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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]

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]

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]

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 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]

Summary: Astra, deployed in 2018, was the first petascale supercomputer to utilize processors based on the ARM instruction set. The system was also the first under Sandia's Vanguard program which seeks to provide an evaluation vehicle for novel technologies that with refinement could be utilized in demanding, large‐scale HPC environments. In addition to ARM, several other important first‐of‐a‐kind developments were used in the machine, including new approaches to cooling the datacenter and machine. This article documents our experiences building a power measurement and control infrastructure for Astra. While this is often beyond the control of users today, the accurate measurement, cataloging, and evaluation of power, as our experiences show, is critical to the successful deployment of a large‐scale platform. While such systems exist in part for other architectures, Astra required new development to support the novel Marvell ThunderX2 processor used in compute nodes. In addition to documenting the measurement of power during system bring up and for subsequent on‐going routine use, we present results associated with controlling the power usage of the processor, an area which is becoming of progressively greater interest as data centers and supercomputing sites look to improve compute/energy efficiency and find additional sources for full system optimization. [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]

Summary: To meet the ever‐increasing requirements of the applications, cloud service providers have further built and managed multiple cloud data centers in multiple regions across geographies with many physical machines (PMs). However, most of the existing resource allocation algorithms are developed for a single cloud data center, which normally cannot efficiently handle the load burst occasions where a single cloud data center may not be enough to satisfy the demands burst of applications. Therefore, it is necessary to consider how to efficiently manage multiple cloud data centers while meeting application requirements and reducing energy consumption. This paper first systematically analyzes multiple cloud data centers and energy consumption models. Then, an energy‐efficient method of Resource Allocation based on Request Prediction in multiple cloud data centers (RARP) is proposed. The RARP method constructs a resource allocation framework based on request prediction in multiple cloud data centers, which anticipates the application request volume in advance. At the same time, the RARP method allocates VMs and PMs based on the principle of minimum remaining resources available to achieve minimum usage of PMs, thus minimizing energy consumption to complete application requests. Extensive experiments are conducted on the proposed RARP method through the simulation platform CloudSim. Finally, the experimental test results show that the accuracy of request detection and the energy consumption of cloud data centers are significantly better than those of the comparison algorithms. [ABSTRACT FROM AUTHOR]

Summary: Cloud services can be expanded and offered in a flexible manner depending on the needs of the users, and cloud computing has become more popular. The energy consumption to provide users with service quality is the biggest concern in virtualization technology. Therefore, reducing energy consumption is a key step in reducing the operating costs of various organizations' data centers. In this article, we propose a dynamic VM relocation (DVMR) system used inside data centers to study virtualization technologies for reducing energy consumption in cloud computing environments. The DVMR system provides a method for predicting PM load by using and selecting loaded and unloaded PM, thereby effectively managing virtualization and energy saving. Experimental evaluation is done by implementing load threshold‐based relocation. The results show that the proposed system can efficiently balance the load between different PMs and efficiently decrease the energy consumption of the entire system. [ABSTRACT FROM AUTHOR]

Summary: By incorporating on‐demand resources, software and data for collaborative services through the Internet, the conventional Information Technology enterprise has been transformed by cloud computing. Based on the pay‐per‐use approach, Infrastructure, platform or Software resources and servers located across data centres are among the several types of resources offered to consumers in cloud computing. Data centres handle these resources. These resources are constantly provisioned to users based on their availability, demand, and quality requirements. Cloud computing systems are known as one of the largest utilisers of energy resources all over the world. Also, power consumption has become a crucial aspect as most cloud computing systems work on traditional nonrenewable resources of energy. In order to make data centres environment‐friendly, there is a need for optimal approaches to reduce energy consumption and their hazardous effects on the environment. To analyse different available strategies for building and maintaining an energy‐efficient cloud is the main objective of this paper. The paper will comprehensively review several energy‐efficient resource provisioning methods and provide a graphical comparative study of Quality of Service (QoS) Metrics in cloud computing. Moreover, the present study identifies the areas of study that need to be further improved to increase the energy efficiency of cloud computing systems. [ABSTRACT FROM AUTHOR]

