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

With the 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]

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]

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]

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]

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

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

The dynamic nature of the cloud environment increases the complexity of managing its resources and the distribution of user workload between the available containers in the data center. However, the workload must be balanced to improve the cloud system's overall performance. Generally, most of the existing load balancing techniques suffer from performance degradation due to the communication overheads among the containers. Moreover, less attention is given to stabilize the load in a multi-cloud environment. Therefore, to overcome this problem, there is a need to develop an elastic load balancing method to improve the performance of cloud systems. This paper proposed an autonomic CSO-ILB load balancer to ensure the elasticity of the cloud system and balance the user workload among the available containers in a multi-cloud environment. The concept of multi-loop has been utilized in our approach to enabling efficient self-management before load balancing. The tasks are scheduled to the containers using an extended scheduling algorithm called Deadline-Constrained Make-span Minimization for Multi-Task Scheduling (DCMM-MTS). Based on the task scheduling, the load in each container is computed and then balanced using the proposed load balancer algorithm CSO-ILB. The proposed approach is evaluated in the Container CloudSim platform, and the performance is compared with the existing meta-heuristic algorithms such as Ant Colony Optimization, Bee Colony Optimization, Shuffled Frog Leaping Algorithm and Cat Swarm Optimization (CSO). The simulations proved that the proposed approach outperformed the other approaches in terms of reliability, CPU utilization, make-span, energy utilization, response time, execution cost, idle time, and task migration. [ABSTRACT FROM AUTHOR]

Thermal-aware (TA) task allocation is one of the most effective software-based dynamic thermal management techniques to minimize energy consumption in data centers (DCs). Compared to its counterparts, TA scheduling attains significant gains in energy consumption. However, the existing literature overlooks the heterogeneity of computing elements in terms of thermal constraints while allocating or migrating user jobs, which may significantly affect the reliability of racks and all the equipment therein. Moreover, the workload distribution among these racks/servers is not fair and efficient in terms of thermal footprints; it is potentially beneficial to determine the workload proportion for each computing node (rack/server) based on its marginal contribution in disturbing the thermal uniformity (TU) in a DC environment. To solve the said problems, we model the workload distribution in DCs as a coalition formation game with the Shapley Value (SV) solution concept. Also, we devise Shapley Workload (SW), a TA scheduling scheme based on the SV to optimize the TU and minimize the cooling cost of DCs. Specifically, the scheduling decisions are based on the ambient effect of the neighboring nodes, for the ambient temperature is affected by the following two factors: (1) the current temperature of computing components and (2) the physical organization of computing elements. This results in lower temperature values and better TU, consequently leading to lower cooling costs. Simulation results demonstrate that the proposed strategy greatly reduces the total energy consumption compared to the existing state-of-the-art. [ABSTRACT FROM AUTHOR]

In cloud computing, virtual machine placement (VMP) is an important process that identifies the most appropriate physical machine to host the virtual machines (VMs). Nevertheless, determining how to place VMs within the data center to provide high availability and good performance is a difficult challenge for cloud providers. In this paper, with the goal of optimizing the availability and the energy consumption of the cloud data center, an improved genetic algorithm (I-GA) is proposed to solve VMP problem. This new algorithm presents a virtual hierarchy architecture model to combine with the genetic algorithm. The model is able to achieve a near-optimal solution in resolving the availability and energy consumption concerns by innovating the initial population generation step of the I-GA. Finite element analysis is applied as background and CloudSim is used as the experiment simulation. The simulated results demonstrate the significant improvement of the data center's energy efficiency and the successful maintenance of its high availability. The results are also highly competitive compared to the benchmark results of other VMP algorithms. [ABSTRACT FROM AUTHOR]

