Reinforcement Learning to Improve Resource Scheduling and Load Balancing in Cloud Computing

• Published in: SN computer science 2023-01, Vol.4 (2), p.188, Article 188
• Main Authors: Kaveri, Parag Ravikant, Lahande, Prathamesh
• Format: Article
• Language: English
• Subjects: Advances in Computational Approaches for Image Processing; Algorithms; Cloud Applications and Network Security; Cloud computing; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data Structures and Information Theory; Empirical analysis; Experiments; Information Systems and Communication Service; Intelligence; Literature reviews; Load balancing; Original Research; Pattern Recognition and Graphics; Regression analysis; Regression models; Resource scheduling; Scheduling; Software Engineering/Programming and Operating Systems; Task scheduling; Taskload; Virtual environments; Vision; Wireless Networks
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-022-01609-9

Cloud computing provides various services to the end-user by processing a high number of tasks using the Internet. The end-user submits this high number of tasks to the cloud for execution. The cloud processes and executes these tasks on the cloud Virtual Machines (VM) using resource scheduling algorithms and performing load-balancing mechanisms. The cloud performance is directly proportional to how the resources are scheduled and how the load is managed. With proper resource scheduling and load balancing, the cloud performance is enhanced, and it can execute a more significant number of tasks. Similarly, the cloud performance is hampered by poor resource scheduling as well as load misbalancing. Therefore, it becomes essential for the cloud to schedule its resources and manage its load in an appropriate way to provide proper Quality of Service (QoS) without any infractions in the Service Level Agreements (SLA). With static resource scheduling, managing the resources and balancing the load becomes challenging while executing tasks, especially when the cloud system has been given no intelligence. Resource scheduling and load balancing become complex without any intelligence to keep a smooth flow of task execution, irrespective of the task load. The main objective of this research paper is to study and compare the behavior of resource scheduling algorithms by executing tasks of different loads under different scenarios and circumstances. This research paper is broadly divided into three phases: the first phase includes a simulation experiment conducted on the WorkflowSim environment where tasks are processed and executed on VMs in four different scenarios and circumstances; the second phase includes a detailed empirical analysis of the results obtained from the experiment conducted in the first phase using the mathematical model of Linear Regression and R 2 analysis; the last part proposes reinforcement learning (RL) to provide intelligence and improve the resource scheduling and load-balancing mechanisms in the cloud computing environment.