In this article, we address a critical concern of the growth of computer servers number and the resulting power expenditures in data centers by analyzing the statistical metrics related to the workload executed in each physical node. The aim is to build a stochastic model for power consumption estimation based on historical data. Relying on in‐depth investigation and experimental testing of the power consumption features and performance of the various workload datasets, we propose a model that considers the workload and the power consumed to be executed by a server as random variables. Based on the properties of the probabilistic distribution function of each random variable, we establish the correlation relationship between the workload and the power consumption using a non‐parametric approach. Our use of a non‐parametric method to learn a given probability model is challenging because it requires estimating the full distribution from the available data samples. The accuracy of our approach is demonstrated by estimating the energy consumption of various workloads. The experimental and simulation results show that our model outperforms many existing approaches in terms of accuracy, and it can be applied to a wide variety of workloads. [ABSTRACT FROM AUTHOR]

There is a drastic increase in urbanization over the past few years, which requires energy‐efficient and optimized solutions for transportation, governance, quality of life in a smart city among all the citizens. The Internet‐of‐Energy (IoE) ecosystem offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoE applications is increased while IoE devices continue to grow. Therefore, smart city solutions must have the ability to utilize energy and handle the associated challenges efficiently. Moreover, energy Optimization (EO) techniques can be used to reduce energy consumption to meet the sustainability goals in IoE. Different techniques have been proposed for EO in various fields by researchers worldwide. Computing systems also need energy optimization. The energy consumption in the data center, clouds, and blockchain (BC)‐based architectures are a point of concern at the current time. Due to the enormous energy demands of these systems, we cannot take advantage of the latest technologies to their fullest. Due to the emergence of new technologies and some limitations of proposed techniques, we can still not optimize energy usage more than some extent. There is minimal exploration done in energy optimization in BC‐based systems. In this paper, we have proposed a survey on the energy optimization techniques in various systems, including the optimization techniques in BC‐based systems. We have proposed a taxonomy that classifies energy optimization techniques. We have also proposed an energy‐efficient consensus mechanism, Proof‐of‐High Performance optimization (named as PoHPo), for High‐Performance Computing (HPC) based ecosystems. The open issues and challenges are then discussed in EO. The survey intends to propose future directions for industry professionals, green‐energy stakeholders, and researchers worldwide to explore this topic further. [ABSTRACT FROM AUTHOR]

Cloud data centers do not completely use their resources, resulting in resource underutilization. Cloud computing companies primarily leverage virtualization technologies to supply cost‐effective service provision. In order to optimize cloud performance, virtual machines (VMs) must be placed among physical machines (PMs). When it comes to concentrating on the issues in the cloud computing environment, effective VM placement (VMP) is one of the primary difficulties that might cost suppliers money. VMP may be applied in a variety of ways in cloud computing. In terms of lowering related processing overhead, consolidating the cloud environment to become a highly on‐demand method, balancing the load among PMs, power usage, and refining performance, VMP techniques still require improvement in the computing environment. This study aims to provide a comprehensive overview of VMP approaches. This article provides an up‐to‐date survey of the most related VMP literature to highlight study possibilities in cloud settings utilizing nature‐inspired metaheuristic algorithms. The findings suggest that placing VMs in the most efficient place saves power usage substantially. The key problem is to minimize data center energy usage without compromising performance or breaking service level agreements. Finally, we will discuss and look at what further may be accomplished in this line of science. [ABSTRACT FROM AUTHOR]

Increasing resource efficiency and reducing the energy consumption of cloud data centers is critical, especially during the global CORONA virus pandemic. Virtual machines' consolidation using live migration maximizes the hosts' and the reduction of energy consumption. An increase in the host's virtual machines in the consolidation process and the dynamic workload of the virtual machines may cause the overloading in the hosts. One approach to overcome this problem is reducing the hosts' virtual machines. One crucial issue to improve the quality of the consolidation process's quality is determining the best virtual machine for the migration process. Although the selection process has lower computational complexity than other challenges (like placement and overload prediction) in the consolidation process, this issue has received less attention. This article aims to present an efficient algorithm for the selection process. We first considered five main criteria for the selection process: migration time, migration risk, virtual machine connectivity, releasable resources, and penalty for SLA violation. Then, we propose an algorithm based on analytic hierarchy process multi‐criteria decision‐making technique. Next, to determine the weight of the proposed criteria, we simulate thousands of virtual machines of the PlanetLab workloads. These weights are tunable based on the data center preferences. The results of the suggested approach results show 23% reduction in the hosts' energy consumption, 49% reduction in the number of migrations, and 18% reduction in the SLA violation compared with other techniques. So, using the proposed method may significantly reduce the overall cost of the data centers. [ABSTRACT FROM AUTHOR]

