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Virtualization technologies are extensively utilized in data centers, particularly cloud computing. This facilitates data center management and diminishes the number of physical machines (servers) and, subsequently, their cooling requirements, leading to cost, space, and power consumption reductions. When applications in data centers are executing independent parallel transactions, but with similar performance requirements, the appropriate level of virtual machine consolidation on a server poses a fundamental challenge for capacity planning. This article introduces a method to evaluate the performance speedup achieved through virtualization on any server and the effects of virtualization and consolidation overheads on physical or virtual machine scalability. This research formalizes the speedup and overheads, using classical computer architecture statements. but at the same time proposes a new method to analyze these overhead amounts and types, showing the scalability and efficiency of different consolidations in the same server and its comparison against no consolidation. This work also proposes a new way to determine the optimal number of physical servers and the optimal number of consolidated virtual machines for a given transaction workload. The real experimentation was performed with different workload sizes, types of virtualizations and different servers. The method presented also facilitates the representation of linear scalability against the real degree of parallelism of either physical machines or consolidated virtual machines for a given transaction workload, as well as striking the right balance between speedup and energy in virtual server consolidation. [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]

How to improve resource utilization of cloud data centers (CDCs) and ensure users' quality of service (QoS) through efficient virtual machine (VM) scheduling is an urgent problem. Especially when service reliability is taken into consideration, the problem becomes more challenging. However, existing related researches mostly ignore the influence of reliability factors, such as failures and recoveries of computing nodes (CNs), which cannot reflect the realistic situations of real-life CDCs. Therefore, this paper investigates the problem of fault tolerance-aware VM scheduling and formulates it as a multi-objective optimization model with multiple QoS constraints. The proposed model tries to minimize users' total expenditure and, at the same time, maximize the successful execution rate of their businesses. To solve the proposed optimization model, a greedy-based best fit decreasing (GBFD) algorithm is then developed. The GBFD algorithm adopts a cost efficiency factor whose definition is according to the characteristics of CNs, to select a suitable CN for each VM request. Finally, extensive experiments are conducted to verify the feasibility of the proposed models and algorithm based on both the real-world CDC cluster data sets and the simulation ones. The results show that, first, as expected, fault tolerance significantly influences the performance criteria of VM scheduling and second, in most cases, the developed algorithm can decrease users' expenditure, increase success rate for executing their business and improve their overall satisfactions. Specifically, under real-world CDC cluster scenario, GBFD algorithm can increase the overall satisfaction of all cloud users by 38.3%, 20.9% and 14.6%, respectively, compared with the other three ones. Thus, the developed algorithm can perform better under fault tolerance-aware cloud environments. [ABSTRACT FROM AUTHOR]

Resource optimization is becoming a prime factor in the progress of Internet-based technology, Cloud Computing. A resource management model is highly required in cloud data center paradigms to utilize available resources effectively. Bin-Packing problem is an applicable combinatorial optimization for Virtual Machine (VM) to Physical Machine (PM) allocation to minimize the required PMs. In this paper, we have proposed an efficient resource allocation and management algorithm in two phases. During the first phase, a Load Balanced Multi-Dimensional Bin-Packing (LBMBP) heuristic for Virtual Machine (VM) to Physical Machine (PMs or host) allocation is introduced, considering multidimensional resources: CPU, RAM, and Network Bandwidth. In the Second Phase, to perform VM migration, a mechanism to detect overloaded and underloaded hosts based on outliers has been described. The proposed work illustrated the simulation results using CloudSim Plus Simulator and observed a reduction in the number of active PMs. Energy consumption and the number of migrations with improved resource utilization. [ABSTRACT FROM AUTHOR]