As an important part of the new infrastructure, the cloud data center is developing rapidly, and its energy consumption problem is becoming more and more prominent. Therefore, research on energy-saving technologies for cloud data centers has attracted widespread attention from academia and industry. Some studies have reviewed energy-saving optimization methods and technologies for data centers, but recently, many state-of-the-art optimization methods of energy consumption and energy-saving technologies have sprung out, which are still worth analyzing and discussing. Depending on the in-depth investigation and analysis of related research status, this article firstly focuses on analyzing and discussing the energy-saving technologies of the two components: IT equipment and cooling systems, both of which bring about the largest energy consumption in cloud data centers. As for IT equipment, its energy-saving technologies mainly include the energy saving of servers, storage systems, and network systems. While as for cooling systems, airflow organization in the computer room, thermal-aware scheduling technology, and other new energy-saving technologies are involved. Secondly, on the basis of analyzing the energy-saving technologies of the two major components, a new optimization scheme of energy consumption for the jointing computing system and cooling system is explained. Throughout this work, various energy-saving strategies and technologies have been described and compared. Finally, the future trends and development directions of energy saving for data centers are further promoted, such as integral optimization of energy consumption jointing multiple components, energy saving using artificial intelligence methods, energy saving based on novel hardware equipment, hybrid cooling energy saving, and comprehensive energy conservation with various energy technologies. [ABSTRACT FROM AUTHOR]

The demand of cloud-based services is growing rapidly due to the high scalability and cost-effective nature of cloud infrastructure. As a result, the size of the data center is increasing drastically, so is the cost of maintenance in terms of resource management and energy consumption. Hence, it is important to develop a proper resource management plan to maximize the profit by reducing the overhead of operational cost. In this paper, we propose a multi-step-ahead workload prediction approach using Machine learning techniques and allocate the resources based on this prediction in a way that allows the resources to be utilized more efficiently and thereby, reducing the data center's overall energy consumption. We evaluate the effectiveness of our framework based on real workload trace of Bitbrains. Experimental results show that our framework outperforms other state-of-the-art approaches for predicting workload over a long-run and significantly improves resource utilization while enabling substantial energy savings. [ABSTRACT FROM AUTHOR]

Energy optimization has become an emerging concern for cloud service providers. Existing methods focus on reducing the energy consumption of either server inside the data center or data transmission between data centers. Moreover, most of the works are based on assumptions that servers and workloads are homogeneous. This is not in accordance with the fact that modern data centers are built from various classes of servers. In this paper, we consider the joint energy optimization of intra- and inter-data center in both homo- and heterogeneous cases. We first propose an optimization model to minimize the joint energy cost of servers and network sides. To tackle the time-coupling constraint of carbon emission, we apply the Lyapunov optimization framework to transform the original problem into a well-studied queue stability problem. For better scalability of time complexity, we derive a distributed solution by using generalized benders decomposition. Then, we extend the model to deal with the situation where requests and data centers are heterogeneous as data centers are typically built from servers with different specifications. To better deal with the dynamic of the network (e.g., the occurrence of faults), we leverage a deep Q-network (DQN) and propose a fault-tolerant DQN-based solution. Finally, the simulation results show the high efficiency of our proposal in cost-saving and performance-enhancing. [ABSTRACT FROM AUTHOR]

Cloud computing adopts virtualization technology, including migration and consolidation of virtual machines, to overcome resource utilization problems and minimize energy consumption. Most of the approaches have focused on minimizing the number of physical machines and rarely have devoted attention to minimizing the number of migrations. They also decide based on the current resources utilization without considering the demand for resources in the future. Some approaches minimize the number of active physical machines and Service Level Agreement (SLA) violations with the number of unnecessary migrations. They consider the current resource utilization of physical machines and neglect from demands for future resource requirements. As a result, as time passes, the number of unnecessary migrations, and subsequently, the rate of SLA violations in data centers increases. Alternatively, several approaches only focus on a hardware level and reduce the physical machine's dynamic power consumption. The lack of control over the overload of physical machines increases the amount of violation. In this paper, a framework called PCVM.ARIMA is presented that focuses on the dynamic consolidation of virtual machines over the minimum number of physical machines, minimize the number of unnecessary migrations, detect the physical machine overloading, and SLA based on the ARIMA prediction model. Moreover, the Dynamic Voltage and Frequency Scaling (DVFS) technique is used to apply the optimal frequency to heterogeneous physical machines. The experimental results show that the presented framework significantly reduces energy consumption while it improves the QoS factors in comparison to some baseline methods. [ABSTRACT FROM AUTHOR]

