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The article introduces an innovative framework called Kororā, which aims to enhance the security and integrity of live virtual machine migration in a public cloud computing environment. The framework incorporates a trusted platform module to ensure the integrity of the migration process. It offers a new approach for virtual machine migration and has been specifically designed and implemented on a public infrastructure-as-a-service cloud platform.The primary research problem identified is the vulnerability of virtual machine instances to attacks during the live migration procedure. The evaluation used involves running the framework simultaneously on the same hardware components (such as I/O, CPU, and memory) and utilizing the same hypervisor's platform (Xen's open-source hypervisor). In addition, the security aspect of live migration is a crucial consideration due to the possibility of security threats across different area: data plane, control plane, and migration plane. Potential attackers may employ both passive and active attack techniques, putting the live migration at risk and resulting in a decline in performance. This poses a significant and alarming risk to the overall platform.To address the research gap, the Kororā framework emerged as a successful approach for achieving control-flow integrity by incorporating the Clark-Wilson security model proved effective in bridging the research gaps while maintaining system integrity. The primary achievement of this research is the introduction of the Kororā framework, which consists of seven agents operating within the Xen-privileged dom0 and establishing communication with the hypervisor. Overall, the finding indicate that the suggested framework offers an effective defence mechanism for moving a virtual machine from one host to another host with minimal disruption to normal operation with enhanced integrity.

Checkpoint/Restore techniques had been thoroughly used by the High Performance Computing (HPC) community in the context of failure recovery. Given the current trend in HPC to use containerization to obtain fast, customized, portable, flexible, and reproducible deployments of their workloads, as well as efficient and reliable sharing and management of HPC Cloud infrastructures, there is a need to integrate Checkpoint/Restore with containerization in such a way that the freeze time of the application is minimal and live migrations are practicable. Whereas current Checkpoint/Restore tools (such as CRIU) support several options to accomplish this, most of them are rarely exploited in HPC Clouds and, consequently, their potential impact on the performance is barely known. Therefore, this paper explores the use of CRIU's advanced features to implement diskless, iterative (pre-copy and post-copy) migrations of containers with external network namespaces and established TCP connections, so that memory-intensive and connection-persistent HPC applications can live-migrate. Our extensive experiments to characterize the performance impact of those features demonstrate that properly-configured live migrations incur low application downtime and memory/disk usage and are indeed feasible in containerized HPC Clouds. • We carry out fully-featured seamless live migrations of runC containers. • We minimize the downtime and disk utilization through diskless, iterative migrations. • We migrate containers with established TCP connections transparently to the clients. • We characterize the facets of live migrations in representative HPC benchmarks. [ABSTRACT FROM AUTHOR]

Live migration is an essential feature in virtual infrastructure and cloud computing datacenters. Using live migration, virtual machines can be online migrated from a physical machine to another with negligible service interruption. Load balance, power saving, dynamic resource allocation, and high availability algorithms in virtual data-centers and cloud computing environments are dependent on live migration. Live migration process has six phases that result in live migration cost. Several papers analyze and model live migration costs for different hypervisors, different kinds of workloads and different models of analysis. In addition, there are also many other papers that provide prediction techniques for live migration costs. It is a challenge for the reader to organize, classify, and compare live migration overhead research papers due to the broad focus of the papers in this domain. In this survey paper, we classify, analyze, and compare different papers that cover pre-copy live migration cost analysis and prediction from different angels to show the contributions and the drawbacks of each study. Papers classification helps the readers to get different studies details about a specific live migration cost parameter. The classification of the paper considers the papers' research focus, methodology, the hypervisors, and the cost parameters. Papers analysis helps the readers to know which model can be used for which hypervisor and to know the techniques used for live migration cost analysis and prediction. Papers comparison shows the contributions, drawbacks, and the modeling differences by each paper in a table format that simplifies the comparison. Virtualized Data-center and cloud computing clusters admins can also make use of this paper to know which live migration cost prediction model can fit for their environments. [ABSTRACT FROM AUTHOR]