Growing demand for reduced local hardware infrastructure is driving the adoption of Cloud Computing. In the Infrastructure-as-a-Service model, service providers offer virtualized computational resources in the form of virtual machine instances. The existence of a large variety of providers and instances makes the decision-making process a difficult task for users, especially as factors such as the datacenter location - where the virtual machine is hosted - have a direct influence on the price of instances. The same instance may present price differences when hosted in different geographically distributed datacenters and, because of that, the datacenter location needs to be taken into account through the decision-making process. Given this problem, we propose the D-AHP, a methodology to aid decision-making based on Pareto Dominance and Analytic Hierarchy Process (AHP). In the D-AHP, the dominance concept is applied to reduce the number of instances to be compared; the instances selection is based on a set of objectives, while AHP ranks the selected ones from a set of criteria and sub-criteria, among them the datacenter location. The results from case studies show that differences may arise in the results, regarding which instance is more suitable for the user, when considering the datacenter location as a criterion to choose an instance. This fact highlights the need to consider this factor during the process of migrating applications to the Cloud. In addition, Pareto Dominance applied early over the set of total instances has proved to be efficient, once it significantly reduces the number of instances to be compared and ordered by the AHP by excluding instances with less computational resources and higher cost in the decision-making process, mainly for larger application workloads. [ABSTRACT FROM AUTHOR]

Dynamic virtual machine (VM) consolidation is a constructive technique to enhance resource usage and is extensively employed to minimize data centers' energy consumption. However, in the current approaches, consolidation techniques are heavily relied on reducing the actively used physical servers (PMs) based on their current resource utilization without considering future resource demands. Also, many of the reported works for cloud workload prediction applied univariate time series-based forecasting models and neglected the dependency of other resource utilization metrics. Thus, resulting in inaccurate predictions, unnecessary migrations, high migration costs, and increased service level agreement violations (SLAVs) may nullify the consolidation benefits. To efficiently address this issue, we propose a multivariate resource usage prediction-based hotspots and coldspots mitigation approach that considers both the current and future usage of resources with O(sk) time complexity, where s and k denote the number of PMs and VMs, respectively. The proposed technique uses a clustering-based stacked bidirectional (Long Short-Term Memory) LSTM deep learning network to predict the future memory and CPU usage of PMs and VMs with high accuracy and O ((Q (Q + W) ∗ Θ) computational complexity, where Q, W, and Θ represent the number of hidden layer cells, outputs, and training epochs, respectively. Through extensive simulations based on Google's cluster workload traces, we demonstrate that our proposed method obtains substantial improvements in terms of prediction performance, energy-efficiency, actively used PMs, VM migrations, and SLA violations over the benchmark approaches. [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]

With the rapid demand for service-oriented computing in association with the growth of cloud computing technologies, large-scale virtualized data centers have been established throughout the globe. These huge data centers consume power at a large scale that results in a high operational cost. The massive carbon footprint from the energy generators is another great issue to deal global warming. It is essential to lower the rate of carbon emission and energy consumption as much as possible. The live-migration-enabled dynamic virtual machine consolidation results in high energy saving. But it also incurs the violation of service level agreement (SLA). Excessive migration may lead to performance degradation and SLA violation. The process of VM selection for migration plays a vital role in the domain of energy-aware cloud computing. Using VM selection policies, VMs are selected for migration. A new power-aware VM selection policy has been proposed in this research that helps in VM selection for migration. The proposed power-aware VM selection policy has been further evaluated using trace-based simulation environment. [ABSTRACT FROM AUTHOR]

High-performance computing in a cloud environment may require massive data transfer among some of the virtual machines (VMs). These VMs are deployed in physical machines (hosts) of a data center. The data transfer among the communicating VMs may use the same shared communication links of the data center. Hence, it is important to have efficient bandwidth allocation policies for different data transfer requests (DTRs) which result in better utilization of bandwidth and fair allocation among the DTRs. In this paper, a few bandwidth allocation policies are proposed and their performances are analyzed. While designing these policies, the objective is the maximization of throughput and bandwidth utilization while minimizing the service time and turnaround time. Some of the policies are based on integer linear programming (ILP) which runs in exponential time while others are based on polynomial-time heuristics. Experimental results show that the performances of heuristic-based policies are comparable to those given by ILP-based exponential time policies. [ABSTRACT FROM AUTHOR]