As the infrastructure of high-performance computing, the data center network plays an important role. As network failures occur frequently, data center networks demand highly performed, robust, and energy-efficient failure recovery mechanisms. Despite process, the existing work still has a huge scope to improve to satisfy these requirements. The backup-based failure recovery schemes reserve backup paths in advance, which results in a large energy consumption under normal network conditions. In order to solve the energy consumption problem, the existing adaptive failure recovery schemes are proposed to calculate the rerouting path of the traffic on the failed link, which reduces the energy consumption. However, most adaptive fault recovery solutions apply multi-path routing to calculate the re-routing path. As multi-path routing cannot detect the congestion status of the path under the asymmetric topology caused by link failures, the network is congested, which ends up in less robustness of the network. In view of this, we design and evaluate AFRM, a novel adaptive failure recovery mechanism that overcomes these challenges. AFRM uses asymmetrical routing to calculate the re-routing path by being congestion-aware and is more robust to topological asymmetries compared with existing schemes. The asymmetrical routing dynamically schedules flows to the path with the least marginal cost, which makes AFRM much more energy-efficient. Additionally, AFRM achieves fast link failure detection based on hash storage and flow table matching. Evaluations show that AFRM can do the trade-off between failure recovery time and energy consumption, reduce flow completion time, and increase network throughput compared with existing schemes. [ABSTRACT FROM AUTHOR]

The large-scale virtualized Cloud data centers consume huge amount of electrical energy leading to high operational costs and emission of greenhouse gases. Virtual machine (VM) consolidation has been found to be a promising approach to improve resource utilization and reduce energy consumption of the data center. However, aggressive consolidation of VMs tends to increase the number of VM migrations and leads to over-utilization of hosts. This in turn affects the quality of service (QoS) of the applications running in the VMs. Thus, reduction in energy consumption and at the same time ensuring proper QoS to the Cloud users are one of the major challenges among the researchers. In this paper, we have proposed an energy efficient and QoS-aware VM consolidation technique in order to address this problem. We have used Markov chain-based prediction approach to identify the over-utilized and under-utilized hosts in the data center. We have also proposed an efficient VM selection and placement policy based on linear weighted sum approach to migrate the VMs from over-utilized and under-utilized hosts considering both energy and QoS. Extensive simulations using real-world traces and comparison with state-of-art strategies show that our VM consolidation approach substantially reduces energy consumption within a data center while delivering suitable QoS. [ABSTRACT FROM AUTHOR]

In recent years, data centers have deployed many rapidly growing Internet-scale services. As the size of the data center continues to expand, it not only causes huge energy consumption, but also brings environmental problems such as huge carbon emissions. However, for an Internet-scale data center, it is a challenge to optimize power consumption while meeting the growing demands of Internet-scale applications. Moreover, current data centers usually have heterogeneous servers (with different power consumption and computing capacity), which causes the dynamic task placement problem more complicated. In this work, we first observe two kinds of heterogeneity from the analysis of a public trace that was collected from a Google data center. Considering the demands in energy consumption and performance, we model the dynamic task placement problem in a heterogeneous Internet-scale data center and propose a heuristic cost-aware algorithm to solve it. By simulating and comparing with the other two scheduling algorithms, our proposed heuristic algorithm can gain a well energy saving and keep application performance within acceptable limits. [ABSTRACT FROM AUTHOR]

The technology, cloud computing, in present days, is vastly used due to the services it provides and the ease with which they can be availed. The enormous development of the Internet technology is due to the advent of the concept of cloud. Along with its benefits, cloud computing brings along itself a detrimental side effect, i.e., carbon emission. This is due to the massive energy consumption in the cloud data centers. Reduction in energy consumption in cloud is thus one of the major challenges among the researchers. This work conducts a thorough study of the various techniques that help in minimization of energy consumption in data centers. It also explores and proposes approaches to reduce the same, eventually making the environment greener. In the proposed work, prediction mechanism has been adopted and implemented on the existing Minimization of Migration (MM) policy for large history data set, followed by dynamic thresholding mechanism in place of static thresholds. Rigorous simulations have been conducted, and the results show reduction in cloud data center energy consumption. [ABSTRACT FROM AUTHOR]

