy consumption elastic space. By transforming the inner operation constraints of data centers into an equivalent elastic space, the ORA does not require any private data to complete the evaluation. Specifically, the energy consumption model of data centers is established to accurately describe the load flexibility. Then, based on multi-parametric programming techniques, the energy consumption elastic space of data centers is characterized by data centers' power demand constraints, which do not involve privacy data, and no privacy concerns exist. Finally, the ORA model and the evaluation method based on the energy consumption elastic space can be constructed. With a lot of data center operation constraints being replaced by power demand constraints, the proposed method can complete an evaluation faster without accuracy loss. Its effectiveness is validated through simulations using the IEEE RTS 24-bus system and a provincial 661-bus system. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of cloud computing, efficient resource allocation and workload prediction are paramount for optimizing infrastructure utilization, cost management, and overall service quality. As a result, an efficient resource management approach involves pooling available resources among many customers in a manner that considers their energy usage, SLAs, and forecasting accuracy all at once. Based on the performance analysis results, it is determined that integrating these techniques would result in the most efficient and adaptable cloud data centre. We proposed a Model based on Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) for handling SLAs in the cloud from both the perspective of consumers and service providers. Subsequently, we delve into the methodology of applying deep CNN and LSTM models to the problem of workload prediction in cloud environments. This methodology encompasses data preprocessing, model architecture, training parameters, and the choice of performance metrics. To anticipate CPU consumption from time series data and detect SLA violations, we suggested a DCNN-LSTM model. The accuracy prediction, energy usage, CPU use, and Service Level Agreement monitoring are all a part of this model. The proposed method is effective in helping cloud providers cut down on service violations and associated fines. Regarding the composite metric of Energy SLA Violation, which assesses the combined aspects of energy use and adherence to Service Level Agreements (SLAs), DCNN-LSTM surpasses ARIMA-LSTM, CNN, LSTM, and ARIMA 6.8%, 10.88%, 16.6%, and 22.4%, respectively. [ABSTRACT FROM AUTHOR]

Cloud computing technology provides access on demand to virtualized resources, services, and applications via a distributed network. In cloud data centers effective energy utilization is a critical concern in today's technology-driven world. Cloud data centers (CDC) are massive facilities that host and manage an enormous amount of data and computing resources. This article addresses the growing significance and energy-intensive nature of data centers. Due to the rapid growth of cloud computing, it offers on-demand access to resources globally and leads to substantial power consumption and carbon impact on the environment. They consume substantial amounts of energy, and optimizing their energy utilization is essential for reducing operational costs, minimizing environmental impact, and ensuring sustainable growth. To combat this, efficient energy-saving approaches using machine learning methods have been researched. ML methods hold great potential for enhancing energy efficiency in CDCs by analysing data, detecting patterns, and optimizing resource usage. The focus areas include CPU usage prediction, overload finding, underload estimation, selection, migration, and relocation of VMs to attain resources and improve energy utilization. The paper compares energy-saving results achieved by different machine learning techniques in data centers to minimize energy usage and meet service level agreements (SLA). The machine learning approaches reduce energy consumption from 1.6% to 88.5% compared to the benchmark approach mentioned, considering various settings and parameters. [ABSTRACT FROM AUTHOR]

Recently, low-cost, low-power sensors have made wireless communication possible for Wireless Sensor Networks (WSNs), which are growing in popularity and bringing about tremendous improvement. Nevertheless, their autonomy eventually wanes because WSN nodes run entirely on batteries. The network lifetime is affected by this energy constraint. Therefore, the proposed method uses Quantum-based Optical sensors with the CUCKOO-ANN method for Enhanced Energy-Efficient Wireless Communication to overcome the existing issues. Initially, the Quantum-based optical sensors helped collect the data from the location and were also used for energy-efficient communication. It transfers the data very quickly and securely without any interruption. Next, the CUCKOO-ANN method optimises the real-time data and provides efficiency. Therefore, it helps to find an optimal route between the user and the Base station. The concept that has been presented exhibits excellent potential as it can efficiently fulfil customer service level agreements, lower average execution times, and improve data centre energy efficiency. Experimental results demonstrate that the proposed energy-efficient method minimises energy consumption, optimises data transmission, improves the network lifetime, and enhances the scalability of the network. [ABSTRACT FROM AUTHOR]