Efforts were exerted to enhance the live virtual machines (VMs) migration, including performance improvements of the live migration of services to the cloud. The VMs empower the cloud users to store relevant data and resources. However, the utilization of servers has increased significantly because of the virtualization of computer systems, leading to a rise in power consumption and storage requirements by data centers, and thereby the running costs. Data center migration technologies are used to reduce risk, minimize downtime, and streamline and accelerate the data center move process. Indeed, several parameters, such as non-network overheads and downtime adjustment, may impact the live migration time and server downtime to a large extent. By virtualizing the network resources, the infrastructure as a service (IaaS) can be used dynamically to allocate the bandwidth to services and monitor the network flow routing. Due to the large amount of filthy retransmission, existing live migration systems still suffer from extensive downtime and significant performance degradation in cross-data-center situations. This study aims to minimize the energy consumption by restricting the VMs migration and switching off the guests depending on a threshold, thereby boosting the residual network bandwidth in the data center with a minimal breach of the service level agreement (SLA). In this research, we analyzed and evaluated the findings observed through simulating different parameters, like availability, downtime, and outage of VMs in data center processes. This new paradigm is composed of two forms of detection strategies in the live migration approach from the source host to the destination source machine. [ABSTRACT FROM AUTHOR]

Soon after the proliferation of User-Equipments (UEs) that work with cloud computing, there was an explosion in the volume of generated data. Furthermore, emerging IoT applications, e.g. virtual reality, augmented reality, video streaming, intelligent transportation, and healthcare, require low latency and real-time data, processing and communication. Fog/Edge computing is a novel criterion in which distributed fog/edge nodes provide resources in close proximity to end devices due to their limited resources. Instead of sending large amounts of data to cloud servers, fog/edge nodes could use local resources to analyze, filter, and process data. Mobile Edge Computing (MEC) can significantly reduce processing delays and network traffic between UEs and servers, especially when user mobility is taken into account. This paper proposed a new approach to migration in fog/edge computing to reduce downtime. Our simulations demonstrate less downtime, packet loss, migration time, and latency compared to current techniques. [ABSTRACT FROM AUTHOR]

Cloud service providers employ virtualization technologies in order to deliver energy efficiency and optimize resource utilization in cloud datacenters. In this way, Virtual Machine (VM) consolidation is an efficient technique for reducing energy related costs and environmental concerns. This technique consists of placing as much as possible VMs on as less as possible Physical Machines (PM)s by means of live VM migration. However, VM consolidation approaches bring advantages, they cannot efficiently guarantee performance isolation of co-located VMs resulting in operational interference issues. Therefore, it is necessary to be aware of workload performance degradation issues during VMs consolidation in a datacenter. In this paper, we propose a novel joint profit and interference-aware scheduling scheme (PIAS) to efficiently consolidate VMs that host multi-tier application workloads in Infrastructure-as-a-Service (IaaS) cloud datacenter. The PIAS scheme considers profit and cost of power consumption, operational interference of VMs, resource utilization, and service level agreements (SLA)s as well as amount of transferred memory pages during live VM migration for consolidating VMs. In this regard, a stochastic dynamic programming is introduced to model the operational behavior of VM. Also, an optimization problem is introduced to maximize provider's profit and minimize the overall execution cost of application workloads. We simulate a series of experiments based on real workloads traces for different IaaS cloud datacenter configurations. The PIAS scheme on average provides better results than other competitors and the results demonstrate how our approach at least improves profit, energy efficiency, and service down time by 40%, 29%, and 35%, respectively. [ABSTRACT FROM AUTHOR]

Large-scale data centers have been widely used for cloud services, and the stability of various cloud services has received additional attention from users. Although service disruptions are not as catastrophic as they once were, their impact might be more extensive than before. These outages may trigger the migration of virtual machines (VMs) located in the failure node. However, the access time of each VM is random, unlike the accident time, which can be predicted. This means that traditional migration caused by service interruptions may result in a large number of unwanted migrations, regardless of the user’s downtime experience. Migration is an expensive process in terms of the resources needed as well as the degradation of application performance during migration. A balance between the recovery time of the service (to minimize the migration resulting from a given placement) and the downtime experience of the users (to minimize the impact of access interruptions) is needed. In this paper, we propose HMGOWM, a hybrid decision-making mechanism for automating the migration of VMs. Our proposed mechanism extends the original VM migration performance cost model, greatly reducing the downtime experience of the users. To achieve high performance and a good load balance, a multi-objective monitoring system for both VMs and physical machine nodes and an adaptive VM migration-scheduling scheme for the OpenStack cloud platform are proposed. Extensive experiment results indicate that the downtime experienced by users can be efficiently reduced and that the implementation of HMGOWM outperforms the original scheduling of the OpenStack cloud platform. [ABSTRACT FROM AUTHOR]

