Virtual Machine Migration Problem Research Articles

A high-applicability heterogeneous cloud data centers resource management algorithm based on trusted virtual machine migration

With the continuous development and maturity of cloud computing technology, the scale and number of cloud data center (CDC) are also expanding. This increasingly draws attention to the problem of high energy consumption in CDCs. Dynamic virtual machine (VM) consolidation is a promising approach for reducing energy consumption. VM migration, as a VM consolidation technology, can effectively improve the utilization of physical machine (PM) and optimize the scheduling process of CDCs. However, most VM integration algorithms, in existing research, are aimed at improving the utilization of PMs. Excessive utilization of PMs may increase the competition for shared resources among the VMs running on them. As a result, the performance of these VMs deteriorates, and the execution time of cloud tasks is increased or even interrupted. This study systematically analyzes the overall architecture of CDCs. Subsequently, migration rules are established for the one-dimensional and multidimensional trusted VMs. A high- applicability heterogeneous CDC resource management algorithm based on trusted VM migration (HTVM2) is then proposed. The proposed algorithm not only solves the one-dimensional VM migration problem of homogeneous and heterogeneous CDCs but also those of multi-dimensional VMs. This improves the success rate of VM migration, reduces the energy consumption of the CDC, and improves load balancing while ensuring VM performance. Finally, the algorithm was compared with the other three algorithms outperforming them all, as demonstrated by experimental results.

A new metaheuristic‐based method for solving the virtual machines migration problem in the green cloud computing

SummaryCloud computing (CC) provides dynamic hiring of server abilities as scalable virtualized services to end‐users. However, data center hosting wastes massive amounts of energy resulting in high operational costs and carbon footprints. Also, virtualization is one of CC's main features, and physical resources are delivered by virtual machine (VM). Therefore, in the present article, a new method is provided to improve the VM energy consumption and execution time in the VM migration problem using a hybrid optimization algorithm. Since this issue is one of the famous NP‐hard problems, a method is proposed in this article works based on genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The hybrid algorithm uses a GA to dominate PSO algorithms' constraints, such as weak convergence and stymie in global optima. The CloudSim simulator is employed to show the efficiency of the method compared to others. Using this method will keep the proficiency and power performance of the data centers at the same level. The results showed that energy consumption in the proposed method is better than the other three methods and has been improved by an average of 23.19%. Also, the results showed that execution time is better than the other three methods and has been improved by an average of 29.01%.

Energy-Aware Virtual Machine Management in Inter-Datacenter Networks Over Elastic Optical Infrastructure

Datacenters (DCs), deployed in a large scale to support the ever increasing demand for data processing applications, consume tremendous energy. Powering DCs with renewable energy can effectively reduce the brown energy consumption. Owing to geographically distributed deployment of DCs, the renewable energy generation and the data processing demands usually vary in different DCs. Migrating virtual machines (VMs) among DCs according to the availability of renewable energy helps match the energy demands and the renewable energy generation in DCs, and thus maximizes the utilization of renewable energy. We first elicit the renewable energy-aware inter-datacenter (inter-DC) VM migration problem in an inter-DC network over the elastic optical infrastructure, present it as a many-manycast communications problem, and then formulate it as an integer linear programming problem. The objective is to minimize the total cost of the brown energy consumption of DCs in such inter-DC network via VM migration. We use CVX and Gurobi to solve this problem for small network configurations, and we propose a few heuristic algorithms that approximate the optimal solution for large network configurations. Through extensive simulations, we show that the proposed algorithms, by migrating VM among DCs, can reduce up to 19.7% cost of the brown energy consumption.

Traffic-Aware Virtual Machine Migration in Topology-Adaptive DCN

Virtual machine (VM) migration is a key technique for network resource optimization in modern data center networks. Previous work generally focuses on how to place the VMs efficiently in a static network topology by migrating the VMs with large traffic demands to close servers. As the flow demands between VMs change, however, a great cost will be paid for the VM migration. In this paper, we propose a new paradigm for VM migration by dynamically constructing adaptive topologies based on the VM demands to lower the cost of both VM migration and communication. We formulate the traffic-aware VM migration problem in an adaptive topology and show its NP-hardness. For periodic traffic, we develop a novel progressive-decompose-rounding algorithm to schedule VM migration in polynomial time with a proved approximation ratio. For highly dynamic flows, we design an online decision-maker (ODM) algorithm with proved performance bound. Extensive trace-based simulations show that PDR and ODM can achieve about four times flow throughput among VMs with less than a quarter of the migration cost compared to other state-of-art VM migration solutions. We finally implement an OpenvSwitch-based testbed and demonstrate the efficiency of our solutions.

