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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]

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 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]

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]

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 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]

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]

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]

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]

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]

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Operating cloud service infrastructures requires high energy efficiency while ensuring a satisfactory service level. Motivated by data centers, we consider a workload routing and server speed control policy applicable to the system operating under fluctuating demands. Dynamic control algorithms are generally more energy-efficient than static ones. However, they often require frequent information exchanges between routers and servers, making the data centers' management hesitate to deploy these algorithms. This study presents a static routing and server speed control policy that could achieve energy efficiency similar to a dynamic algorithm and eliminate the necessity of frequent communication among resources. We take a robust queueing theoretic approach to response time constraints for the Quality of Service (QoS) conditions. Each server is modeled as a G / G / 1 processor sharing queue, and the concept of uncertainty sets defines the domain of stochastic primitives. We derive an approximative upper bound of sojourn times from uncertainty sets and develop an approximative sojourn time quantile estimation method for QoS. Numerical experiments confirm the proposed static policy offers competitive solutions compared with the dynamic algorithm. [ABSTRACT FROM AUTHOR]

The process of remapping virtual machines (VMs) to host machines (HMs), which is defined as VM replacement, affects cloud data centers (DCs) performance. To enhance the performance, optimal replacement of VMs regarding conflicting objectives has been proposed in some research, most of which use the non-dominance method to compare generated solutions. Although the non-dominance approach reaches acceptable results, it is not desirable in the VM replacement problem since we need only one mapping of VMs to HMs as the optimal solution to this problem. Indeed, in this method, a solution is considered better if it outperforms another solution regarding all parameters otherwise, two solutions are considered equally good. Also, a solution can be better even though it demonstrates poor performance regarding some parameters. In this paper, we propose two enhanced multi-objective algorithms, Fuzzy-RLVMrB and Fuzzy-MOVMrB, to address the mentioned problems of the non-dominance algorithm. The proposed algorithms aim to balance the horizontal and vertical load between PMs in terms of processor, bandwidth, and memory. We simulated all algorithms using the CloudSim simulator and compared them in terms of horizontal and vertical load balance, energy consumption, and execution time. The simulation results depict that Fuzzy-RLVMrB and Fuzzy-MOVMrB algorithms outperform other algorithms in terms of vertical load balancing and horizontal load balancing. In addition, Fuzzy-RLVMrB surpasses other approaches in terms of energy consumption. [ABSTRACT FROM AUTHOR]

Reducing the energy consumption while guaranteeing the quality of service (QoS) in the cloud data centers is challenge task for cloud providers. Dynamic virtual machine (VM) consolidation technology is regarded as a promising approach to satisfy goals. Considering dynamic workload of physical machine (PM) results in VM migration and high resources utilization of PM results in resources contention among VMs that affects working performance of VMs. Hence, it is vital to provide an efficient approach for dynamic VM placement during the consolidation to achieve the objectives while alleviating resources contention among VMs in the data centers. In this paper, the proposed strategy called LBVMP aims to build a novel conception consisting of a balancing flat surface of a PM in terms of CPU, RAM, bandwidth (BW) and another proportion flat surface that the remaining resources capacity of the targeted PM was divided by the request resources (CPU, RAM and BW) of a VM. Then LBVMP calculates the distance between two plats to evaluate VM allocation solutions. Extensive experimental results based on the CloudSim simulator demonstrate that compared with the state-of-the-art algorithm BCAVMP, the proposed strategy enables to reduce the cloud data centers of energy consumption, the number of migrations, SLAV, ESV by an average of 3.50%, 9.40%, 78.40%, 79.91%, respectively. [ABSTRACT FROM AUTHOR]

Fog computing contains resource-constrained devices closer to user proximity, which are insufficient for compute-intensive and large workloads. Cloud computing can help to address this limitation by transparently integrating fog and cloud data centers. However, the existing state-of-the-art does not integrate cloud and fog resources for maintaining application performance by dynamically offloading the tasks to the cloud while minimizing the cost. This paper introduces a new workload/job placement method that performs two-way offloading between cloud and fog to efficiently utilize the fog resources while minimizing the cost of using cloud resources to serve the workload. Our proposed solution is evaluated on various setups, including small-scale and large-scale data centers having homogeneous and heterogeneous physical machines (PMs) with different job batches, and compared with the existing state-of-the-art baseline method. Overall, the proposed solution outperforms the baseline method by utilizing 40.87 % more fog resources while reducing × 1.68 times cloud cost. [ABSTRACT FROM AUTHOR]