Heterogeneous computing that exploits simultaneous co-processing with different device types has been shown to be effective at both increasing performance and reducing energy consumption. In this paper, we extend a scheduling framework encapsulated in a high-level C++ template and previously developed for heterogeneous chips comprising CPU and GPU cores, to new high-performance platforms for the data center, which include a cache coherent FPGA fabric and many-core CPU resources. Our goal is to evaluate the suitability of our framework with these new FPGA-based platforms, identifying performance benefits and limitations.We target the state-of-the-art HARP processor that includes 14 high-end Xeon classes tightly coupled to a FPGA device located in the same package. We select eight benchmarks from the high-performance computing domain that have been ported and optimized for this heterogeneous platform. The results show that a dynamic and adaptive scheduler that exploits simultaneous processing among the devices can improve performance up to a factor of 8 × compared to the best alternative solutions that only use the CPU cores or the FPGA fabric. Moreover, our proposal achieves up to 15% and 37% of improvement compared to the best heterogeneous solutions found with a dynamic and static schedulers, respectively. [ABSTRACT FROM AUTHOR]

A cloud data center consumes more energy for computation and switching servers between modes. Virtual Machine (VM) migration enhances the execution of cloud server farm in terms of energy proficiency, adaptation to internal failure, and accessibility. Cloud suppliers, be that as it may, ought to likewise enhance for amounts like energy consumption and administrations costs and in this manner, trying to have all the Virtual Machines with the least measures of physical equipment machines conceivable. The part of virtualization is critical and its execution is subjected to VM migration and machine allotment. A greater amount of the energy is caught up in the cloud; consequently, the use of various calculations is required for sparing energy and productivity upgradation in the proposed work. In the proposed work, the Naive Bayes classifier with hybrid optimization using Artificial Bee Colony–Bat Algorithm (ABC–BA) was implemented to reduce the energy consumption in VM migration. The proposed method was evaluated in CloudSim and the performances were compared using performance index such as success &failure rate, and energy consumption. It is observed from the implementation results that the proposed method reduces energy consumption compared to other existing methods. From the implementation outcomes of the proposed work, it was understood that the model was able to achieve the minimum energy consumption and failure rate i.e., 1000–1200 kWh, 0.2 with maximum success rate and accuracy of 1 and 97.77%. [ABSTRACT FROM AUTHOR]

With the development of information technology, there is a need for computational works everywhere and every time. Thus, people should be able to carry out their heavy computations without having the burden of purchasing expensive hardware and software. Cloud computing is an attractive solution to such needs, but the high energy consumption of physical machines in a Cloud data center is a matter of great concern. Therefore, some of the low-loaded machines can be turned off or switched into low energy mode using server consolidation approaches. In this paper, a Stochastic Process-Based Dynamic Server Consolidation (SB-DSC) policy is developed to reduce the total cost of data centers while satisfying the required quality of service. A novel algorithm, which we call it Stochastic Process-Based BFD (SBBFD), is employed in SB-DSC policy to perform virtual machine placements over time. SBBFD overcomes most drawbacks of other algorithms proposed in the literature. The simulation results on real workload data show that SB-DSC leads to a noticeable reduction in total cost in terms of power consumption, SLA violations, number of mode switching and number of migrations. [ABSTRACT FROM AUTHOR]

With the development of cloud computing, energy consumption has become a major and costly problem in data centers. To improve the energy efficiency of data centers, we analyze the influence factors of energy consumption and discover that reducing the idle servers can effectively cut down the energy consumption of data centers. Then the load demand forecasting algorithm using weighted random forests is proposed. And time factor matching coefficient obtained by considering the day type and the time span is employed to calculate the weights. To enhance the forecasting performance, an error correction strategy is also introduced into the forecasting model. The experimental results show that these strategies further improve the prediction accuracy, and the root-mean-square error is 2.6–4.1% lower than other forecasting algorithms. We finally design an adaptive scheduling technology that utilizes short-term prediction of load demand. This technology adaptively adjusts the scale of the data center cluster based on the forecast results. The simulation results indicate that the technology can reduce 12.5% energy consumption while ensuring the service quality. [ABSTRACT FROM AUTHOR]

