Multi-objective optimization for rebalancing virtual machine placement

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Multi-objective optimization for rebalancing virtual machine placement. / Li, Rui; Zheng, Qinghua; Li, Xiuqi; Yan, Zheng.

In: Future Generation Computer Systems, 2017.

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@article{729c8a75e7e242d28eb301d13840d44d,
title = "Multi-objective optimization for rebalancing virtual machine placement",
abstract = "Load balancer, as a key component in cloud computing, seeks to improve the performance of a distributed system by allocating workload amongst a set of cooperating hosts. A good balancing strategy would make the distributed system efficient and enhance user satisfaction. However, the balance of Host Machines (HMs) in a real cloud environment often breaks due to frequently occurred addition and removal of Virtual Machines (VMs). Therefore, it is essential to schedule the VMs to be reBalanced (VMrB). In this paper, we first summarize and analyze the existing studies on load rebalancing. We then propose a novel solution to the VMrB problem, namely a Pareto-based Multi-Objective VM reBalance solution (MOVMrB), which aims to simultaneously minimize the disequilibrium of both inter-HM and intra-HM loads. It is one of the first solutions that leverages the inter-HM and intra-HM loads and applies a multiple objective optimization strategy to overcome the virtual machine rebalance problem. In our work, we keep migration cost in mind and propose a hybrid VM live migration algorithm that significantly reduces the I/O complexity of VMrB processing. The proposed rebalancing solution is evaluated based on two synthetic datasets and two real-world datasets under a CloudSim framework. Our experimental results show that MOVMrB outperforms other existing multi-objective solutions and also demonstrate its extensibility to support complex scenarios in cloud computing.",
keywords = "Multi-objective optimization, Resource utilization, Virtual machine placement",
author = "Rui Li and Qinghua Zheng and Xiuqi Li and Zheng Yan",
year = "2017",
doi = "10.1016/j.future.2017.08.027",
language = "English",
journal = "Future Generation Computer Systems: the international journal of grid computing and escience",
issn = "0167-739X",

}

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TY - JOUR

T1 - Multi-objective optimization for rebalancing virtual machine placement

AU - Li, Rui

AU - Zheng, Qinghua

AU - Li, Xiuqi

AU - Yan, Zheng

PY - 2017

Y1 - 2017

N2 - Load balancer, as a key component in cloud computing, seeks to improve the performance of a distributed system by allocating workload amongst a set of cooperating hosts. A good balancing strategy would make the distributed system efficient and enhance user satisfaction. However, the balance of Host Machines (HMs) in a real cloud environment often breaks due to frequently occurred addition and removal of Virtual Machines (VMs). Therefore, it is essential to schedule the VMs to be reBalanced (VMrB). In this paper, we first summarize and analyze the existing studies on load rebalancing. We then propose a novel solution to the VMrB problem, namely a Pareto-based Multi-Objective VM reBalance solution (MOVMrB), which aims to simultaneously minimize the disequilibrium of both inter-HM and intra-HM loads. It is one of the first solutions that leverages the inter-HM and intra-HM loads and applies a multiple objective optimization strategy to overcome the virtual machine rebalance problem. In our work, we keep migration cost in mind and propose a hybrid VM live migration algorithm that significantly reduces the I/O complexity of VMrB processing. The proposed rebalancing solution is evaluated based on two synthetic datasets and two real-world datasets under a CloudSim framework. Our experimental results show that MOVMrB outperforms other existing multi-objective solutions and also demonstrate its extensibility to support complex scenarios in cloud computing.

AB - Load balancer, as a key component in cloud computing, seeks to improve the performance of a distributed system by allocating workload amongst a set of cooperating hosts. A good balancing strategy would make the distributed system efficient and enhance user satisfaction. However, the balance of Host Machines (HMs) in a real cloud environment often breaks due to frequently occurred addition and removal of Virtual Machines (VMs). Therefore, it is essential to schedule the VMs to be reBalanced (VMrB). In this paper, we first summarize and analyze the existing studies on load rebalancing. We then propose a novel solution to the VMrB problem, namely a Pareto-based Multi-Objective VM reBalance solution (MOVMrB), which aims to simultaneously minimize the disequilibrium of both inter-HM and intra-HM loads. It is one of the first solutions that leverages the inter-HM and intra-HM loads and applies a multiple objective optimization strategy to overcome the virtual machine rebalance problem. In our work, we keep migration cost in mind and propose a hybrid VM live migration algorithm that significantly reduces the I/O complexity of VMrB processing. The proposed rebalancing solution is evaluated based on two synthetic datasets and two real-world datasets under a CloudSim framework. Our experimental results show that MOVMrB outperforms other existing multi-objective solutions and also demonstrate its extensibility to support complex scenarios in cloud computing.

KW - Multi-objective optimization

KW - Resource utilization

KW - Virtual machine placement

UR - http://www.scopus.com/inward/record.url?scp=85029424834&partnerID=8YFLogxK

U2 - 10.1016/j.future.2017.08.027

DO - 10.1016/j.future.2017.08.027

M3 - Article

JO - Future Generation Computer Systems: the international journal of grid computing and escience

JF - Future Generation Computer Systems: the international journal of grid computing and escience

SN - 0167-739X

ER -

ID: 16193980