Application-aware resource allocation for SDN-based cloud datacenters

Weifan Hong, Kuo-Chen Wang, Yi Huai Hsu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Scopus citations

Abstract

In cloud datacenters, since resource requirements change frequently, how to assign and manage resources efficiently while meeting service level agreements (SLAs) of different types of applications is an important research issue. In this paper, we propose an Application-aware Resource Allocation (App-RA) scheme to predict resource requirements and allocate an appropriate number of virtual machines (VMs) for each application in SDN-based cloud datacenters. To the best of our knowledge, the proposed App-RA is the first application-aware resource allocation scheme that adapts to all types of applications. The App-RA can meet SLAs, allocate resources efficiently, and reduce power consumption for each application in cloud datacenters. The proposed App-RA adopts the neural network based predictor to forecast the requirements of resources (CPU, Memory, GPU, Disk I/O and bandwidth) for an application. In the proposed App-RA, we have designed two algorithms which allocate appropriate numbers of virtual machines and use the VM allocation threshold to avoid SLA violations for five different types of applications. In addition, we adopt an SDN-based OpenFlow network with CICQ switches to appropriately schedule packets for different types of application in the network layer. Finally, simulation results show that the power consumption of the proposed App-RA is only 9.21% higher than that of the best case (oracle) and the power consumption of EAACVA, which is a representative resource allocation method for non-graphic applications, is 104.58% worse than that of App-RA. Furthermore, the SLA violation rate of the proposed App-RA is less than 4% for all applications.

Original languageEnglish
Title of host publicationProceedings - 2013 International Conference on Cloud Computing and Big Data, CLOUDCOM-ASIA 2013
PublisherIEEE Computer Society
Pages106-110
Number of pages5
ISBN (Print)9781479928293
DOIs
StatePublished - 1 Jan 2013
Event2013 International Conference on Cloud Computing and Big Data, CLOUDCOM-ASIA 2013 - Fuzhou, Fujian, China
Duration: 16 Dec 201318 Dec 2013

Publication series

NameProceedings - 2013 International Conference on Cloud Computing and Big Data, CLOUDCOM-ASIA 2013

Conference

Conference2013 International Conference on Cloud Computing and Big Data, CLOUDCOM-ASIA 2013
CountryChina
CityFuzhou, Fujian
Period16/12/1318/12/13

Keywords

  • Application-aware
  • Cloud datacenter
  • Resource allocation
  • Service level agreement
  • Software define network

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