Towards Service Composition Aware Virtual Machine Migration Approach in the Cloud

Ao Zhou, Shangguang Wang, Xiao Ma, Stephen S. Yau

Research output: Contribution to journalArticle

Abstract

There is a growing trend for service providers to migrate their services from local clusters to the cloud data center. When there is no single service can satisfy the functionality requirement of the end user, existing services are combined together to fulfill the requirements. The data communication between component service hosting servers imposes a heavy burden on the data center network. In this paper, we seek to reduce the data center network resource consumption by designing a novel service composition aware virtual machine migration approach. Firstly, we formulate the problem as a multi-object integer non-linear(INLP) programming problem. The problem, which can be reduced into a well-known multi-object quadratic assignment problem, is proved to be NP-hard. Secondly, we simplify the multiple-objects INLP formulation into an equivalent, but much simplified single object ILP formulation. Then, we prove that the simplified formulation can also lead to the optimal solutions. Finally, optimization problem solvers, such as LPSolver, are employed to solve the problem. Experimental results in a large scale cloud data center demonstrate that our method significantly reduce the network resource consumption than other approaches.

Original languageEnglish (US)
JournalIEEE Transactions on Services Computing
DOIs
Publication statusAccepted/In press - Jan 1 2019

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Keywords

  • cloud computing
  • data center
  • service composition
  • virtual machine migration

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems and Management

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