One‐on‐one contract game–based dynamic virtual machine migration scheme for Mobile Edge Computing

AbstractIn the evolution toward fifth‐generation networks, Mobile Edge Computing (MEC) is an emerging paradigm, conceived to meet the ever‐increasing computational demands of mobile applications. Within the access range of mobile devices, the MEC technique promises the enablement of efficient Mobile Cloud Computing services. In an MEC system, virtual machine (VM) migration is a key issue; VM migration is the process of moving a VM from an edge node to another edge node. To improve service quality and system performance, the VM migration method has a dual focus on the MEC system's computation and communication resources. In this study, we formulate the VM migration problem as a one‐on‐one contract game model and develop a learning‐based price control mechanism to effectively handle the MEC's resource. By using the game methodology and learning process, our approach is able to capture the dynamics of MEC systems, and it interacts continually with an unknown system environment. Finally, extensive simulation results are provided to demonstrate the capability of the proposed approach in achieving, with respect to existing MEC schemes, both higher resource utilization and system throughput, as well as reduced service drop ratio and reduced service delay.

Communication-aware virtual machine migration in cloud data centres

The virtual machine (VM) migration technology is widely used in cloud data centres for achieving load balance, or saving energy consumption. Despite the potential benefits of the VM migrations, relatively little work has taken the inter-VM communication into account, with researchers mainly focusing on reducing the network cost of migration or the energy cost. In this paper, we focus on the VM migration problem in the context of large volume of inter-VM communication traffic. To this end, we make decisions on three problems of when, which, and to where the VMs shall be migrated. Specifically, we formulate an optimisation of minimising the number of migrations as well as the cost of both inter-VM traffic and migration traffic. To solve the optimisation, we design an efficient communication-aware VM migration algorithm and seamlessly combine three types of resources: CPU, memory and bandwidth. Finally, we conduct comprehensive experiments based on the CloudSim simulator. Extensive simulation results show that the proposed method can outperform the state-of-art RIAL and Sandpiper, in terms of the following three metrics: the number of migrations, the inter-VM communication cost, and the migration network cost.

Mobile Cloud-Based Big Healthcare Data Processing in Smart Cities

In recent years, the Smart City concept has become popular for its promise to improve the quality of life of urban citizens. The concept involves multiple disciplines, such as Smart health care, Smart transportation, and Smart community. Most services in Smart Cities, especially in the Smart healthcare domain, require the real-time sharing, processing, and analyzing of Big Healthcare Data for intelligent decision making. Therefore, a strong wireless and mobile communication infrastructure is necessary to connect and access Smart healthcare services, people, and sensors seamlessly, anywhere at any time. In this scenario, mobile cloud computing (MCC) can play a vital role by offloading Big Healthcare Data related tasks, such as sharing, processing, and analysis, from mobile applications to cloud resources, ensuring quality of service demands of end users. Such resource migration, which is also termed virtual machine (VM) migration, is effective in the Smart healthcare scenario in Smart Cities. In this paper, we propose an ant colony optimization-based joint VM migration model for a heterogeneous, MCC-based Smart Healthcare system in Smart City environment. In this model, the user’s mobility and provisioned VM resources in the cloud address the VM migration problem. We also present a thorough performance evaluation to investigate the effectiveness of our proposed model compared with the state-of-the-art approaches.