Cloud data center consumes enormous amount of electrical power resulting in high operating cost and carbon dioxide emission. Virtual machine (VM) consolidation strives to reallocate VMs to the minimum number of physical machines dynamically to reduce energy consumption. An approach is proposed to solve consolidation problem of VMs toward optimization of their energy consumption. A novel algorithm is presented, and an energy consumption model is also presented to support the algorithm to find optimal solutions. Experimental results show that energy consumption can be reduced greatly by dynamic VM consolidation. In comparison with other classic algorithms, our proposed algorithm outcomes them and achieves average savings of 42% on energy consumption. [ABSTRACT FROM AUTHOR]

The energy consumption of virtualized data centers has grown very fast in last several years. Because a large number of hosts are running in an idle state, virtualized data centers waste a large amount of electric energy. To save more energy for virtualized data centers, an energy‐saving mechanism is proposed based on switching operating mode of physical servers and reserving virtual machines (VMs). The main idea is that when the amount of idle VMs reaches twice of a specified threshold, half of these idle VMs are reserved to process the new task that is about to arrive, and the physical server which hosts the other half of the idle VMs is switched to the sleep mode. From the perspective of task arrival rate and sleep parameters, we use two‐dimensional Markov processes to analyze the proposed energy‐saving mechanisms. By using matrix geometry solutions, we theoretically estimate energy consumption and response performance. According to the numerical experiments, the proposed energy‐saving mechanism obviously cut down electric energy consumption and ensures response performance. Finally, the specified threshold for the amount of VMs is optimized by building a cost function. [ABSTRACT FROM AUTHOR]

Energy efficiency of data centers (DCs) is of great concern due to their large amount of energy consumption and the foreseeable growth in the demand of digital services in the future. The past decade witnessed improvements of the energy efficiency of DCs from an extensive margin—a shift from small to large, more efficient DCs. Improvements from the intensive margin, that is, from more efficient operation, would be critical in limiting the energy consumption and environmental impact of DCs in the upcoming period. Machine learning algorithms have advantages in optimizing the operation of DCs to improve energy efficiency as they have shown the potential of discovering control strategies not found by traditional method, and producing working condition‐dependent control strategies. This study proposes ready‐to‐use machine learning methods with practical details to decrease the most commonly used energy efficiency metric—power usage effectiveness in DCs. We achieved an accurate prediction by properly selecting the features used in the proposed prediction models established by neural network, light gradient boosting machine, recurrent neural network, and random forests. The proposed approaches are implemented in one of the largest hyperscale DCs in China—Tencent Tianjin DC, to optimize the set points of controllable variables in the cooling system and to detect and adjust the unreasonable working conditions in the modular data centers. The lower bound of PUE reduction was 0.005 with the proposed approaches, leading to about 1500 MWh (0.24% of the total designed electricity consumption of this DC) of energy saved per year in this hyperscale DC. The proposed methods have the potential to be transferred to DCs of similar scale, and the framework of our work could serve as a guide for machine learning‐based optimization of environmental indicators in other complex product/service systems. [ABSTRACT FROM AUTHOR]

