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GA-based cloud resource estimation for agent-based execution of bag-of-tasks applications

Author

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  • J. Octavio Gutierrez-Garcia

    (Gwangju Institute of Science and Technology)

  • Kwang Mong Sim

    (Gwangju Institute of Science and Technology)

Abstract

Executing bag-of-tasks applications in multiple Cloud environments while satisfying both consumers’ budgets and deadlines poses the following challenges: How many resources and how many hours should be allocated? What types of resources are required? How to coordinate the distributed execution of bag-of-tasks applications in resources composed from multiple Cloud providers?. This work proposes a genetic algorithm for estimating suboptimal sets of resources and an agent-based approach for executing bag-of-tasks applications simultaneously constrained by budgets and deadlines. Agents (endowed with distributed algorithms) compose resources and coordinate the execution of bag-of-tasks applications. Empirical results demonstrate that the genetic algorithm can autonomously estimate sets of resources to execute budget-constrained and deadline-constrained bag-of-tasks applications composed of more economical (but slower) resources in the presence of loose deadlines, and more powerful (but more expensive) resources in the presence of large budgets. Furthermore, agents can efficiently and successfully execute randomly generated bag-of-tasks applications in multi-Cloud environments.

Suggested Citation

  • J. Octavio Gutierrez-Garcia & Kwang Mong Sim, 2012. "GA-based cloud resource estimation for agent-based execution of bag-of-tasks applications," Information Systems Frontiers, Springer, vol. 14(4), pages 925-951, September.
    • Handle: RePEc:spr:infosf:v:14:y:2012:i:4:d:10.1007_s10796-011-9327-8
    DOI: 10.1007/s10796-011-9327-8
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    References listed on IDEAS

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    1. Konak, Abdullah & Coit, David W. & Smith, Alice E., 2006. "Multi-objective optimization using genetic algorithms: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 992-1007.
    2. Brent R. Moulton, 1996. "Bias in the Consumer Price Index: What Is the Evidence?," Journal of Economic Perspectives, American Economic Association, vol. 10(4), pages 159-177, Fall.
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    Citations

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    Cited by:

    1. Jason J. Jung & Yue-Shan Chang & Ying Liu & Chao-Chin Wu, 2012. "Advances in intelligent grid and cloud computing," Information Systems Frontiers, Springer, vol. 14(4), pages 823-825, September.
    2. Haoyi Xiong & Daqing Zhang & Daqiang Zhang & Vincent Gauthier & Kun Yang & Monique Becker, 2014. "MPaaS: Mobility prediction as a service in telecom cloud," Information Systems Frontiers, Springer, vol. 16(1), pages 59-75, March.
    3. Sanjaya K. Panda & Indrajeet Gupta & Prasanta K. Jana, 0. "Task scheduling algorithms for multi-cloud systems: allocation-aware approach," Information Systems Frontiers, Springer, vol. 0, pages 1-19.
    4. Li Chunlin & Li LaYuan, 2017. "Optimal scheduling across public and private clouds in complex hybrid cloud environment," Information Systems Frontiers, Springer, vol. 19(1), pages 1-12, February.
    5. Shuai Yuan & Sanjukta Das & Ram Ramesh & Chunming Qiao, 2023. "Availability-Aware Virtual Resource Provisioning for Infrastructure Service Agreements in the Cloud," Information Systems Frontiers, Springer, vol. 25(4), pages 1495-1512, August.
    6. John Oredo & Denis Dennehy, 2023. "Exploring the Role of Organizational Mindfulness on Cloud Computing and Firm Performance: The Case of Kenyan Organizations," Information Systems Frontiers, Springer, vol. 25(5), pages 2029-2050, October.
    7. Sanjaya K. Panda & Indrajeet Gupta & Prasanta K. Jana, 2019. "Task scheduling algorithms for multi-cloud systems: allocation-aware approach," Information Systems Frontiers, Springer, vol. 21(2), pages 241-259, April.
    8. Chulhwan Chris Bang, 2015. "Information systems frontiers: Keyword analysis and classification," Information Systems Frontiers, Springer, vol. 17(1), pages 217-237, February.

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