Search

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]

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]

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]

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]

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]

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]

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]

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]

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]

The significant energy consumption within data centers is an essential contributor to global energy consumption and carbon emissions. Therefore, reducing energy consumption and carbon emissions in data centers plays a crucial role in sustainable development. Traditional cloud computing has reached a bottleneck, primarily due to high energy consumption. The emerging federated cloud approach can reduce the energy consumption and carbon emissions of cloud data centers by leveraging the geographical differences of multiple cloud data centers in a federated cloud. In this paper, we propose Eco-friendly Reinforcement Learning in Federated Cloud (ERLFC), a framework that uses reinforcement learning for task scheduling in a federated cloud environment. ERLFC aims to intelligently consider the state of each data center and effectively harness the variations in energy and carbon emission ratios across geographically distributed cloud data centers in the federated cloud. We build ERLFC using Actor-Critic algorithm, which select the appropriate data center to assign a task based on various factors such as energy consumption, cooling method, waiting time of the task, energy type, emission ratio, and total energy consumption of the current cloud data center and the details of the next task. To demonstrate the effectiveness of ERLFC, we conducted simulations based on real-world task execution data, and the results show that ERLFC can effectively reduce energy consumption and emissions during task execution. In comparison to Round Robin, Random, SO, and GJO algorithms, ERLFC achieves respective reductions of 1.09, 1.08, 1.21, and 1.26 times in terms of energy saving and emission reduction. [ABSTRACT FROM AUTHOR]

Connecting to the internet will increase our computing challenges as it becomes an integral part of our daily lives. Therefore, it is necessary to advance the service qualities of Internet of Things (IoT) applications because the data produced by all these devices will need to be processed quickly and sustainably. Previously, cloud data centers with large capacity interface IoT devices with support servers. While IoT devices proliferate and generate massive amounts of data, communicating between devices and the Cloud is becoming more complex and harder, resulting in high costs and inefficiencies. Fog computing emerges as an approach to address the growing demand for IoT solutions. In this article, an IoT-fog-cloud application's general framework is developed, followed by an algorithm for Energy efficiency through an integrated approach computation model. Fog-Enabled Smart Cities (FESC) are proposed to minimize service delay and response time by using a fog offloading policy for the fog-enabled IoTs. Also, we developed an analytical model evaluating the proposed framework's effectiveness in reducing the delay of IoT services. Comparing the proposed model and the Alternating Direction Method of Multipliers (ADMM-VS) algorithm, the proposed model performs significantly better. Thus, by optimizing response and processing times, fog-enabled smart grids determine whether computation will be performed autonomously or semi-autonomously on fog nodes or in the Cloud. [ABSTRACT FROM AUTHOR]

The highlighted energy consumption of Internet data center (IDC) in China has become a pressing issue with the implementation of the Chinese dual carbon strategic goal. This paper provides a comprehensive review of cooling technologies for IDC, including air cooling, free cooling, liquid cooling, thermal energy storage cooling and building envelope. Firstly, the environmental requirements for the computer room and the main energy consumption items for IDC are analyzed. The evaluation indicators and government policies for promoting green IDC are also summarized. Next, the traditional cooling technology is compared to four new cooling technologies to find effective methods to maximize energy efficiency in IDC. The results show that traditional cooling consumes a significant amount of energy and has low energy efficiency. The application of free cooling can greatly improve the energy efficiency of IDC, but its actual implementation is highly dependent on geographical and climatic conditions. Liquid cooling, on the other hand, has higher energy efficiency and lower PUE compared to other cooling technologies, especially for high heat density servers. However, it is not yet mature and its engineering application is not widespread. In addition, thermal energy storage (TES) based cooling offers higher energy efficiency but must be coupled with other cooling technologies. Energy savings can also be achieved through building envelope improvements. Considering the investment and recovery period for IDC, it is essential to seek efficient cooling solutions that are suitable for IDC and take into account factors such as IDC scale, climate conditions, maintenance requirements, etc. This paper serves as a reference for the construction and development of green IDC in China. [ABSTRACT FROM AUTHOR]