Workload consolidation is an important method for the efficient operation of cloud data centers, impacting important quality attributes such as resource utilization and power consumption. Many different approaches have been proposed for workload consolidation, but few comparative studies were executed to date. Therefore, it is unclear which of the proposed approaches work best in which situation. In this article, we present a comprehensive simulation‐based comparison of five workload consolidation techniques. We introduce a general framework for workload consolidation techniques to the DISSECT‐CF simulator to foster the development and comparison of efficient data center consolidation algorithms. We use this framework to evaluate the effectiveness of a first fit best fit decreasing heuristic, a custom heuristic, and three population‐based metaheuristics (genetic algorithm, artificial bee colony, and particle swarm optimization). The evaluation is based on a wide variety of real‐world workload traces. The five algorithms are compared in terms of total energy consumption, the duration of the simulation, and the number of migrations. Based on the results, there is no generally best consolidation technique. The results deliver insight into the pros and cons of the algorithms as well as the impact of different parameters. In particular, the results show that population‐based metaheuristics do not offer a significant gain in terms of solution quality to compensate for the increased simulation time. [ABSTRACT FROM AUTHOR]

Summary: Energy efficiency is a contemporary and challenging issue in geographically distributed data centers. These data centers consume significantly high energy and cast a negative impact on the energy resources and environment. To minimize the energy cost and the environmental impacts, Internet service providers use different approaches such as geographical load balancing (GLB). GLB refers to the placement of data centers in diverse geolocations to exploit variations in electricity prices with the objective to minimize the total energy cost. GLB helps to minimize the overall energy cost, achieve quality of service, and maximize resource utilization in geo‐distributed data centers by employing optimal workload distribution and resource utilization in the real time. In this paper, we summarize various optimization‐based workload distribution strategies and optimization techniques proposed in recent research works based on commonly used optimization factors such as workload type, load balancer, availability of renewable energy, energy storage, and data center server specification in geographically distributed data centers. The survey presents a systemized and a novel taxonomy of workload distribution in data centers. Moreover, we also debate various challenges and open research issues along with their possible solutions. [ABSTRACT FROM AUTHOR]

The electric energy demand of data centers in Germany has grown rapidly from 10.5 TWh/a in 2010 to 13.2 TWh/a in 2017, an average of 25% of which are used to fulfill the data centers' cooling demand. In order to increase its energy efficiency, TU Darmstadt applies a new cooling concept in the next generation of its high‐performance computing data center "Lichtenberg II." Instead of the current air‐cooled servers with water‐cooled rear doors at 17‐24°C, the new data center will be equipped with direct hot‐water cooling for the high‐performance computer, supplying heat at a temperature of 45°C. The high‐temperature waste heat is used for heating purposes on the university's campus Lichtwiese. For waste heat utilization, two concepts are presented, either integrating the heat in the return line of the district heating network or using it locally in buildings located near the data center. Reductions in CO2 emission and annuity are generated both by decreased compression cooling demand for the data center and by decreased heat generation due to waste heat utilization. Depending on the scenario, a total of 20%‐50% of the waste heat emitted by the high‐performance computer can be used for heating purposes, while the remaining heat is dissipated efficiently via free cooling without additional energy demand for mechanical chillers. CO2 emission can be decreased by up to 720 tCO2/a, representing a reduction of about 4% of the total emission at campus Lichtwiese. TU Darmstadt is currently implementing the waste heat integration into its district heating network and will benefit from this concept starting in 2020. [ABSTRACT FROM AUTHOR]

Summary: The efficient use of energy is essential to address concerns of cost and sustainability. Many data centers contain MapReduce clusters to process Big Data applications. A large number of machines and fault tolerance capabilities make MapReduce clusters energy inefficient. In this paper, we present a Configurator based on performance and energy models to improve the energy efficiency of MapReduce systems. Our solution is novel as it takes into account the dependence of the performance and energy consumption of a cluster on MapReduce parameters. While this dependence is known, we are the first to model it and design a Configurator to optimize these parameter settings for maximizing the energy efficiency of MapReduce systems. Our empirical evaluations show that the Configurator can result in up to 50% improvement in the energy efficiency of typical MapReduce applications in two architecturally different clusters. [ABSTRACT FROM AUTHOR]