High energy consumption in data center has serious impacts on environment. Energy-efficient scheduling under Customer Satisfaction Level (CSL) constraint has become a key problem of cloud computing in data centers. In this paper, a CSL-driven and energy-efficient resource scheduling (CDEERS) framework has been designed to optimize energy efficiency and CSL in cloud data centers. To achieve different goals in different CSL states, three scheduling strategies (aiming to minimize energy saving, maximize CSL and maximize CSL per energy, respectively) are auto-adaptively applied to consolidate resources according to CSL states. To identify CSL states, a metric based on Service Level Agreement (SLA) violation rate (MSVR) is designed and applied in the proposed method. Additionally, the Weighted Moving Average (WMA) model is applied to predict the future state of CSL and optimize the resource allocation. Simulation result shows that the MSVR improves energy efficiency (CSL per energy) by 51.1% and 30.4% compared with traditional metric based on workload (MW) and metric based on response time (MRT), respectively, during the whole test period. It reduces the SLA violation rate by 7.9% and the number of host overloads by 9.6%, respectively, while prediction mechanism is applied. It shows that CDEERS has better energy efficiency than these static scheduling methods with single objection in the condition of dynamic state of CSL. [ABSTRACT FROM AUTHOR]

Cloud computing provides online services and solutions for dynamic and on-demand resource provisioning. These resources consume high energy leading to higher operational expenditures and carbon footprints in data centers. There are several research works performed on energy efficiency of data centers, but mostly focus on energy consumption of a single factor, i.e., CPU, leaving the RAM neglected. Recently, the researchers have focused on the impact of RAM's energy consumption on the data centers. Studies show that RAM consumes 25% of a server's overall energy. In this paper, we propose two sets of schemes that consider the server capacity for virtual machine consolidation to reduce the overall energy cost. The proposed techniques are implemented in CloudSim, and the results are compared with state-of-the-art solutions. Our proposed techniques reduce energy consumption and maintain a service level agreement to satisfy the customer requirements with a minimum cost. [ABSTRACT FROM AUTHOR]

In virtualized data centers, consolidation of virtual machines (VMs) on minimizing the number of total physical machines (PMs) has been recognized as a very efficient approach. This paper considers the energy-efficient consolidation of VMs in a cloud datacenter. Concentrating on CPU-intensive applications, the objective is to schedule all requests non-preemptively, subjecting to constraints of PM capacities and running time interval spans, to make the total energy consumption of all PMs is minimized (called MinTE for abbreviation). The MinTE problem is NP-complete in general. We propose a self-adaptive approach called SAVE. The approach makes decisions of the assignment and migration of VMs by probabilistic processes and is based exclusively on local information. Both simulation and real environment test show that our proposed method SAVE can reduce energy consumption about 30 % against VMWare DRS and 10–20% against ecoCloud on average. Extensive experiments show that our method outperforms the existing method and achieves significant energy savings and high utilization. [ABSTRACT FROM AUTHOR]

Excessive consumption of energy in cloud data centers whose number is increasing day by day has led to substantial problems. Hence, offering efficient schemes for virtual machine (VM) placement to decrease energy consumption in cloud computing environments has become a significant research field in recent years. In this paper, with the goal of reducing energy consumption in cloud data centers, we present a VM placement method using the cultural algorithm. In the proposed algorithm called balance-based cultural algorithm for virtual machine placement (BCAVMP), a new fitness function is introduced to evaluate VM allocation solutions. In this function, by using the sum of balance vector lengths for each VM placement, balanced utilization of resources is considered. Also, by applying the amount of energy usage in the fitness function, solutions with lower energy consumption are intended. The performance of the proposed method is evaluated using CloudSim simulator. The simulation results indicate that by appropriate VM assignment and resource wastage reduction, energy consumption in cloud data centers can be decreased. [ABSTRACT FROM AUTHOR]

Cloud computing is the most prominent computing paradigm in the present era of information technology. However, data centers needed for hosting cloud services demand huge amount of electrical energy and release harmful gases to the atmosphere. To ensure a sustainable future, there is a need to focus on energy efficiency in cloud computing. Early literature pertaining to energy consumption in cloud computing is primarily focused on individual sub-domains like scheduling techniques, optimization, and green computing metrics. Research literature on cloud resource optimization is found to be the most discussed but less structured. This paper intends to provide a complete picture of energy efficiency in cloud computing. It also classifies heuristics-based optimization methods and the dynamic power management techniques. The survey shows the research trends based on regions, journals, conferences, etc., in the domain of energy efficiency in cloud computing. The study concludes with research issues and future research directions. [ABSTRACT FROM AUTHOR]