Data centres, though a necessary part of modern society, are being stigmatised for consuming vast amounts of electricity for their operational and cooling needs. Due to Ireland's reliance on fossil fuels to meet the increased energy demand of data centres, the data centres are contributing significantly to Ireland's total carbon emissions. As much of this energy is expelled from data centres as waste heat energy, the potential for recycling some of this wasted heat energy was explored using environmentally friendly systems from recent publications. The recovered waste heat energy was applied in a vertical farming system, and the benefits of this waste heat to the vertical farm were analysed and quantified in two scenarios. Using conservative estimates, it was predicted that each vertical farm could be between 5–23% the size of the data centre and produce enough food to feed between 14–61 adults their daily calorie needs, and between 13–58 people their daily fresh produce requirements, depending on the scenario applied. For a more accurate prediction, each vertical farm would have to be assessed on a case-by-case basis, as there is no current research in this area. However, there was not enough data available on Irish data centres to perform these calculations. [ABSTRACT FROM AUTHOR]

Based on the cooling load of the data center in the Pearl River Delta region and the time-of-use tariff policy, a water storage cooling system was selected as the energy storage solution. The water cold storage system and operation control plan were optimized, and the operation and energy consumption of the water cold storage system were analyzed for typical days, months, and whole year. Through economic analysis of its operation, it was found that the annual cooling operation cost was reduced by 25. 78%, further verifying the feasibility of the water storage plan. Overall, it is shown that the water cold storage system can operate efficiently while ensuring the demand for indoor end load, and has a good effect of “peak-load shifting effect” and reducing cooling electricity costs. [ABSTRACT FROM AUTHOR]

Data center is one of the indispensable infrastructures in the modern information society, however, its high energy consumption and carbon emission have become an urgent problem. In order to reduce the large energy consumption of data centers in hot and humid areas due to mechanical cooling and air-conditioning systems, the operation of an indirect evaporative cooling (IEC) air-conditioning system was simulated in a hot and humid area data center based on Matlab and the Energy Plus platform, its annual energy consumption was evaluted. The impact of solar assisted dehumidification (LDD) system on the three operation modes and the performance of IEC air-conditioning system were also investigated. The results show that the IEC air-conditioning system can achieve 16.5% annual energy saving intensity compared to a conventional mechanical cooling system, and that the addition of an LDD unit can increase the wet mode operating time of the IEC air-conditioning system by 35.9% and reduce the system energy consumption by 20.1%. Therefore, IEC air-conditioning systems have significant energy-saving advantages in hot and humid data centers and can significantly improve the operational efficiency and sustainability of data center air-conditioning systems. [ABSTRACT FROM AUTHOR]

Aiming at the difficulty of accurately reflecting the actual effect of the engineering application of the data center’s energy consumption evaluation results. In this paper, on the basis of the existing data center energy efficiency evaluation index, from the data center, data center air conditioning system, indirect evaporative cooling air conditioning equipment in three dimensions, respectively, put forward the data center indirect evaporative cooling air conditioning PUE coefficient, data center air conditioning energy utilization efficiency, indirect evaporative cooling air conditioning equipment refrigeration density and other evaluation indicators. Using the new evaluation indexes as well as the assignment analysis method, case studies were conducted on data centers with different cooling systems. The results show that the indirect evaporative cooling air-conditioning system has obvious advantages of power and water saving compared with the traditional chilled water system, but its refrigeration density index still has much room for improvement. The proposal of the new index improves the energy efficiency evaluation system of data centers and can lead to further improvement of the performance quality of related air conditioning products. [ABSTRACT FROM AUTHOR]