Chrome plating will be banned in all new cars starting in 2024 due to health-related issues, including the risk of lung cancer. The ban will take place in Europe and has also been proposed in California. Car companies will need to find alternative materials for exterior protection. In other news, researchers have developed a heat-resistant ceramic ink that can be used to mark metal components in the automotive industry. This ink allows for the detection of production glitches and can improve the efficiency of process chains. Additionally, materials science could help reduce energy consumption in data centers, and a new laser-based method has been developed to discover puncture-resistant materials. Finally, scientists have observed metals healing themselves, which could have significant implications for engineering. [Extracted from the article]

The energy consumption of data centers (DCs) is rising year by year, and cooling station accounts for more than 40% of the total DCs' energy consumption, which has a huge energy-saving potential. Building models for whole DCs' cooling station can help predict total power to improve the energy efficiency of the system, before this, establishing a single model for each component is a basic work. This paper mainly studies the chiller and cooling tower models and compares the predictive performance of the empirical model, hybrid model, and neural network model of chillers and cooling towers. Giving the model selection scheme of the chiller and cooling tower for the establishment of the whole system of the refrigeration station. For the chiller model, the Yoshida function model in empirical models has the highest accuracy with a mean square error of 0.0592, followed by the neural network model with a mean square error of 0.2, and the hybrid model has a lower accuracy than the former two models. For the cooling tower model, the empirical models and the neural network model have similar accuracy, and both are higher than the hybrid model. [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]

On-demand provisioning of computing resources using the network over the Cloud consumes a tremendous amount of energy. The energy consumed does not scale out efficiently with the computational use of the resources. A large portion accounts for the power consumption to drive the hardware and cooling facilities for the hardware resources. Nowadays, energy and power consumption have become the most critical concerns in data centers. This work highlights a novel technique to achieve energy efficiency through server consolidation to address the abovementioned situations by effectively scheduling Virtual Machines (VMs) onto the servers and rescheduling active VMs upon deletion of existing VMs through cold migration. An advanced technology, i.e. Wake on LAN (WoL), is employed through power wake for dynamic and wake up of the servers on private Cloud. Compared to existing models, the proposed approach's maximum estimated energy and power saved are experimentally verified to be 54% and 50%, respectively. [ABSTRACT FROM AUTHOR]

Cloud computing technology is widely used in many industries to build a large-scale data center. One of the main challenges in this technology is to minimize the energy utilization in data centres. The quality of service is remarkably obsessed with percentage of power utilization in cloud centres. Many methods have been explored to minimize the energy consumption and for effective Virtual Machine (VM) migration. But failed to attend the significant outcomes in migration and consolidation problems. Consolidation of VM into the minimal number of physical machines is considered one of the best solutions to consume minimum power. This research proposed an Energy Migration Optimized using Enhanced Pelican Remora (EMOEPR) for achieving the suitable placement of VM into minimum possible physical machine to diminish the number of overload hosts as much as possible. Also, it minimizes the energy consumption without violating service level agreement. The proposed method is simulated using Cloudsim Toolkit. The experimental procedure done on different workloads with various sizes of VM and PM, shows that does not affect service level agreement and outperforms compared with the existing techniques. The proposed method reduced 5% energy consumption, 22.62% of VM migrations, 8.62% of service level agreement violation when compared with the existing techniques. [ABSTRACT FROM AUTHOR]

Summary: Virtual machine (VM) consolidation is to assign a set of VMs requested by operators to the physical machines (PMs) in the data centers so that certain cost, profit or performance objective is optimized, subject to the PMs resource capacity constraints. It is an important mean to decrease the total power consumption by reducing the number of active physical machines (PM) in a cloud. In this context, most of the existing solutions rely on highly frequent live migration to reduce the number of active physical machines. However, live migration is a high resource consumption and time‐consuming operation, thus, frequent use of live migration not only increases energy consumption but also affects the stability of the physical machine, which in turn affects the services on the virtual machine. Reducing the number of active physical machines while reducing the number of live migrations is a major challenge in the face of massive fluctuating virtual machine loads. In order to solve this problem, in this paper, we present a VM consolidation algorithm for predictable loads (VCPL) to reduce the live migration operations. First, we present a cyclic usage prediction (CUP) method to predict the load in a whole cycle (a day) of a VM. Then, we separate the VMs with stable and cyclic load out from others and consolidate them to PMs by using VCPL to make sure each PM has a stable load. Thus, energy can be reduced by avoiding most of live migration operations, and the stability of the data center can be observably improved. We evaluate our solution through simulations on real‐world workloads, the results show that, 66% of long‐term VMs have stable and cyclic loads and are predictable, by using VCPL, the live migration operations occurring on the PMs which accommodate those VMs can be reduced significantly than other solutions. [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]