"More data, more information." Big data helps businesses and research communities to gain insights and increase productivity. Many public cloud service providers offer Hadoop MapReduce as a service based on pay-per-use via infrastructure as a service on clusters of virtual machines promising on-demand horizontal scaling. These clusters of virtual machines are launched in various physical machines across racks in cloud data centers. Such multi-tenancy negatively introduces performance heterogeneity for Hadoop virtual machines due to hardware heterogeneity and interference from co-located virtual machine. Performance heterogeneity largely affects MapReduce job latency and resource utilization of rented Hadoop virtual clusters. Default MapReduce schedulers assign map/reduce tasks assuming the hardware is homogeneous. Interference-aware schedulers perform by only observing the interference pattern generated by co-located virtual machines. These schedulers do not consider the heterogeneous performance of virtual machines. Therefore, we propose a dynamic ranking-based MapReduce job scheduler that places the map and reduces tasks based on a virtual machine's performance rank to minimize job latency and improve resource utilization. Our proposed approach calculates the performance score for each virtual machine based on hardware heterogeneity and co-located virtual machine interference. Then, it ranks the virtual machines based on the map and reduce performance separately to place map and reduce tasks. To demonstrate our ideas, we have set a test bed with 29 virtual machines on eight physical machines with different configurations and capacities. We modify a default fair scheduler in Hadoop 2.x to incorporate our ideas and evaluate them with different workloads on the PUMA dataset. The proposed method is then compared against a default fair scheduler (resource-aware) and an interference-aware scheduler based on job latency and resource utilization. Finally, we argue in favor of our approach as it improves resource utilization by 30–65% and overall job latency by up to 30%. [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]

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]

Modern data center consists of thousands of servers, racks and switches. Complicated structure means it requires well-designed algorithms to utilize resources of data centers efficiently. Current virtual machine scheduling algorithms mainly focus on the initial allocation of virtual machines based on the CPU, memory and network bandwidth requirements. However, when tasks finished or lease expired, related virtual machines would be deleted from the system which would generate resource fragments. Such fragments lead to unbalanced resource utilization and decline of communication performance. This paper investigates the network influence on typical applications in data centers and proposed a self-adaptive network-aware virtual machine clustering and consolidation algorithm to maintain an optimal system-wide status. Our consolidation algorithm periodically checks whether consolidation is necessary and then clusters and consolidates virtual machines to lower communication cost with an online heuristic. We used two benchmarks in a real environment to examine network influence on different tasks. To evaluate the advantages of the proposed algorithm, we also built a cloud computing testbed. Real workload trace-driven simulations and testbed-based experiments showed that, our algorithm greatly shortened the average finish time of map-reduce tasks and reduced time delay of web applications. Simulation results showed that our algorithm considerably reduced the amount of high-delay jobs, lowered the average traffic passed through aggregate switches and improved the communication ability among virtual machines. [ABSTRACT FROM AUTHOR]

Virtual machine (VM) migration is a process of migrating VMs from one physical server to another. It provides several benefits to a data center in a variety of scenarios including improved performance, fault tolerance, manageability load balancing and power management. However, VM’s migration leads to performance degradation and service-level agreement (SLA) violations which cannot be ignored, particularly if critical business goals are to be met. In this paper, we propose an algorithm for VM’s placement and migration that considers different users quality of service requirement, in order to decrease energy consumption and SLA violations due to under utilization of data centers. The proposed work mainly focuses on a novel heuristics-based energy-aware resource allocation to allocate the user’s tasks in the form of cloudlets to the cloud resources that consumes minimal energy. In addition to that, it is incorporated with load balancing and constraint-based scheduling mechanism. The proposed work is implemented using the service-oriented-based architecture, and the same has been simulated using the CloudSim toolkit. In this paper, we compared our work with non-power-aware (NPA), dynamic voltage and frequency scaling (DVFS), single-threshold (ST) policies and minimization migration policy (MMP). The experiment results indicate that our approach saves about 83% of power comparing to the NPA system and 77% comparing to a system that apply only DVFS. However, if we compare these algorithms, which allow dynamic consolidation of VMs such as ST, it saves 53%, and finally, if we compare to MMP, it saves power between 22 and 38%. Similarly if we compare number of VM migration comparing to ST, it reduces 23 and 73% compared to MMP polices. [ABSTRACT FROM AUTHOR]

Cloud-based content delivery networks (CCDNs) have been developed as the next generation of content delivery networks (CDNs). In CCDNs, the cloud contributes to the cost-effective, pay-as-you-go model, and virtualization and the traditional CDNs contribute to content replications. Delivering infrastructure as a service in a networked cloud computing environment requires mapping virtual resources to physical resources, as well as traditional surrogate placement. In this paper, we develop a novel algorithm for virtual surrogate placement that combines multiple knapsack and competitive facility location problems. Moreover, we provide new formulations and theories for this problem. Finally, we compare our algorithm with the previous heuristics. Simulation results show that the proposed algorithm achieves significantly better results in terms of a decreased number of surrogate servers, decreased total path length between end users and surrogate servers, decreased average workload variance and CCDN deployment cost. [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]

