Using swarm intelligence to optimize the energy consumption for distributed systems

Bergmann Neil Bergmann, Yuk Ying Chung*, Xiangrui Yang, Zhe Chen, Wei Chang Yeh, Xiangjian He, Raja Jurdak

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Large, distributed, network-based computing systems (also known as Cloud Computing) have recently gained significant interest. We expect significantly more applications or web services will be relying on network-based servers, therefore reducing the energy consumption of these systems would be beneficial for companies to save their budgets on running their machines as well as cooling down their infrastructures. Dynamic Voltage Scaling can save significant energy for these systems, but it faces the challenge of efficient and balanced parallelization of tasks in order to maximize energy savings while maintaining desired performance levels. This paper proposes our Simplified Swarm Optimization (SSO) method to reduce the energy consumption for distributed systems with Dynamic Voltage Scaling. The results of SSO have been compared to the most popular evolutionary Particle Swarm Optimization (PSO) algorithm and have shown to be more efficient and effective, reducing both the execution time for scheduling and makespan.

Original languageEnglish
Pages (from-to)59-66
Number of pages8
JournalModern Applied Science
Issue number6
Publication statusPublished - 2013
Externally publishedYes


  • Distributed computing
  • Energy optimization
  • Evolutionary algorithm


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