This survey is an up-to-date account of the research on the performance-energy trade-off in virtualized environments, specifically in virtual machine consolidation. The factors that influence the performance and energy in consolidated data centres and the performance-energy trade-off itself are analysed. Based on these factors, we propose a categorization that classifies the most important research on performance and energy in consolidated systems. We have analysed and summarized 91 selected research works from an initial set of 1030. This article summarizes all previous surveys on the subject of virtual machine consolidation and updates them with the most recent papers in the field. [ABSTRACT FROM AUTHOR]

The fast processing speeds of the current generation of supercomputers provide a great convenience to scientists dealing with extremely large data sets. The next generation of exascale supercomputers could provide accurate simulation results for the automobile industry, aerospace industry, and even nuclear fusion reactors for the very first time. However, the energy cost of super-computing is extremely high, with a total electricity bill of 9 million dollars per year. Thus, conserving energy and increasing the energy efficiency of supercomputers have become critical in recent years. Many researchers have studied this problem and are trying to conserve energy by incorporating the dynamic voltage frequency scaling technique into their methods. However, this approach is limited, especially when the workload is high. In this paper, we developed a power-aware job scheduler by applying a rule-based control method and taking into consideration real-world power and speedup profiles to improve power efficiency while adhering to predetermined power constraints. The intensive simulation results showed that our proposed method is able to achieve the maximum utilization of computing resources as compared to baseline scheduling algorithms while keeping the energy cost under the threshold. Moreover, by introducing a power performance factor based on the real-world power and speedup profiles, we are able to increase the power efficiency by up to 75%. [ABSTRACT FROM AUTHOR]

This paper focuses on energy consumption which is a major problem in the dark silicon era. As energy consumption becomes a key issue for operation and maintenance of cloud data centers, cloud computing providers are becoming significantly concerned. Here, we show how spin-transfer torque random access memory (STT-RAM) can be used as an on-chip L2 cache to obtain lower energy compared to conventional L2 caches, like SRAM. High density, fast read access and non-volatility make STT-RAM a significant technology for on-chip memories. Previous studies have mainly studied specific schemes based on common applications and do not provide a thorough analysis of emerging scale-out applications with multiple design options. Here, we discuss different outlooks consisting of performance and energy efficiency in cloud processors by running emerging scale-out workloads. Experiment results on the CloudSuite benchmarks show that the proposed method reduces energy by 51% (on average) and improves energy delay product by 37% (on average) where instruction per cycle degradation is only 22% (on average) compared to the SRAM method. [ABSTRACT FROM AUTHOR]

Data center network (DCN) is the core of cloud computing and accounts for 40% energy spend when compared to cooling system, power distribution and conversion of the whole data center (DC) facility. It is essential to reduce the energy consumption of DCN to ensure energy-efficient (green) data center can be achieved. An analysis of DC performance and efficiency emphasizing the effect of bandwidth provisioning and throughput on energy proportionality of two most common switch-centric DCN topologies: three-tier (3T) and fat tree (FT) based on the amount of actual energy that is turned into computing power are presented. Energy consumption of switch-centric DCNs by realistic simulations is analyzed using GreenCloud simulator. Power-related metrics were derived and adapted for the information technology equipment processes within the DCN. These metrics are acknowledged as subset of the major metrics of power usage effectiveness and data center infrastructure efficiency, known to DCs. This study suggests that although in overall FT consumes more energy, it spends less energy for transmission of a single bit of information, outperforming 3T. [ABSTRACT FROM AUTHOR]

Cloud-based data centers consume a significant amount of energy which is a costly procedure. Virtualization technology, which can be regarded as the first step in the cloud by offering benefits like the virtual machine and live migration, is trying to overcome this problem. Virtual machines host workload, and because of the variability of workload, virtual machines consolidation is an effective technique to minimize the total number of active servers and unnecessary migrations and consequently improves energy consumption. Effective virtual machine placement and migration techniques act as a key issue to optimize the consolidation process. In this paper, we present a novel virtual machine consolidation technique to achieve energy-QoS-temperature balance in the cloud data center. We simulated our proposed technique in CloudSim simulation. Results of evaluation certify that physical machine temperature, SLA, and migration technique together control the energy consumption and QoS in a cloud data center. [ABSTRACT FROM AUTHOR]