Geo-distributed data-centers in cloud computing are becoming increasingly popular due to their lower end-user perceived latency and increased reliability in distributed applications. The important challenge of resource allocation in cloud management is more pronounced in geo-distributed data-centers compared to traditional data-centers. A geo-distributed cloud manager faces applications whose virtual machines (VMs) are far apart and need to interact with end users, access distributed data and communicate with each other. In such applications, the service level agreement is not met if the communication latency is not bounded. In this paper, we focus on the problem of finding data-centers for hosting VMs while the requested VMs are located in different geo-distributed data-centers and are sensitive to the communication latency. We propose an algorithm to minimize communication latency by taking into account the cloud network topology to be either a tree or that of the Internet. Moreover, our algorithm can utilize user's locations to find better candidate solutions. In the case of tree topology, we prove that our algorithm finds a solution whose latency is minimum. In addition, we show that our algorithm performs well in the Internet topology, with simulation results indicating that it can reduce the communication latency up to $$92~\%$$ compared to existing algorithms. [ABSTRACT FROM AUTHOR]

The ongoing trends in cloud computing demonstrate increasing need for efficient, yet economical data centers. Thus, recently the research community has focused its efforts on frameworks for optimised usage of the available resources that will result with energy-efficient and highly effective data centers. Toward this goal, in this paper we present a community-based framework for virtual machine placement inside a cloud data center. The framework is based on the complex network structural property of grouping tightly coupled nodes, and a matching process that maps virtual to physical communities while employing different optimisation functions on different hierarchy levels. The presented simulation results of the framework application reflect its high usage potential achieved by improvement in communication efficiency and reduced power consumption compared to the traditional heuristics. [ABSTRACT FROM AUTHOR]

Virtualization efficiently manages the ever-increasing demand for storage, computing, and networking resources in large-scale Cloud Data Centers. Virtualization attains multifarious resource management objectives including proactive server maintenance, load balancing, pervasive service availability, power management, and fault tolerance by virtual machine (VM) migration. VM migration is a resource-intensive operation as it constantly requires adequate CPU cycles, memory capacity, system cache, and network bandwidth. Consequently, it adversely affects the performance of running applications and cannot be entirely overlooked in contemporary data centers, particularly when user SLA and critical business goals are to be met. The unavailability of a comprehensive survey on VM migration schemes that covers various VM migration aspects such as migration patterns, sequence, application performance, bandwidth optimization, and migration granularity has motivated this review of existing schemes. This paper reviews state-of-the-art live and non-live VM migration schemes. Through an extensive literature review, a detailed thematic taxonomy is proposed for the categorization of VM migration schemes. Critical aspects and related features of current VM migration schemes are inspected through detailed qualitative investigation. We extract significant parameters from existing literature to discuss the commonalities and variances among VM migration schemes. Finally, open research issues and challenges with VM migration that require further consideration to develop optimal VM migration schemes in Cloud Data Centers are briefly addressed. [ABSTRACT FROM AUTHOR]

Virtualization is growing in different areas: from powerful servers in data centers to students' laptops and even cell phones. It can provide a more efficient use of hardware resources. Virtualization enables multiple virtual machines to run side-by-side in an isolated environment on a physical hardware. Modern processors are enhanced with technologies like Intel-VT and AMD-V that speed up virtual machines. However, there is still room for improving support of hardware for virtualization workloads. Gem5 is an open-source full system simulator capable of simulating a Chip-Multiprocessor with its caches, interconnection network, memory controllers among others. In its current state, gem5 does not support virtualized workloads. In this paper, we present a modified version of gem5, named gem5v, that simulates the behavior of a virtualization layer and can simulate virtual machines. We test this simulator in different scenarios using Parsec, Splash, MapReduce (Phoenix), SPEC and EEMBC benchmarks and compare its measured runtime with real systems. Results show 1-9 % difference between the simulated system and two virtualization softwares on a real hardware, namely KVM and VMware ESX. The comparison of vCPU overhead in VMware ESX and gem5v shows between 0.1 and 9 % difference. [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]