To reduce energy consumption in data center cooling systems and ensure the healthy and rapid development of data centers, this study focuses on the cooling system of a data center in Guangzhou. Addressing the efficiency variations of chillers at different Partial Load Rates (PLR), a novel model predictive control method is proposed. The study employs Gaussian process regression to establish a chillers’ operational efficiency model and utilizes Long ShortTerm Memory (LSTM) neural networks to create an outdoor wet bulb temperature time series prediction model. Based on the data center cooling load and the prediction of outdoor wet bulb temperature, the cooling system’s chilled water storage and discharge modes, storage and discharge capacity, the number of chillers, and the PLR are adjusted. This adjustment aims to partially decouple the demand and supply sides, resulting in the chillers being operated in high efficiency or turned off, thereby reducing the operational energy consumption of the cooling system. To evaluate the performance of this strategy, annual energy consumption simulations were conducted, compared to the traditional rule-based control strategy, the model predictive control strategy reduced the overall energy consumption by 3.13% and operating costs by 3.33%. [ABSTRACT FROM AUTHOR]

With the launch of the "Channel Computing Resources from the East to the West" project, Beijing-Tianjin-Hebei region, as a national integrated big-data center system, has established a Zhangjiakou data center cluster and continuously optimized the regional computing power layout. Beijing has been accelerating the construction of a global digital economy benchmark city since 2021, committed to forming a collaborative development pattern of computing power integration, and achieving a globally leading density of data centers. The core industry of Beijing's digital economy has strong fundamental advantages, but as a high-energy consuming industry, data centers cannot fully meet real-time computing power needs solely relying on the local infrastructure construction in Beijing. Therefore, this paper explored the promoting role of the Beijing-Tianjin-Hebei national integrated big-data center system in the construction of a global digital economy benchmark city in Beijing through channels such as computing power resource sharing, green energy consumption support, and industrial development collaboration. Based on this, it analyzed the challenges that still exist in current regional collaboration. [ABSTRACT FROM AUTHOR]

The cloud computing center can dynamically respond to various needs, schedule computing resources, and provide users with convenient IT services. As the demand for cloud computing services continues to increase, the scale of the data center is getting larger and larger, and the problem of high energy consumption of equipment is becoming more and more prominent. Therefore, building a green data center is key to ensuring the development of the technology industry. Virtual machine online migration technology has been widely used in energy consumption management, which plays an important role in the energy‐saving management of large‐scale data centers. Considering the problem of energy consumption in a multi‐data center environment, a cross‐data center virtual machine migration strategy is proposed, EVMA. First, the target data center of the virtual machine migration is determined according to the bandwidth between data centers, and then the overload host and virtual machine selection strategy is determined according to the historical CPU load. The experimental results showed that the algorithm had a good performance in reducing the energy consumption of the data center and ensuring the quality of service. [ABSTRACT FROM AUTHOR]

The digital transition that drives the new industrial revolution is largely driven by the application of intelligence and data. This boost leads to an increase in energy consumption, much of it associated with computing in data centers. This fact clashes with the growing need to save and improve energy efficiency and requires a more optimized use of resources. The deployment of new services in edge and cloud computing, virtualization, and software-defined networks requires a better understanding of consumption patterns aimed at more efficient and sustainable models and a reduction in carbon footprints. These patterns are suitable to be exploited by machine, deep, and reinforced learning techniques in pursuit of energy consumption optimization, which can ideally improve the energy efficiency of data centers and big computing servers providing these kinds of services. For the application of these techniques, it is essential to investigate data collection processes to create initial information points. Datasets also need to be created to analyze how to diagnose systems and sort out new ways of optimization. This work describes a data collection methodology used to create datasets that collect consumption data from a real-world work environment dedicated to data centers, server farms, or similar architectures. Specifically, it covers the entire process of energy stimuli generation, data extraction, and data preprocessing. The evaluation and reproduction of this method is offered to the scientific community through an online repository created for this work, which hosts all the code available for its download. [ABSTRACT FROM AUTHOR]

