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

Tseng, Fan‐Hsun; Chen, Xiaojiao; Chou, Li-Der; Chao, Han‐Chieh; Chen, Shiping

doi:10.1007/s11042-014-2086-z

Cited by 15 publications

(3 citation statements)

References 19 publications

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“…Apart from these, the various machine-learning approaches are also used to migrate the VM from one host to other. The techniques like autoregressive integrated moving average [71], support vector regression [72], linear regression, SVR with bootstrap aggregation [73] were also used for VM migration. These approaches are used to forecast and manage resources effectively in the data center, as well as to calculate the energy consumption.…”

Section: Virtual Machine Migrationmentioning

confidence: 99%

Energy efficiency in cloud computing data centers: a survey on software technologies

2022

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Cloud computing is a commercial and economic paradigm that has gained traction since 2006 and is presently the most significant technology in IT sector. From the notion of cloud computing to its energy efficiency, cloud has been the subject of much discussion. The energy consumption of data centres alone will rise from 200 TWh in 2016 to 2967 TWh in 2030. The data centres require a lot of power to provide services, which increases CO2 emissions. In this survey paper, software-based technologies that can be used for building green data centers and include power management at individual software level has been discussed. The paper discusses the energy efficiency in containers and problem-solving approaches used for reducing power consumption in data centers. Further, the paper also gives details about the impact of data centers on environment that includes the e-waste and the various standards opted by different countries for giving rating to the data centers. This article goes beyond just demonstrating new green cloud computing possibilities. Instead, it focuses the attention and resources of academia and society on a critical issue: long-term technological advancement. The article covers the new technologies that can be applied at the individual software level that includes techniques applied at virtualization level, operating system level and application level. It clearly defines different measures at each level to reduce the energy consumption that clearly adds value to the current environmental problem of pollution reduction. This article also addresses the difficulties, concerns, and needs that cloud data centres and cloud organisations must grasp, as well as some of the factors and case studies that influence green cloud usage.

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Section: Virtual Machine Migrationmentioning

confidence: 99%

Energy efficiency in cloud computing data centers: a survey on software technologies

2022

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“…This methodology was relocated the maximally loaded VM to the most un‐loaded dynamic hub while keeping up with the exhibition and energy productivity of the server farms. Moreover, Tseng et al 17 had developed VM migration on the cloud data center. To achieve this concept, a support vector machine (SVM) has been introduced.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved beetle swarm optimization algorithm for energy efficient virtual machine consolidation on cloud environment

Hariharan

Rathinavelayatham

Shanmugaiah

et al. 2022

Concurrency and Computation

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In cloud computing, virtual machine (VM) consolidation and migration are significant schemes to enhance the energy efficiency and costs of the cloud environment. The process of VM consolidation is to execute a more number of tasks with less power consumption. However, due to unreliable physical resources, energy consumption is increased. So, to solve this issue, improved beetle swarm optimization (IBSO) algorithm based on energy-aware VM consolidation is presented in this article. IBSO algorithm joins together beetle swarm optimization (BSO) and particle swarm optimization (PSO). The proposed algorithm is defined with the effective representation of solution and multi-objective functions such as power consumption, resource wastage, and SLA violation. Results of the work are depicted that the proposed approach outperformed different previous methods in terms of different evaluation measures.

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“…1 In order to guarantee high QoS, huge amounts of resources need to be provisioned in cloud data centers. However, managing and maintaining over-configured resources in turn leads to a large amount of energy costs, including the configuration and maintenance costs of cooling systems, physical components and other facilities [7], [13]- [15]. Therefore, one challenge that cloud service providers face is to ensure high service quality while controlling the energy consumption of data centers.…”

Section: Introduction a Motivationmentioning

confidence: 99%

An Experience-Based Scheme for Energy-SLA Balance in Cloud Data Centers

Zhou

Liu

et al. 2019

IEEE Access

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The proliferation of cloud computing has resulted in the establishment of large-scale data centers containing thousands of computing nodes and consuming enormous amounts of electrical energy. However, the low-cost and high-efficiency slogans are getting louder and louder, and the IT industry is also striving for this pursuit. Therefore, it is vital to minimizing the energy consumption for cloud providers while ensuring the quality of service for cloud users. In this paper, we propose several heuristic strategies to optimize these two metrics based on a two-level management model under a heterogeneous cloud environment. First, to detect whether a physical node is continuously overloaded, we propose an empirical forecast algorithm, which predicts the future state of the host by statistically analyzing the historical utilization data of the host. Second, we propose a weighted priority virtual machine (VM) selection algorithm. For each VM on the overloaded host, we weight several utilization factors and calculate its migration priority. Then, we simulate the proposed approach and compare it with the existing overloaded hosts detection algorithms with different VM selection policies under different workloads.

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

Cited by 15 publications

References 19 publications

Energy efficiency in cloud computing data centers: a survey on software technologies

Energy efficiency in cloud computing data centers: a survey on software technologies

Improved beetle swarm optimization algorithm for energy efficient virtual machine consolidation on cloud environment

An Experience-Based Scheme for Energy-SLA Balance in Cloud Data Centers

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