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