Significant savings in the energy consumption, without sacrificing service level agreement (SLA), are an excellent economic incentive for cloud providers. By applying efficient virtual Machine placement and consolidation algorithms, they are able to achieve these goals. In this paper, we propose a comprehensive technique for optimum energy consumption and SLA violation reduction. In the proposed approach, the issues of allocation and management of virtual machines are divided into smaller parts. In each part, new algorithms are proposed or existing algorithms have been improved. The proposed method performs all steps in distributed mode and acts in centralized mode only in the placement of virtual machines that require a global vision. For this purpose, the population-based or parallel simulated annealing (SA) algorithm is used in the Markov chain model for virtual machines placement policy. Simulation of algorithms in different scenarios in the CloudSim confirms better performance of the proposed comprehensive algorithm. [ABSTRACT FROM AUTHOR]

Increasing demand for acquiring diverse range of services has led to the establishment of huge energy hungry cloud data centers all around the world. Cloud providers face with major concerns to reduce their energy consumption while ensuring high quality of service based on the Service Level Agreement (SLA). Consolidation is proposed as one of the most effective techniques for online energy saving in cloud environments with dynamic workloads. This paper proposes novel proactive online resource management policies to optimize energy, SLA, and number of migrations in cloud data centers. More precisely, this paper proposes new prediction algorithm for determination of overloaded hosts as well as novel multi-criteria decision making techniques to select virtual machines. The results of simulations using CloudSim simulator shows up to 98.11 % reduction in the output metric which is representative of energy consumption, SLA violation, and number of migrations, in comparison with state of the art. [ABSTRACT FROM AUTHOR]

This work analyzes a new and very subtle kind of security threat that can affect large-scale cloud-based IT service infrastructures, by exploiting the computational resources of their component data center to waste as much energy as possible. The consequence of these threats ranges from increased costs in the energy bill, to penalization for exceeding the agreed quantity of greenhouse gases (GHG) emissions, up to complete denial of service caused by electrical outages due to power budget exhaustion. We analyzed different types of such attacks with their potential impacts on the energy consumption, modeled their behavior and quantified how current energy-proportional technologies may provide attackers with great opportunities for raising the target facility emissions and costs. These efforts resulted in a simple model with some parametric reference values that can be used to estimate the impact of such attacks also in presence of very large infrastructures containing thousands or millions of servers. [ABSTRACT FROM AUTHOR]

To reduce the construction cost of the power-supplying infrastructure in data centers and to increase the utilization of the existing one, many researchers have introduced software-based or hardware-based power-capping schemes. In servers with consolidated virtual machines, which can be easily found in cloud systems, exporting virtual machines to other light-loaded servers through live-migration is one of the key approaches to impose power-capping on servers. Up until now, most researchers who have tried to achieve power-capping through live-migration assumed that exporting a virtual machine instantly reduces the server power consumption. However, our analysis introduced in this paper reveals that the power consumption remains high or increases for a few seconds during a migration instance. This behavior contradicts the aim of power-capping, and may endanger the stability of servers. Based on this observation, we also propose and evaluate two power-suppressing live-migration schemes to resolve the power overshooting issue. Our evaluation shows that both approaches immediately limit the power consumption after live-migration is initiated. [ABSTRACT FROM AUTHOR]

Thermal cameras provide fine-grained thermal information that enables monitoring and autonomic thermal management in large datacenters. The real-time thermal monitor network employing thermal cameras is proposed to cooperatively localize hotspots and extract their characteristics (i.e., temperature, size, and shape). These characteristics are adopted to classify the causes of hotspots and make energy-efficient thermal management decisions such as job migration. Specifically, a sculpturing algorithm for extracting and reconstructing shape characteristics of hotspots is proposed to minimize the network overhead. Experimental results show the validity of all the algorithms proposed in this paper. [ABSTRACT FROM AUTHOR]