Network-aware virtual machine migration in an overcommitted cloud

Virtualization, which acts as the underlying technology for cloud computing, enables large amounts of third-party applications to be packed into virtual machines (VMs). VM migration enables servers to be reconsolidated or reshuffled to reduce the operational costs of data centers. The network traffic costs for VM migration currently attract limited attention.However, traffic and bandwidth demands among VMs in a data center account for considerable total traffic. VM migration also causes additional data transfer overhead, which would also increase the network cost of the data center.This study considers a network-aware VM migration (NetVMM) problem in an overcommitted cloud and formulates it into a non-deterministic polynomial time-complete problem. This study aims to minimize network traffic costs by considering the inherent dependencies among VMs that comprise a multi-tier application and the underlying topology of physical machines and to ensure a good trade-off between network communication and VM migration costs.The mechanism that the swarm intelligence algorithm aims to find is an approximate optimal solution through repeated iterations to make it a good solution for the VM migration problem. In this study, genetic algorithm (GA) and artificial bee colony (ABC) are adopted and changed to suit the VM migration problem to minimize the network cost. Experimental results show that GA has low network costs when VM instances are small. However, when the problem size increases, ABC is advantageous to GA. The running time of ABC is also nearly half than that of GA. To the best of our knowledge, we are the first to use ABC to solve the NetVMM problem.

A heuristic placement selection of live virtual machine migration for energy-saving in cloud computing environment.

The field of live VM (virtual machine) migration has been a hotspot problem in green cloud computing. Live VM migration problem is divided into two research aspects: live VM migration mechanism and live VM migration policy. In the meanwhile, with the development of energy-aware computing, we have focused on the VM placement selection of live migration, namely live VM migration policy for energy saving. In this paper, a novel heuristic approach PS-ES is presented. Its main idea includes two parts. One is that it combines the PSO (particle swarm optimization) idea with the SA (simulated annealing) idea to achieve an improved PSO-based approach with the better global search's ability. The other one is that it uses the Probability Theory and Mathematical Statistics and once again utilizes the SA idea to deal with the data obtained from the improved PSO-based process to get the final solution. And thus the whole approach achieves a long-term optimization for energy saving as it has considered not only the optimization of the current problem scenario but also that of the future problem. The experimental results demonstrate that PS-ES evidently reduces the total incremental energy consumption and better protects the performance of VM running and migrating compared with randomly migrating and optimally migrating. As a result, the proposed PS-ES approach has capabilities to make the result of live VM migration events more high-effective and valuable.

Support vector machine approach for virtual machine migration in cloud data center

The social media services are popular with Internet services today, such as Facebook, YouTube, Plurk and Twitter. However, the enormous interactions among human beings also result in highly computational costs. The requested resources and demands of some specific social media services are changing severely, and the virtual machines (VMs) exhaust the computing resource of physical machine (PM). Thus this will lead to VM migration. Many researchers investigate how to stabilize the average utilization of virtual machines and physical machines in cloud data center. In this paper, we formulated the VM migration problem in cloud data center based on mixed integer linear programming (MILP). Then, the VM allocation algorithm was proposed to allocate the VMs among the PMs, which is based on the Support Vector Machine (SVM). According to the training process during a specific time, the minimum numbers of VM migration and maximum resource utilization of PMs were accomplished. As the allocation case and simulation results showed, we achieved the stable and low-cost for social media services in cloud data center.

Optimal Green Virtual Machine Migration Model

Cloud computing provides the opportunity to migrate virtual machines to “follow-the-green” data centres. That is, to migrate virtual machines between green data centres on the basis of clean energy availability, to mitigate the environmental impact of carbon footprint emissions and energy consumption. The virtual machine migration problem can be modelled to maximize the utility of computing resources or minimizing the cost of using computing resources. However, this would ignore the network energy consumption and its impact on the overall CO2 emissions. Unless this is taken into account the extra data traffic due to migration of data could then cause an increase in brown energy consumption and eventually lead to an unintended increase in carbon footprint emissions. Energy consumption is a key aspect in deploying distributed service in cloud networks within decentralized service delivery architectures. In this paper, the authors address an optimization view of the problem of locating a set of cloud services on a set of sites green data centres managed by a service provider or hybrid cloud computing brokerage. The authors’ goal is to minimize the overall network energy consumption and carbon footprint emissions for accessing the cloud services for any pair of data centres i and j. The authors propose an optimization migration model based on the development of integer linear programming (ILP) models, to identify the leverage of green energy sources with data centres and the energy consumption of migrating VMs.