Multiple Resource Attributes and Conditional Logic Assisted Task Scheduling in Cloud Computing

Abstract

From the past few decades, the cloud computing has been arisen as an extensively used platform to provide storage, compute and analytics services to organizations and end users on the basis of pay-as-you-use. This enabled the organizations as well as individuals to access the larger set of resources without establishing a costly and high performance computing platform. However, the major issue in cloud computing is the task scheduling which is declared as NP-hard problem and most of the researchers applied meta-heuristic algorithms to solve it. However, they experienced a slower convergence speed which has a direct impact on the efficiency of cloud computing environment. To achieve a faster convergence along with efficient quality of Service, this paper proposes a simple and effective task scheduling mechanism based on multiple resource attributes and conditional logic. This method considers totally four resource attributes such as Resource reaction time resource location, resource availability and resource reliability rate. Based on these four attributes, the proposed method constructs an index called as task scheduling index (TSI) and assigns tasks for resources based on their TSI value. The TSI is constructed through the proposed conditional logic and high priority is given for resources those have higher TSI. For experimental validation, we used two benchmark datasets such as GOCJ and synthetic dataset. Three performance metrics namely Makespan, Throughput and convergence speed are measured and compared with state-of-the-art methods like PSO, GA and GWO. On an average, the reduced Makespan of proposed method is 48.22% and 46.87% for GOCJ and Synthetic datasets respectively.

Utilizing Probability Distribution for Selecting Optimal and Minimal Replicas to Achieving Fault Tolerance in a Distributed System

“…User interface: This interface allows users to submit their tasks to the network. The received tasks are then directed to the available network resources [22][23][24][25].…”

“…First, calculate the LH (using formula 1) for each server and neglect the servers that have more than medium. (according to Microsoft [25] CPU utilization < = 66% and memory utilization < = 62%).…”