Summary: With the rapid development of cloud computing, many distributed data centers have been deployed. This means larger energy consumption requirements from the data center. How to reduce the cost of data center has received significant attention recently. Although there are several efforts in studying energy consumption of the data center, very few have considered modeling and analyzing cost‐aware job scheduling for the cloud data center. To address this emerging problem, we propose a systematic approach that considers both basic elements and their relationships in cloud data center. First, we present a formal language to describe the cloud data center, and a job scheduling net is proposed to formally model the basic elements such as user request, Web portal, data center, and server. Second, we minimize the total cost of the cloud data center by considering the multidimensional resource and local electricity price on the basis of the state space of constructed model. The dynamic job scheduling algorithm and its specific execution steps are proposed based on the alternating direction method of multipliers algorithm. Third, the operational semantics and related theories of Petri nets for establishing the correctness of our proposed method are presented. Finally, a series of simulations are performed to illustrate that the proposed method can guarantee the correct behavior of job scheduling in the cloud data center while meeting the required cost. [ABSTRACT FROM AUTHOR]

Summary: Number of cloud data centers which consists of hundreds of hosts has increased tremendously around the world due to increase in demands for cloud services. It is expected energy consumption of data centers will reach 139.8 billion Kwh by 2020. Many algorithms are proposed to reduce energy consumption as well as service level agreement violationby minimizing the number of active hosts. Current proposed algorithms do not consider data center architecture, the physical position of hosts, and energy consumption of numerous switches that are in data centers. In this paper, a novel hierarchical cloud resource management is proposed that not only minimizes the number of hosts but also aggregates virtual machines on a limited subset of data center racks and modules to minimize energy consumption. Experimental results with Cloudsim show that our proposed algorithm reduces energy consumption up to 26% and service level agreement violation up to 96%. [ABSTRACT FROM AUTHOR]

Data centers today account for up to 3% of all global electricity consumption and produce 200 million metric tons of CO 2 . Hence, improving energy efficiency is an important operational concern. This article examines common operating practices such as server clustering, powering on/off, and bang–bang control in terms of energy efficiency. Extending the mixed integer programming (MIP) formulation in a previous research study, we introduce new constraints reflecting the operating practices adopted for operational convenience. We develop an algorithmic strategy for constructing energy efficient clusters of servers using two upper bounds on the cluster size. Numerical experiments show that server clustering and bang–bang control do not diminish energy efficiency, and that powering on/off by itself is insufficient. Our results produce a conclusion that differs from the published literature on data center design and operations. © 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 71(2), 107–119 2018 [ABSTRACT FROM AUTHOR]

Summary: With the explosive growth of data, hundreds of thousands of servers may be contained in a single data center. Hence, node failures are unavoidable and generally negatively effects the performance of the whole data center. Additionally, data centers with a large number of nodes will cause plenty of energy consumption. Many existing task scheduling techniques can effectively reduce the power consumption in data centers by considering heat recirculation. However, the traditional techniques do not take the situation of node failures into account. This paper proposes an airflow‐based failure model for data centers by leveraging heat recirculation. In this model, the spatial distribution and time distribution of failures are considered. Furthermore, a genetic algorithm (GA) and a simulated annealing algorithm (SA) are implemented to evaluate the proposed failure model. Because the positions of node failures have a significant impact on the heat recirculation and the energy consumption of data centers, failures with different positions are analyzed and evaluated. The experimental results demonstrate that the energy consumption of data centers can be significantly reduced by using the GA and SA algorithms for task scheduling based on the proposed failure model. [ABSTRACT FROM AUTHOR]

Summary: Data centers consume an enormous amount of energy to meet the ever‐increasing demand for cloud resources. Computing and Cooling are the two main subsystems that largely contribute to energy consumption in a data center. Dynamic Virtual Machine (VM) consolidation is a widely adopted technique to reduce the energy consumption of computing systems. However, aggressive consolidation leads to the creation of local hotspots that has adverse effects on energy consumption and reliability of the system. These issues can be addressed through efficient and thermal‐aware consolidation methods. We propose an Energy and Thermal‐Aware Scheduling (ETAS) algorithm that dynamically consolidates VMs to minimize the overall energy consumption while proactively preventing hotspots. ETAS is designed to address the trade‐off between time and the cost savings and it can be tuned based on the requirement. We perform extensive experiments by using the real‐world traces with precise power and thermal models. The experimental results and empirical studies demonstrate that ETAS outperforms other state‐of‐the‐art algorithms by reducing overall energy without any hotspot creation. [ABSTRACT FROM AUTHOR]