With the increased penetration of cloud computing and virtualization, a plethora of internet of things devices have been deployed globally. As a result, computationally intensive tasks are transmitted from the edge towards the centralized cloud for processing that leads to increased energy utilization in the cloud data centers while at the same increasing significant latency for critical applications. Recent years have witnessed a paradigm shift from centralized cloud computing towards mobile edge computing (MEC), where computational tasks are offloaded at the edge servers near user equipment (UE). This paradigm leads to lowering the energy utilization in the cloud data centers, along with low latency for UE and efficient resource utilization at the edge. In this context, the scale and complexity of the MEC networks is drastically increasing and, consequently, finding effective energy-efficient solutions for computational task offloading and resource allocation in MEC networks has become an ambitious task. To address the aforementioned challenges, this work surveys the state of the art in different categorizations of algorithm-based computational task offloading and resource allocation strategies focusing on energy utilization. It also provides a detailed cross-comparison of existing strategies in terms of their implementation specifications. Additionally, this paper also highlights open challenges and potential future research directions to facilitate efficient task offloading and resource allocation at the edge with reduced energy consumption at the centralized data centers. Our work also paves the way for the deployment of critical applications at the edge that require low latency and high service quality guarantees. [ABSTRACT FROM AUTHOR]

The IoT has connected a vast number of devices on a massive internet scale. With the rapid increase in devices and data, offloading tasks from IoT devices to remote Cloud data centers becomes unproductive and costly. Optimizing energy consumption in IoT devices while meeting deadlines and data constraints is challenging. Fog Computing aids efficient IoT task processing with proximity to nodes and lower service delay. Cloud task offloading occurs frequently due to Fog Computing's limited resources compared to remote Cloud, necessitating improved techniques for accurate categorization and distribution of IoT device task offloading in a hybrid IoT, Fog, and Cloud paradigm. This article explores relevant offloading strategies in Fog Computing and proposes MCEETO, an intelligent energy-aware allocation strategy, utilizing a multi-classifier-based algorithm for efficient task offloading by selecting optimal Fog Devices (FDs) for module placement. MCEETO decision parameters include task attributes, Fog node characteristics, network latency, and bandwidth. The method is evaluated using the iFogSim simulator and compared with edge-ward and Cloud-only strategies. The proposed solution is more energy-efficient, saving around 11.36% compared to Cloud-only and approximately 9.30% compared to the edge-ward strategy. Additionally, the MCEETO algorithm achieved a 67% and 96% reduction in network usage compared to both strategies. [ABSTRACT FROM AUTHOR]

Air cooling is the traditional solution to chill servers in data centers. However, the continuous increase in global data center energy consumption combined with the increase of the racks' power dissipation calls for the use of more efficient alternatives. Immersion cooling is one such alternative. In this paper, we quantitatively examine and compare air cooling and immersion cooling solutions. The examined characteristics include power usage efficiency (PUE), computing and power density, cost, and maintenance overheads. A direct comparison shows a reduction of about 50% in energy consumption and a reduction of about two-thirds of the occupied space, by using immersion cooling. In addition, the higher heat capacity of used liquids in immersion cooling compared to air allows for much higher rack power densities. Moreover, immersion cooling requires less capital and operational expenditures. However, challenging maintenance procedures together with the increased number of IT failures are the main downsides. By selecting immersion cooling, cloud providers must trade-off the decrease in energy and cost and the increase in power density with its higher maintenance and reliability concerns. Finally, we argue that retrofitting an air-cooled data center with immersion cooling will result in high costs and is generally not recommended. [ABSTRACT FROM AUTHOR]

