Reliability-Aware: Task Scheduling in Cloud Computing Using Multi-Agent Reinforcement Learning Algor

Reliability-Aware: Task Scheduling in Cloud Computing Using Multi-Agent Reinforcement Learning Algorithm and Neural Fitted Q

Husamelddin Balla, Chen Sheng, and Jing Weipeng

College of Information and Computer Engineering, Northeast Forestry University, China

Abstract: Cloud computing becomes the basic alternative platform for the most users application in the recent years. The complexity increasing in cloud environment due to the continuous development of resources and applications needs a concentrated integrated fault tolerance approach to provide the quality of service. Focusing on reliability enhancement in an environment with dynamic changes such as cloud environment, we developed a multi-agent scheduler using Reinforcement Learning (RL) algorithm and Neural Fitted Q (NFQ) to effectively schedule the user requests. Our approach considers the queue buffer size for each resource by implementing the queue theory to design a queue model in a way that each scheduler agent has its own queue which receives the user requests from the global queue. A central learning agent responsible of learning the output of the scheduler agents and direct those scheduler agents through the feedback claimed from the previous step. The dynamicity problem in cloud environment is managed in our system by employing neural network which supports the reinforcement learning algorithm through a specified function. The numerical result demonstrated an efficiency of our proposed approach and enhanced the reliability.

Keywords: Reinforcement learning, multi-agent scheduler, neural fitted Q, reliability, cloud computing, queuing theory.

Received April 5, 2018; accepted January 28, 2020

https://doi.org/10.34028/iajit/18/1/5
Last modified on Thursday, 24 December 2020 05:39
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