With the striking rise in penetration of Cloud Computing, energy consumption is considered as one of the key cost factors that need to be managed within cloud providers' infrastructures. Subsequently, recent approaches and strategies based on reactive and proactive methods have been developed for managing cloud computing resources, where the energy consumption and the operational costs areminimized. However, tomake better cost decisions in these strategies, the performance and energy awareness should be supported at both Physical Machine (PM) and Virtual Machine (VM) levels. Therefore, in this paper, a novel hybrid approach is proposed, which jointly considered the prediction of performance variation, energy consumption and cost of heterogeneous VMs. This approach aims to integrate auto-scaling with live migration as well as maintain the expected level of service performance, in which the power consumption and resource usage are utilized for estimating the VMs' total cost. Specifically, the service performance variation is handled by detecting the underloaded and overloaded PMs; thereby, the decision(s) is made in a cost-effectivemanner. Detailed testbed evaluation demonstrates that the proposed approach not only predicts theVMs workload and consumption of power but also estimates the overall cost of live migration and auto-scaling during service operation, with a high prediction accuracy on the basis of historical workload patterns. [ABSTRACT FROM AUTHOR]

In a cloud environment, Virtual Machines (VMs) consolidation and resource provisioning are used to address the issues of workload fluctuations. VM consolidation aims to move the VMs from one host to another in order to reduce the number of active hosts and save power. Whereas resource provisioning attempts to provide additional resource capacity to the VMs as needed in order to meet Quality of Service (QoS) requirements. However, these techniques have a set of limitations in terms of the additional costs related to migration and scaling time, and energy overhead that need further consideration. Therefore, this paper presents a comprehensive literature review on the subject of dynamic resource management (i.e., VMs consolidation and resource provisioning) in cloud computing environments, along with an overall discussion of the closely related works. The outcomes of this research can be used to enhance the development of predictive resource management techniques, by considering the awareness of performance variation, energy consumption and cost to efficiently manage the cloud resources. [ABSTRACT FROM AUTHOR]

Live migration helps to achieve resource consolidation and fault tolerance. It transfers VM storage together with VM memory and CPU status. During migration, a dirty page rate also delays the period of live migration, and it affects the performance of migration by increasing migration time, network bandwidth consumption, CPU processing overheads and application downtime. Experimental results after comparing with existing methods of VM live migration, reflects that with high data transfer rate, prolonged migration time and downtime make it infeasible to achieve seamless live migration. This paper provides a detailed analysis of the KVM strategy for live migration. It shows that the KVM iterative copy method, where all RAM data is marked as dirty and transferred during the first iteration, initially generating majority of additional overheads, mainly due to large data transfer. Based on these findings, the innovative Pareto Optimized framework [POF-SVLM] was developed and deployed as a standardized VM storage network, such as the Network Attached Storage (NAS), shared between the source and the target machine. Only the VM primary storage is to be transferred during live migration. Additionally, the architecture effectively monitors all I/O VM requests to determine unique pages in the primary memory that are only transferred during the iterations. All duplicate pages are downloaded directly from the NAS to the target machine. Detailed experimental evaluation shows that the proposed mechanism reduces the VM live migration overheads by 55–60%. Experimental results also shows that the downtime of POF-SVLM based live migration is in the range less than 60 s even for highly intense workloads. This is the most significant contribution of POF-SVLM because since the downtime less than 60 s, the user will not be able to notice it and results in seamless VM live migration. [ABSTRACT FROM AUTHOR]

An important issue of energy efficiency in cloud environment is to perform more jobs while consuming less amount of power. Virtual machine consolidation remains the most deployed strategy to manage both performance and energy consumption. Most of existing energy efficiency techniques save energy against the cost on performance degradation. Consolidation techniques leverage thresholds to detect overloaded and underloaded hosts that could be vacated to achieve optimal balance between host utilization and energy consumption. In this research, we propose an energy-efficient strategy (EES) to consolidate virtual machines in cloud environment with an aim of reducing the energy consumption while completing more tasks with the highest throughput. Our proposal makes use of the performance-to-power ratio to set upper thresholds for overload detection. In addition, EES considers the overall data center workload utilization to set lower thresholds, which can reduce the number of virtual machine migrations. The simulation results show that EES leads to energy-efficient workload consolidation with the minimal number of migrations and less energy consumption. The results conclude that EES saves energy consumption without compromising user's workload requirement. [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]

