A Learning Automata-based Scheduling for Deadline Sensitive Task in The Cloud

Abstract

Cloud computing is a revolutionary paradigm, which allows applications to run in a virtualized environment. The application runs on a virtual cloud resource makes the system scalable and cost-efficient. Noticeably many applications, such as healthcare systems, video streaming, Internet of Things (IoT) running in the cloud, are real-time in nature, i.e., these applications demand responses within a particular time limit, i.e., deadline. To meet the requirement of such applications, a Cloud Service Provider (CSP) must have a sufficient number of cloud resources (virtual machines). Further, the ever-growing demand for applications forces a CSP to deploy more and more cloud resources. Inevitably, the massive count of cloud resources in a cloud data center consumes a tremendous amount of energy. Specifically, it becomes cumbersome to offer services to deadline-sensitive tasks while minimizing energy consumption. An efficient task scheduling is an attractive way to reduce energy usage while ensuring satisfactory services for cloud users. Learning Automata (LA) is a reinforcement-based adaptive decision-making unit that learns and selects the best action from a set of actions applied in a dynamic environment. Similar to LA, in task scheduling, the best task and virtual machine combinations are chosen from a set of available combinations. In this context, this paper implemented the LA technique to solve a bi-objective deadline-sensitive task scheduling problem which includes minimization of energy consumption and makespan. At first, a learning automata-based scheduling framework is designed for deadline-sensitive tasks in the cloud. Later, a scheduling algorithm, namely, the LA-based Scheduling (LAS) algorithm, is proposed. The LAS algorithm exploits the heterogeneity of tasks and virtual machines (VMs) while guaranteeing the task’s deadline. Extensive simulation is carried out to designate the effectiveness and applicability of LAS for deadline-sensitive task scheduling in the heterogeneous cloud environment.