Data Centres serve as the foundation for digital technologies in the energy sector, enabling advanced analytics, optimization, and automation. However, their rapid growth can exert a substantial influence on the environment due to their energy consumption, water utilization, and production of electronic waste. This research begins with an energy overview of the setup and operations of data centres, highlighting their key components and infrastructure, and emphasizing their crucial role in managing energy resources and driving the energy sector's digital technologies. Building upon this understanding, a holistic framework is proposed to tackle energy sustainability concerns in data centres, with a focus on energy-related aspects. The framework places emphasis on three primary sustainability metrics, namely energy efficiency, water consumption, and waste management. It underscores the significance of green building design principles and energy-efficient equipment as crucial constituents of sustainable data centre infrastructure. The framework delineates optimal energy operational best practices encompassing virtualization and consolidation, effective cooling tactics, and energy management and monitoring, all aimed at reducing energy consumption and enhancing energy performance. Furthermore, the framework emphasizes the significance of incorporating energy-related sustainability metrics into decision-making procedures and adhering to regulatory standards for energy efficiency. Through adherence to this framework, data centres' environmental impact can be mitigated and a positive contribution towards a sustainable future can be made, particularly in the realm of energy conservation and optimization. [ABSTRACT FROM AUTHOR]

VMP (Virtual Machine Placement) is a crucial technology for energy consumption optimization of the cloud data center. Existing works mainly focus on virtual machine consolidation to increase resource utilization and reduce computing energy consumption. However, existing studies usually ignore the thermal effect that an intensive workload on IT (Information Technology) equipment can raise energy consumption by cooling systems and generate hotspots. In addition, an excessive number of virtual machine migrations increases migration costs and risks violating the SLA (Service Level Agreement) signed with users. In this paper, we present a comprehensive system model and formulate the problem as a constrained multi-objective optimization. We propose a novel thermal-aware VMP strategy to solve the problem by jointly considering virtual machines' migration cost, energy consumption, and heat recirculation around server racks. Our strategy makes placement decisions using MOPFGA (Multi-objective algorithm based on Pathfinder Algorithm and Genetic Algorithm) that combines classic MOPFA and GA enhanced by OBL (Opposition Based Learning) for fast convergence and avoidance of local optimum. Extensive experiments based on CloudSim using real data center workload data from PlanetLab show that our algorithm overcomes the defects of the MOPFA (multi-objective pathfinder algorithm) and GA (genetic algorithm) and significantly improves the overall efficiency of a data center. Compared with several state-of-the-art algorithms, MOPFGA on average reduces virtual machine migrations by 77.52%, increases CRAC (Computer Room Air Conditioner) supply temperature by 1.24%, and reduces cooling energy consumption by 24.78% and computational energy consumption by 23.62%. [ABSTRACT FROM AUTHOR]

Massive MIMO systems can support a large number of Internet of Things (IoT) devices, even if the number of IoT devices exceeds the number of service antennas in a single base station (BS) located at the data center. In order to improve the performance of Massive MIMO with massive IoT connectivity in a BS, simple scheduling and power control schemes can be of great help, but typically, they require high power consumption in the situation of serious shadow fading. In this paper, we try to improve the performance of Massive MIMO with massive IoT connectivity by using the dropping technique that drops the IoT devices that require high power consumption. Several scheduling and power control schemes have been proposed to increase the spectral efficiency (SE) and the energy efficiency (EE) of Massive MIMO systems. By the combination of these schemes with the dropping technique, we show that the performance can be even further increased under some circumstances. There is a dropping coefficient factor (DCF) to determine the IoT devices that should be dropped. This technique gives more benefits to the power control schemes that require higher power consumption. Simulation results and relevant analyses are provided to verify the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

Deploying data centers in the waters near coastal cities can greatly reduce the distance between data and users, not only eliminating the need to occupy land resources, but also saving energy consumption, which is a completely green and sustainable data center solution. Under the background of resource conservation and green development, this paper puts forward the project of undersea data center heat dissipation. Through the establishment of multiple linear regression model, the influence of tidal and seasonal changes on the heat dissipation of undersea data center is analyzed. Finally, it is concluded that the seasonal changes have the greatest impact on the heat dissipation of the undersea data center container. In the future, the placement strategy of data center and the methods to improve the heat dissipation efficiency will be further discussed. [ABSTRACT FROM AUTHOR]

