Performance and energy-aware bi-objective tasks scheduling for cloud data centers

Huned Materwala, Leila Ismail

Research output: Contribution to journalConference articlepeer-review

6 Citations (Scopus)


Cloud computing enables remote execution of users' tasks. The pervasive adoption of cloud computing in smart cities' services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in the cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to trade off the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm.

Original languageEnglish
Pages (from-to)238-246
Number of pages9
JournalProcedia Computer Science
Publication statusPublished - 2021
Event6th Information Systems International Conference, ISICO 2021 - Virtual, Online, Italy
Duration: Aug 7 2021Aug 8 2021


  • Autonomous agents
  • Cloud computing
  • Energy-efficiency
  • Evolutionary algorithm
  • Genetic algorithm
  • Multi-objective optimization
  • Performance
  • Quality of service

ASJC Scopus subject areas

  • General Computer Science


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