A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing

Keng Mao Cho, Pang Wei Tsai, Chun Wei Tsai, Chu Sing Yang

Research output: Contribution to journalArticlepeer-review

112 Scopus citations


Virtual machine (VM) scheduling with load balancing in cloud computing aims to assign VMs to suitable servers and balance the resource usage among all of the servers. In an infrastructure-as-a-service framework, there will be dynamic input requests, where the system is in charge of creating VMs without considering what types of tasks run on them. Therefore, scheduling that focuses only on fixed task sets or that requires detailed task information is not suitable for this system. This paper combines ant colony optimization and particle swarm optimization to solve the VM scheduling problem, with the result being known as ant colony optimization with particle swarm (ACOPS). ACOPS uses historical information to predict the workload of new input requests to adapt to dynamic environments without additional task information. ACOPS also rejects requests that cannot be satisfied before scheduling to reduce the computing time of the scheduling procedure. Experimental results indicate that the proposed algorithm can keep the load balance in a dynamic environment and outperform other approaches.

Original languageEnglish
Pages (from-to)1297-1309
Number of pages13
JournalNeural Computing and Applications
Issue number6
StatePublished - 25 Aug 2015


  • Ant colony optimization
  • Cloud computing
  • Load balance
  • Particle swarm optimization
  • Scheduling


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