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Machine Learning Failure-Aware Scheme for Profit Maximization in the Cloud Market

Author

Listed:
  • Bashar Igried

    (Department of Computer Science and Applications, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, Zarqa 13133, Jordan)

  • Atalla Fahed Al-Serhan

    (Department of Business Administration, Al-Bayt University, Al-Mafraq 25113, Jordan)

  • Ayoub Alsarhan

    (Department of Information Technology, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, Zarqa 13133, Jordan)

  • Mohammad Aljaidi

    (Department of Computer Science, Zarqa University, Zarqa 13110, Jordan)

  • Amjad Aldweesh

    (College of Computing and Information Technology, Shaqra University, Riyadh 11911, Saudi Arabia)

Abstract

A successful cloud trading system requires suitable financial incentives for all parties involved. Cloud providers in the cloud market provide computing services to clients in order to perform their tasks and earn extra money. Unfortunately, the applications in the cloud are prone to failure for several reasons. Cloud service providers are responsible for managing the availability of scheduled computing tasks in order to provide high-level quality of service for their customers. However, the cloud market is extremely heterogeneous and distributed, making resource management a challenging problem. Protecting tasks against failure is a challenging and non-trivial mission due to the dynamic, heterogeneous, and largely distributed structure of the cloud environment. The existing works in the literature focus on task failure prediction and neglect the remedial (post) actions. To address these challenges, this paper suggests a fault-tolerant resource management scheme for the cloud computing market in which the optimal amount of computing resources is extracted at each system epoch to replace failed machines. When a cloud service provider detects a malfunctioning machine, they transfer the associated work to new machinery.

Suggested Citation

  • Bashar Igried & Atalla Fahed Al-Serhan & Ayoub Alsarhan & Mohammad Aljaidi & Amjad Aldweesh, 2022. "Machine Learning Failure-Aware Scheme for Profit Maximization in the Cloud Market," Future Internet, MDPI, vol. 15(1), pages 1-11, December.
  • Handle: RePEc:gam:jftint:v:15:y:2022:i:1:p:1-:d:1008919
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