As the internet data centers are mushrooming, the energy consumption and carbon emission of data centers are increasing rapidly. To cut down the electricity cost and carbon emission of the data centers, we proposed an optimization method to reduce the electricity cost and carbon emission in geo-distributed multiple data centers. In the proposed method, the carbon tax is considered in the overall operation cost to optimize the carbon emission. The spatial and temporal flexibility of computational workload is fully utilized by considering the difference in renewable energy power output, local electricity and carbon emission of multiple geo-distributed data centers to achieve a better performance. Furthermore, the nonlinear characteristics of the power loss of uninterruptible power supply (UPS) are considered in the optimization. To verify the proposed optimization method, simulation of six cases is carried out with realistic data, and results have proved the proposed method can reduce the operational costs by 4.93%-12.7% and decrease carbon emissions by up to 10%. [ABSTRACT FROM AUTHOR]

Fault detection and localization are vital for ensuring the stability of data center networks (DCNs). Specifically, adaptive fault diagnosis is deemed a fundamental technology in achieving the fault tolerance of systems. The highly scalable data center network (HSDC) is a promising structure of server-centric DCNs, as it exhibits the capacity for incremental scalability, coupled with the assurance of low cost and energy consumption, low diameter, and high bisection width. In this paper, we first determine that both the connectivity and diagnosability of the m-dimensional complete HSDC, denoted by H S D C m (m) , are m. Further, we propose an efficient adaptive fault diagnosis algorithm to diagnose an H S D C m (m) within three test rounds, and at most N + 4 m (m − 2) tests with m ≥ 3 (resp. at most nine tests with m = 2 ), where N = m · 2 m is the total number of nodes in H S D C m (m) . Our experimental outcomes demonstrate that this diagnosis scheme of HSDC can achieve complete diagnosis and significantly reduce the number of required tests. [ABSTRACT FROM AUTHOR]

Designing a cost-effective, energy-efficient and highly scalable network for data centers that can deliver sufficient bandwidth has drawn tremendous attentions recently. The data center networks constructed by using multi-port servers can provide sufficient bandwidth, such as BCube, DCell and GBC3. As the volume of data keeps growing rapidly, more and more servers are continuously added into data centers. In order to reduce the cost and energy consumption, data center networks can be expanded gradually by adding a small number of servers from time to time instead of adding a huge number of servers at a time. This paper proposes a new type of data center network structure called GHB, which is constructed by using commercial switches and multi-port servers. Two types of incomplete GHB structures are also proposed. A small number of servers can be gradually added into the incomplete structures without changing their topological properties. As shown in the experimental results, the throughput of GHB is comparable to that of BCube, and is larger than that of GBC3 and DCell. The analysis results indicate that GHB strikes a good balance among diameter, bisection width, incremental scalability, cost, and energy consumption in contrast to BCube, DCell and GBC3. Compared with the BCube and GBC3, GHB reduces the cost and energy consumption by about 6% and 18%, respectively. The highest throughput of GHB is higher than that of DCell and GBC3 by about 3.4% and 8.35%, respectively. [ABSTRACT FROM AUTHOR]