Cloud computing provides online services to users from anywhere in a pay‐as‐you‐go basis. This leads to the construction of different forms of data centers around the world, which usually consumes huge amount of energy and produce pollution (heat and carbon dioxide footprint). Many efforts have focused on managing cloud resources to decrease their energy consumption. Since servers are the highest energy consumers in data centers, most researchers have focused on optimizing their energy consumption while other resources such as network devices and cooling systems have received less attention. In this paper, we propose a network‐aware virtual machine (VM) relocation algorithm, which optimizes the distribution of the VMs running on the data center in order to conserve energy in both servers and network devices (switches). The proposed technique targets the communicated VMs and places them closer to each other. This would reduce the traffic overhead between any communicating VMs so that fewer network devices are involved in the communication process. This also increases the VMs consolidation to some servers while leaving other servers idle. Switching off the idle servers and network devices would significantly reduce the total energy consumed by the data center. The proposed algorithm is evaluated using simulation and the performance of the proposed algorithm is compared against different previously proposed algorithms in the literature. Performance metrics used are the energy consumed by the host and by the network switches, the service level agreement violations, and the number of VM migrations. The impact of different packet sizes, the efficiency of switching the network elements to sleep mode, and the impact of resource homogeneity and heterogeneity are also studied and discussed. Simulation results show that the proposed algorithm outperforms the existing algorithms in terms of all evaluation metrics. [ABSTRACT FROM AUTHOR]

Optical links in datacentres operate mainly in burst mode. The utilisation might be as low as 10%, i.e. most of the time the link is idle, but still consumes power. It would therefore be desirable to put these links into a low-power sleep mode during idle signal by reducing the bias, and to operate the electronics and laser only when data is actually being transmitted. The laser is typically implemented with vertical cavity surface-emitting lasers (VCSELs). VCSELs should not be biased below their threshold current as otherwise an extensive turnon delay will be incurred. A significant wake-up time is still observed in VCSELs even if the bias is not reduced during idle and only logic zeros are transmitted. With reduced bias, an unacceptable wake-up time is observed. The authors present measured data and propose a high-amplitude wake-up pulse at the beginning of the data burst, to significantly speed up the wake-up time. Measurements show the effectiveness of this pulse. [ABSTRACT FROM AUTHOR]

Summary: Energy consumption in Cloud and High Performance Computing platforms is a significant issue and affects aspects such as the cost of energy and the cooling of the data center. Host level monitoring and prediction provides the groundwork for improving energy efficiency through the placement of workloads. Monitoring must be fast and efficient without unnecessary overhead, to enable scalability. This precludes the use of Watt meters attached per host, requiring alternative approaches such as integrated measurements and models. IPMI and RAPL are subject to error and partial measurement, which may be mitigated. Models allow for prediction and more responsive measures of power consumption, but require calibrating. The causes of calibration error are discussed, along with mitigation strategies, without overly complicating the underlying model. An outcome is a Watt meter emulator that provides hosts level power measurement along with estimated power consumption for a given workload, with an average error of 0.20W. [ABSTRACT FROM AUTHOR]

Summary: Many of today's important applications of our everyday lives, eg, weather forecast, design of plane and car shapes, medical analysis, or search engine queries depend on massively parallel computer programs executed in data centers. A large amount of energy is used to power them, and it is of primary importance to compute more efficiently to sustain the increasing demand of computing power while keeping energy consumption reasonable. One promising research path in this domain is heterogeneous systems since specific computing resources (processors, accelerators, etc) are more adapted to efficiently execute parts of applications. Nevertheless, the exploitation of these platforms raises new challenges in terms of application management optimization. The aim of our work is to determine effective algorithms to exploit these heterogeneous platforms by finding appropriate application mapping and scheduling to optimize the execution time and energy consumption with respect to various constraints. To achieve this goal, there is a need of a detailed modeling of the applications and the underlying hardware to be able to find realistic solutions. In this paper, we propose such a model, provide two implementations with state‐of‐the‐art tools, and propose a fast greedy online resolution algorithm and preliminary mapping and scheduling numerical results. [ABSTRACT FROM AUTHOR]