Cloud computing is Internet-based computing which requires more physical machines and consumes a large amount of power. By this means, that will reduce the profit of the service providers and harm the environment. How to effectively handle the power consumption of cloud computing has been an issue in recent years. When making a large number of operations, and power consumption cannot be underestimated. In this case, the usage of Virtualization that become widely in cloud computing nowadays, also need energy efficient scheduling methods. However, existing energy efficient scheduling methods of virtual machines (VMs) in the cloud cannot work well if the physical machines (PMs) are heterogeneous and their total power is considered. In this paper, we propose an implementation of a cloud infrastructure that can monitor the status of OpenStack and monitor the real-time status of a virtual machine on OpenStack. Also, achieve energy saving through live migration. The projects of monitoring include the utilization of CPU, load of memory, and power consumption. These data show in real-time, thoroughly monitor the real-time status of physical machines and virtual machines. It also records the utilization and power consumption of physical machines then show on this cloud infrastructure, to provide experimental evidence for the user as a reference. Based on the power consumption monitoring system, we can automatically allocate virtual machines on every physical machine by live migration, to balance the power consumption of every physical machine. It is not only can avoid idle and a waste of resources but also can avoid reducing machine life-time because of the physical machines always keep in high usage. [ABSTRACT FROM AUTHOR]

IaaS cloud computing data centers always employ live migration of virtual machines (VM) to achieve dynamic scheduling and management of IT resources. Pre-copy and Post-copy are two prevalent live migration algorithms. Both have pros and cons, and they work well only in suitable scenarios. Contrary to Pre-copy, Post-copy performs better with memory writing workloads running in VM to be migrated. Post-copy switches the VM to the target host immediately once the live migration is launched, and then it requests the source host to transfer memory pages whenever the VM accesses a nonexistent page. Although this Post-copy scheme can guarantee that every memory page needs to copy only once and the migration time is predictable, it can cause a mass of network requests for fault pages, which can lead to a prolonged downtime for VM and a degraded live migration performance. This paper proposes an improved Post-copy scheme with memory page prefetching, which leverages PPM (Prediction by Partial Match) to build a prediction model for memory page accessing and predict the pages to be read soon. Once a network page fault happens, this model determines the following pages to be accessed. Then, this faulted page together with these predicted pages are sent to the target host to avoid subsequent page faults. Experiment results show that this proposed scheme can allow for accurate page prediction and prominently decrease the network page faults and downtime for VM. [ABSTRACT FROM AUTHOR]

In IaaS cloud computing platforms, live migration of multiple virtual machines plays a dominant role in the dynamic scheduling, optimization and management of IT resources. Although Pre-copy and Post-copy are the prevalent live migration algorithms for the single virtual machine, which both have pros and cons, only one of them is monotonously adopted in the context of the gang of live migration. This scheme cannot choose the best migration algorithm for each virtual machine according to its business traffic and cause a traffic contention problem. This paper proposes an adaptive traffic-aware live migration algorithm selection framework, which leverages fuzzy clustering method to classify the virtual machines to be migrated according to their business traffics and migrate them with the chosen best-fitting migration algorithms. Experiment results show that the proposed framework can effectively choose the suitable migration algorithms for classified virtual machines and eventually improve the whole live migration performance meanwhile avoiding the degradation of business performance. [ABSTRACT FROM AUTHOR]

The Infrastructure-as-a-Service model of cloud computing allocates resources in the form of virtual machines that can be resized and live migrated at runtime. This paper presents a novel distributed resource allocation approach for VM consolidation relying on multi-agent systems. Our approach uses a utility function based on host CPU utilization to drive live migration actions. Experimental results show reduced service level agreement violations and a better overall performance compared to a centralized approach and a threshold-based distributed approach.

