WARM: Workload-Aware Multi-Application Task Scheduling for Revenue Maximization in SDN-Based Cloud Data Center

Haitao Yuan, Jing Bi, Mengchu Zhou, Khaled Sedraoui

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Nowadays an increasing number of companies and organizations choose to deploy their applications in data centers to leverage resource sharing. The increase in tasks of multiple applications, however, makes it challenging for a provider to maximize its revenue by intelligently scheduling tasks in its software-defined networking (SDN)-enabled data centers. Existing SDN controllers only reduce network latency while ignoring virtual machine (VM) latency, which may lead to revenue loss. In the context of SDN-enabled data centers, this paper presents a workload-Aware revenue maximization (WARM) approach to maximize the revenue from a data center provider's perspective. Its core idea is to jointly consider the optimal combination of VMs and routing paths for tasks of each application. This work compares it with state-of-The-Art methods, experimentally. The results show that WARM yields the best schedules that not only increase the revenue but also reduce the round-Trip time of tasks for all applications.

Original languageEnglish (US)
Pages (from-to)645-657
Number of pages13
JournalIEEE Access
Volume6
DOIs
StatePublished - 2018

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

Keywords

  • Performance modeling and analysis
  • delay assurance chaotic search
  • metaheuristic optimization
  • particle swarm optimization
  • revenue maximization
  • simulated annealing
  • task scheduling

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