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Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi

Sudheer Mangalampalli1, Ganesh Reddy Karri1, Sachi Nandan Mohanty1

  • 1School of Computer Science and Engineering, VIT-AP University, Amaravati, AP, 522237, India.

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Summary

This study introduces a novel task scheduling algorithm for cloud computing, combining Harris Hawk optimization and deep reinforcement learning. The algorithm enhances reliability and trust by minimizing failures and improving service level agreement parameters.

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Area of Science:

  • Cloud Computing
  • Artificial Intelligence
  • Task Scheduling Algorithms

Background:

  • Cloud computing offers on-demand services but faces single points of failure due to improper task-to-virtual machine assignments.
  • These failures negatively impact Service Level Agreement (SLA) parameters like availability and success rate, eroding trust in cloud providers.

Purpose of the Study:

  • To propose an advanced task scheduling algorithm to mitigate failures in cloud environments.
  • To enhance the reliability and trust in cloud services by optimizing task assignments and resource utilization.

Main Methods:

  • A hybrid task scheduling algorithm combining Harris Hawk optimization and Deep Q-Network (DQN) based deep reinforcement learning.
  • A two-phase approach: task selection using Harris Hawk optimization and task mapping optimized by DQN.
  • Utilized a multi-cloud environment for dynamic VM availability and task migration to reduce latency.

Main Results:

  • The proposed FTTHDRL algorithm demonstrated superior performance compared to existing methods (MOABCQ, RATS-HM, AINN-BPSO).
  • Significant reduction in failure rates and resource costs.
  • Marked improvement in SLA-based trust parameters, including availability, success rate, and turnaround efficiency.

Conclusions:

  • The hybrid Harris Hawk optimization and deep reinforcement learning approach effectively addresses task scheduling challenges in cloud computing.
  • The FTTHDRL algorithm enhances cloud service reliability, reduces operational costs, and boosts customer trust through improved SLA adherence.