In a cloud computing the live migration of virtual machines shows a process of moving a running virtual machine from source physical machine to the destination, considering the CPU, memory, network, and storage states. Various performance metrics are tackled such as, downtime, total migration time, performance degradation, and amount of migrated data, which are affected when a virtual machine is migrated. This paper presents an overview and understanding of virtual machine live migration techniques, of the different works in literature that consider this issue, which might impact the work of professionals and researchers to further explore the challenges and provide optimal solutions. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) applications running on mobile devices are subject to the low storage capacity and short battery lifetime. Edge clouds (EC) provide an approach to offload computation tasks and reduce network latency for these applications. The main challenge in such ecosystems is how to efficiently monitor and allocate VM resources to realize load balancing among edge clouds. In this paper, we propose Ada-Things, an adaptive VM monitoring and live migration Strategy for IoT applications in edge cloud architecture. The basic idea of Ada-Things is that the migration method of a VM should be determined by its workload characteristics. Specifically, based on the variation of current memory dirty page rate in IoT applications, Ada-Things can adaptively select the most appropriate migration method to copy memory pages, thus addressing the two limitations (application generality and performance imbalance) of existing VM migration methods in edge cloud. Evaluation results show, compared with traditional methods, Ada-Things can significantly reduce the total migration time by 21%, the VM downtime by 38% and the amount of pages transferred by 29% in average. • We find the needs of VM migration for load balancing among edge clouds in IoT ecosystems. • We analysis the two major limitations: application generality and performance imbalance in lots of existing migration methods. • We present a general memory copy strategy Ada-Things for VM monitoring and live migration to address these two major limitations. • We realize Ada-Things on Openstack based edge cloud computing management platform. [ABSTRACT FROM AUTHOR]

In the last decade, cloud computing has brought enormous changes to people's lives. Cloud computing gives a client-driven computational model. In this case, the pay-per-use model enables a flexible method of deploying and sharing distributed services and resources. Furthermore, computational resources are progressively reassembled and insulated to give various types of services. Current solutions on how to efficiently utilize energy consumption are inadequate. Thus, we propose two methods to tackle the problem, which are the dynamic resource allocation method and the energy saving method, respectively. In this work, we firstly deployed an infrastructure platform based on OpenStack to actualize the proposed approach with live migration of virtual machines (VMs). Secondly, we allocated the dynamic resources and proposed the energy saving algorithm. Thirdly, we monitor the power distribution unit status to record the energy consumption of the system. Finally, in the experiments, we found that the proposed algorithms cannot just accomplish the proficient use of VMs' resources but also thrift the energy usage of physical machines. Our experimental results demonstrate about 39.89% of energy is saved from 20 VCPUs of 20 GB memory. [ABSTRACT FROM AUTHOR]

Cloud providers leverage live migration of virtual machines to reduce energy consumption and allocate resources efficiently in data centers. Each migration decision depends on three questions: when to move a virtual machine, which virtual machine to move and where to move it? Dynamic, uncertain, and heterogeneous workloads running on virtual machines make such decisions difficult. Knowledge-based and heuristics-based algorithms are commonly used to tackle this problem. Knowledge-based algorithms, such as MaxWeight scheduling algorithms, are dependent on the specifics and the dynamics of the targeted Cloud architectures and applications. Heuristics-based algorithms, such as MMT algorithms, suffer from high variance and poor convergence because of their greedy approach. We propose an online reinforcement learning algorithm called Megh. Megh does not require prior knowledge of the workload rather learns the dynamics of workloads as-it-goes. Megh models the problem of energy- and performance-efficient resource management during live migration as a Markov decision process and solves it using a functional approximation scheme. While several reinforcement learning algorithms are proposed to solve this problem, these algorithms remain confined to the academic realm as they face the curse of dimensionality. They are either not scalable in real-time, as it is the case of MadVM, or need an elaborate offline training, as it is the case of Q-learning. These algorithms often incur execution overheads which are comparable with the migration time of a VM. Megh overcomes these deficiencies. Megh uses a novel dimensionality reduction scheme to project the combinatorially explosive state-action space to a polynomial dimensional space with a sparse basis. Megh has the capacity to learn uncertain dynamics and the ability to work in real-time without incurring significant execution overhead. Megh is both scalable and robust. We implement Megh using the CloudSim toolkit and empirically evaluate its performance with the PlanetLab and the Google Cluster workloads. Experiments validate that Megh is more cost-effective, converges faster, incurs smaller execution overhead and is more scalable than MadVM and MMT. An empirical sensitivity analysis explicates the choice of parameters in experiments. [ABSTRACT FROM AUTHOR]