Featured Application: This study develops a model for a chip-level two-phase cooling system in data centers and employs simulation to investigate its operational characteristics and performance. These findings offer valuable insights for practical implementation. As a powerful solution for heat dissipation in data centers, chip-level cooling continues to capture escalating attention in research and application domains. To accurately analyze system performance, identify potential avenues for system optimization, and inform future practical applications, we developed a steady-state, one-dimensional mathematical model for a novel pump-driven chip-level two-phase cooling system (PCTCS). This model was constructed based on our previous study and was confirmed against existing experimental data. Our simulations scrutinized PCTCS performance under default conditions and investigated the effects of key parameters, such as refrigerant type, condenser vertical positioning, and cooling water temperature. Results showed that the system could manage an 80 W power output from each CPU while maintaining CPU temperatures around 79 °C at a cooling water temperature of 45 °C. We discovered the choice of refrigerant had a significant impact on performance, with R32 outperforming R134a and R113. While the vertical position of the condenser influenced the PCTCS's internal parameters, its overall impact on system performance was negligible. Moreover, provided the chip temperature remained within a safe range, our study found that increasing the cooling water temperature improved the energy efficiency ratio of the refrigerant pump and reduced the temperature difference between the chips and the cold source. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has induced many advances in the modern world, thus always arousing great interest from the scientific community. Due to a large number of connected devices, this paradigm has put into circulation an enormous data quantity to be processed and offloaded while respecting latency and energy constraints that Central Cloud alone cannot meet. Hence, Fog Computing has come to fill these gaps by providing computing, management, and storage resources via small‐distributed data centres located at the network edge. The majority of previous research on centralised clustering in an IoT environment did not consider both residual energy and the location of the IoT devices, as well as the signalling communication frequency, to solve an issue of extreme energy consumption during data exchange between IoT devices and Fog entities. Thus, the objective of this study is to find solutions taking into account these criteria to guarantee the Fog system's energy efficiency while coping with the energy consumption increase related to the handling of massive data volume. Indeed, the proposed study presents a novel approach based on Fog‐IoT architecture to ensure intelligent energy management during communication and offloaded task processing in IoT applications. The simulation results show its effectiveness. [ABSTRACT FROM AUTHOR]

With the rapid growth of cloud computing services, the high energy consumption of cloud data centers has become a critical concern of the cloud computing society. While virtual machine (VM) consolidation is often used to reduce energy consumption, excessive VM consolidation may lead to local hot spots and increase the risk of equipment failure. One possible solution to this problem is to utilize thermal-aware scheduling, but existing approaches have trouble realizing the balance between SLA and energy consumption. This paper proposes a novel method to manage cloud data center resources based on thermal management (TM-VMC), which optimizes total energy consumption and proactively prevents hot spots from a global perspective. Its VM consolidation process includes four phases where the VMs scheduler uses an improved ant colony algorithm (UACO) to find appropriate target hosts for VMs based on server temperature and utilization status obtained in real-time. Experimental results show that the TM-VMC approach can proactively avoid data center hot spots and significantly reduce energy consumption while maintaining low Service Level Agreement (SLA) violation rates compared to existing mainstream VM consolidation algorithms with workloads from real-world data centers. [ABSTRACT FROM AUTHOR]

As the volume of data keeps growing rapidly, more and more storage devices, servers, and network devices are continuously added into data center networks (DCNs) to store, manage, and analyze the data. The industry experience indicates that, instead of adding a huge number of servers into the DCNs at a time, the DCN can also be expanded gradually by adding a small number of servers from time to time. This paper proposes a new type of dual-centric data center network structure, called GHDC (Generalized Hypercube Data Center network architecture), which is constructed by using commodity switches and multi-port servers. After analyzing the shortest distance between any two vertices, a routing algorithm for GHDC is developed. To achieve incremental scalability, two incomplete GHDC structures are proposed. A small number of servers can be added into the incomplete GHDC structures while their topological properties are maintained. The analysis and experiment results show that GHDC significantly outperform the other DCN structures, such as FatTree, BCube, Platonica, FCell, FSquare, and FRectangle, in terms of the incremental scalability and robustness. The average throughput of GHDC is approximately comparable to that of FatTree, BCube, and FSquare, and is higher than that of Platonica, FCell, and FRectangle by about 130.2%, 17.45% and 25.5%. Compared with the FatTree, BCube, FCell, FRectangle, and FSquare, GHDC reduces the cost by about 68.49%, 78.04%, 10.84%, 22.85%, and 29.55%, and reduces the max energy consumption by about 69.45%, 34.48%, 11.55%, 24.31%, and 29.58%, respectively. The actual energy consumption of GHDC is much lower than that of FatTree, BCube, FCell, FRectangle, and FSquare, and is little higher than that of Platonia. [ABSTRACT FROM AUTHOR]