Traditional scheduling techniques are designed to reduce processing times while disregarding energy costs. One way of lowering energy usage is to implement scheduling strategies that distribute tasks to specified resources, which influence the processing time and power usage. Among the primary objectives to be achieved in cloud computing, power and energy consumption of the cloud environment have become issues due to ecological and economic reasons. Despite the existence of research efforts from the past pertaining to the same topic, an ideal solution to this problem has not yet been found. One of the main drawbacks of utilizing cloud computing is that the cloud data centers hosting cloud computing applications use higher volumes of energy, adding to the increased functioning cost and carbon footprint in the environment, which in turn increases the need for energy-efficient systems. In this research work, a hybrid optimization algorithm is presented, which is intended to minimize energy in the cloud computing environment. Thus, the crow search algorithm (CSA) and the sparrow search algorithm (SSA) are combined to obtain the proposed hybrid model. The hybrid approach achieves the optimal position in the shortest period of time and with the least amount of energy, load, and makespan, hence improving system performance. The experiments were conducted using three setups with different task sizes. The analysis while using the task size = 300 shows that the proposed method improved QoS, Resource Utilization (RU) and decreased makespan, energy usage, and load at a rate of 0.5073, 4.4035, 0.0331, and 0.0014, respectively. [ABSTRACT FROM AUTHOR]

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Data centers are characterized by high energy consumption, with operating costs being extremely sensitive to electricity prices. Therefore, modern data centers are often equipped with microgrids for power supply, which adjust their operational strategies based on the given electricity prices to minimize costs. However, existing research has overlooked the flexible pricing potential of electricity retailers, prompting this study to propose a non-cooperative game theory- based optimization method for data center electricity procurement negotiation. A two-layer optimization model is established for data center electricity pricing. The upper layer focuses on electricity price optimization, modeling price negotiation as a Stackelberg game and adopting a weighted average cost approach to describe the electricity procurement prices. The lower layer addresses data center operational optimization, formulated as a nonlinear programming problem. To enable rapid solution convergence, a hybrid problemsolving method combining a genetic algorithm and branch-and-cut algorithm is proposed. Finally, a simulation is conducted using a data center located in California, United States, to validate the proposed method. The results demonstrate that the proposed method can reduce data center operational costs by 3% and increase the revenue for electricity retailers by 17%, achieving a win-win outcome. [ABSTRACT FROM AUTHOR]

Decreasing carbon emissions of data centers while guaranteeing Quality of Service (QoS) is one of the major challenges for efficient resource management of large-scale cloud infrastructures and societal sustainability. Previous works in the area of carbon reduction mostly focus on decreasing overall energy consumption, replacing energy sources with renewable ones, and migrating workloads to locations where lower emissions are expected. These measures do not consider the energy mix of the power used for the data center. In other words, all KWh of energy are considered the same from the point of view of emissions, which is rarely the case in practice. In this paper, we overcome this deficit by proposing a novel practical CO2-aware workload scheduling algorithm implemented in the Kubernetes orchestrator to shift non-critical jobs in time. The proposed algorithm predicts future CO2 emissions by using historical data of energy generation, selects time-shiftable jobs, and creates job schedules utilizing greedy sub-optimal CO2 decisions. The proposed algorithm is implemented using Kubernetes' scheduler extender solution due to its ease of deployment with little overheads. The algorithm is evaluated with real-world workload traces and compared to the default Kubernetes scheduling implementation on several actual scenarios. [ABSTRACT FROM AUTHOR]

This study addresses the energy efficiency challenge in cloud data centers by examining the Virtual Machine Placement (VMP) problem. VMP involves mapping virtual machines (VMs) to physical machines (PMs) under capacity constraints. The paper focuses on the bin packing with linear usage cost (BPLUC) variant of bin packing, which includes fixed and variable costs in the calculation of the cost of a used bin. We prove that every approximation algorithm for the bin and vector bin packing can be used for BPLUC and VBPLUC, respectively. We propose a more power-efficient approach to VMP by applying a vector bin packing algorithm to minimize power consumption in data centers. We test the proposed algorithm on various synthetic and real workloads, and the experimental results demonstrate that it is more power-efficient than existing algorithms for VMP. The findings suggest that the proposed algorithm has significant implications for energy-efficient strategies in cloud data centers. Generally, this study makes contributes to the development of energy-efficient approaches to VMP that can help reduce power consumption and improve the sustainability of cloud data centers. [ABSTRACT FROM AUTHOR]