Cloud Computing has the facility to transform a large part of information technology into services in which computer resources are virtualized and made available as a utility service. From here comes the importance of scheduling virtual resources to get the maximum utilization of physical resources. This paper presents two cooperative algorithms: a Smart Elastic Scheduling Algorithm (SESA) and an Adaptive Worst Fit Decreasing Virtual Machine Placement (AWFDVP) algorithm. The proposed algorithms work to dynamically distribute the cloud system's physical resources to obtain a load-balanced consolidated system with minimal used power, memory, and processing time. SESA arranges VMs in clusters based on their memory and CPU parameters' value. Then it deals with the colocated VMs that share some of their memory pages and located on the same physical machine, as a group. Then the migration decision is made based on the evaluation for the entire system by AWFDVP. This process minimizes the number of migrations among the system, saves the consumed power, and prevents performance degradation for the VM while preserving the load-balance state of the entire system. SESA reduces the power consumption in the cloud system by 28.1%, the number of migrations by 57.77%, and performance degradation by 57.1%. [ABSTRACT FROM AUTHOR]

By live migration technology, multiple virtual machines (VMs) can be consolidated into a fewer physical servers and the idle ones can be shut down or switched to low-power mode, thus reducing the energy consumption of cloud data centers. However, live migration can result in performance degradation of migrated VMs, or even interrupting their services. At the same time, live migration can also aggravate the overheads of data transmissions and produce additional energy consumption in cloud data centers. All these negative influences belong to migration cost (MC) caused by VM migration, which becomes an important cost factor that can't be ignored. Otherwise, another important concern, remaining runtime of the migrated VM, also has influence on the efficiency of VM consolidation, which is not well addressed as well. This paper investigates MC-aware VM consolidation problem and formulates the problem as a multi-constraint optimization model by considering migration cost and remaining runtime of VMs. Based on the proposed model, a heuristic algorithm, called MC-aware VM consolidation (MVC) algorithm, is developed. Finally, based on a real-world cloud trace, we conduct extensive experimental studies to verify the validity of the proposed model and algorithm. Experimental results show that, compared with some popular algorithms, MVC algorithm can effectively decrease the migration cost and, at the same time guarantee the energy consumption within a certain low level. [ABSTRACT FROM AUTHOR]

With the blooming of cloud computing, the demand for data centers has been rising greatly in recent years. Their energy consumption and environmental impact has become much more significant due to the continuous growth of data center supply. It is possible to reduce the amount of energy consumed by a data center by shutting down unnecessary servers and maintaining only a subset running, such that it is enough to fulfill the resource demand. With recent advances in virtualization technology, it even became possible to consolidate the workload of multiple under-utilized servers into a single server. However, too aggressive consolidation may lead to significant degradation of data center performance. Therefore, the problem of simultaneously minimizing energy consumption and performance degradation in a data center is a complex and challenging problem. In this paper, a novel multi-objective Boolean optimization encoding for virtual machine consolidation is proposed and several approaches to solve it are described and compared. Moreover, this encoding is extended to consider anti-collocation constraints and the migration of virtual machines that are initially placed. This work is in part motivated by the great improvements in the performance of Boolean optimization solvers, thus increasing their applicability and effectiveness for a wider spectrum of complex problems. In this case, specific techniques are applied to further boost the performance, namely search space reduction by symmetry breaking and heuristic reduction of the instance size. An extensive experimental evaluation shows the suitability of the proposed solution in comparison to the state-of-the-art approaches based on stochastic methods. [ABSTRACT FROM AUTHOR]

To achieve energy efficiency in data centers, dynamic virtual machine (VM) consolidation as a key technique has become increasingly important nowadays due to the significant amounts of power needed to operate these data centers. Most of the existing works on VM consolidation have been focused only on reducing the number of active physical machines (PMs) using VM live migration to prevent inefficient usage of resources. But on the other hand, high frequency of VM consolidation has a negative effect on the system reliability. Indeed, there is a crucial trade-off between reliability and energy efficiency, and to optimize the relationship between these two metrics, further research is needed. Therefore, in this paper a novel approach is proposed that considers the reliability of each PM along with reducing the number of active PMs simultaneously. To determine the reliability of PMs, a Markov chain model is designed, and then, PMs have prioritized based on their CPU utilization level and the reliability status. In each phase of the consolidation process, a new algorithm is proposed. A target PM selection criterion is also presented that by considering both energy consumption and reliability selects the appropriate PM. We have validated the effectiveness of our proposed approach by conducting a performance evaluation study using CloudSim toolkit. The simulation results show that the proposed approach can significantly improve energy efficiency, avoid inefficient VM migrations and reduce SLA violations. [ABSTRACT FROM AUTHOR]