Cloud remains an active and dominant player in the field of information technology. Hence, to meet the rapidly growing requirement of computational processes and storage resources, the cloud provider deploys efficient data centres globally that comprise thousands of IT servers. Because of tremendous energy and resource utilization, a reliable cloud platform has to be necessarily optimized. Effective load balancing is a great option to overcome these issues. However, loading balancing difficulties, such as increased computational complexity, the chance of losing the client data during task rescheduling, and consuming huge memory of the host, and new VM (Virtual Machine), need appropriate optimization. Hence, the study aims to create a newly developed IG-WA (Inquisitive Genetic–Wolf Optimization) framework that meritoriously detects the optimized virtual machine in an environment. For this purpose, the system utilises the GWO (Grey Wolf Optimization) method with an evolutionary mechanism for achieving a proper compromise between exploitation and exploration, thereby accelerating the convergence and achieving optimized accuracy. Furthermore, the fitness function evaluated with an inquisitive genetic algorithm adds value to the overall efficacy. Performance evaluation brings forward the outperformance of the proposed IGWO system in terms of energy consumption, execution time and cost, makespan, CPU utilization, and memory utilization. Further, the system attains more comprehensive and better results when compared to the state of art methods. [ABSTRACT FROM AUTHOR]

Although the coexistence of ARM and INTEL technologies in green data centres is technically feasible, significant challenges exist that must be addressed. These challenges stem from the differences in instruction sets and power consumption between the two processor architectures. While ARM processors are known for their energy efficiency, INTEL processors tend to consume more power. Consequently, evaluating the performance of hybrid architectures can be a complex task. The contributions of this article consist of (i) a multiformalism-based model of a data centre, providing a natural and convenient approach to the specification process and performance analysis of a realistic scenario and (ii) a review of the performance indices, including the choice of one architecture over another, power consumption, the response time, and request loss, according to different policies. As a result, the model aims to address issues such as system underutilization and the need to estimate the optimal workload balance, thereby providing an effective solution for evaluating the performance of hybrid hardware architectures. [ABSTRACT FROM AUTHOR]

Cloud data centers consume high volume of energy for processing and switching the servers among different modes. Virtual Machine (VM) migration enhances the performance of cloud servers in terms of energy efficiency, internal failures and availability. On the other end, energy utilization can be minimized by decreasing the number of active, underutilized sources which conversely reduces the dependability of the system. In VM migration process, the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the operations. In this view, the current study develops an Improved Metaheuristic Based Failure Prediction with Virtual Machine Migration Optimization (IMFP-VMMO) model in cloud environment. The major intention of the proposed IMFP-VMMO model is to reduce energy utilization with maximum performance in terms of failure prediction. To accomplish this, IMFPVMMO model employs Gradient Boosting Decision Tree (GBDT) classification model at initial stage for effectual prediction of VM failures. At the same time, VMs are optimally migrated using Quasi-Oppositional Artificial Fish Swarm Algorithm (QO-AFSA) which in turn reduces the energy consumption. The performance of the proposed IMFP-VMMO technique was validated and the results established the enhanced performance of the proposed model. The comparative study outcomes confirmed the better performance of the proposed IMFP-VMMO model over recent approaches. [ABSTRACT FROM AUTHOR]