The cloud-computing paradigm based on containers has progressively grown in recent years as a flexible strategy that has proven to be energy efficient. The increasing usage of the container as a service technology in data centers (DCs) among cloud providers highlights the necessity of the container installation design phase in cloud environments. Cloud providers attempt to enhance resource utilization and reduce energy consumption by employing various VM selection and placement policies. This procedure for placement acquires a new aspect, with containers now being deployed on virtual machines (VMs) and those guest VMs being installed on physical machines (PMs). The intricacy of this issue increases when the variety of the containers, VMs, and PMs is taken into account. In this paper, an optimal placement strategy for containers is proposed based on the bio-inspired algorithms. The firefly algorithm has been modified to use discretization strategy (Discrete Firefly Algorithm, DFF) and has also used local search mechanism (Discrete Firefly with Local Search Mechanism, DFFLSM). The proposed versions of firefly algorithm are compared with first fit, first fit decreasing, random algorithm and ant colony algorithm. The comparison is done based on average energy consumption, average active VM, average active PM and average overall service-level agreement violations in the DC. The results show that DFFLSM performs better than all pre-existing container placement algorithms in terms of energy efficiency. It reduces average energy consumption of DC by 9.32% and 40.85% and average active PM by 18.30% and 21.89% in homogenous and heterogeneous environment, respectively. [ABSTRACT FROM AUTHOR]

With the rapid growth of Internet applications, massive demand for cloud services has brought challenges to data center networks (DCNs). Traditional wired DCNs adopt multilayer structure, leading to the congestion in high-layer switches. Furthermore, complex cabling in wired DCNs affects the flow of cold air, reduces the cooling efficiency, and increases energy consumption. To solve these problems, in this paper, we introduce an architecture for intra-DCN based on free space optic (FSO) wireless communication. The proposed scheme exploits spatial position of FSO transceivers at different heights to realize obstruction-free transmission for inter-rack requests. We formulate the transceiver arrangement problem into a mathematical model while satisfying the requirement for direct line-of-sight communication between any pair of transceiver nodes. Moreover, we evaluate the network performance of the proposed scheme via simulation in terms of flow completion time, server throughput, and power consumption. Compared with traditional ones, our scheme can considerably decrease power consumption while obtaining better network performance. [ABSTRACT FROM AUTHOR]

Cloud computing has become a critical infrastructure for modern society, like electric power grids and roads. As the backbone of the modern economy, it offers subscription‐based computing services anytime, anywhere, on a pay‐as‐you‐go basis. Its use is growing exponentially with the continued development of new classes of applications driven by a huge number of emerging networked devices. However, the success of Cloud computing has created a new global energy challenge, as it comes at the cost of vast energy usage. Currently, data centres hosting Cloud services world‐wide consume more energy than most countries. Globally, by 2025, they are projected to consume 20% of global electricity and emit up to 5.5% of the world's carbon emissions. In addition, a significant part of the energy consumed is transformed into heat which leads to operational problems, including a reduction in system reliability and the life expectancy of devices, and escalation in cooling requirements. Therefore, for the future generations of Cloud computing to address the environmental and operational consequences of such significant energy usage, they must become energy‐efficient and environmentally sustainable while continuing to deliver high‐quality services. In this article, we propose a vision for learning‐centric approach for the integrated management of new generation Cloud computing environments to reduce their energy consumption and carbon footprint while delivering service quality guarantees. In this article, we identify the dimensions and key issues of integrated resource management and our envisioned approaches to address them. We present a conceptual architecture for energy‐efficient new generation Clouds and early results on the integrated management of resources and workloads that evidence its potential benefits towards energy efficiency and sustainability. [ABSTRACT FROM AUTHOR]