MTD-DHJS: Makespan-Optimized Task Scheduling Algorithm for Cloud Computing with Dynamic Computational Time Prediction

Abstract

Cloud computing has revolutionized the management and analysis of data for organizations, offering scalability, flexibility, and cost-effectiveness. Effective task scheduling in cloud systems is crucial to optimize resource utilization and ensure timely job completion. This research presents a novel method for job scheduling in cloud computing, employing the Johnson Sequencing algorithm across three servers. Originally developed for scheduling tasks in a manufacturing context, the Johnson Sequencing method has proven successful in resolving task scheduling challenges. Here, we adapt this method to address job scheduling among three servers within a cloud computing environment. The primary objective of the algorithm is to minimize the makespan, representing the total time required to complete all tasks. This study considers a scenario where a diverse set of jobs, each with varying processing durations, needs to be distributed across three servers using the Johnson Sequencing method. The algorithm strategically determines the optimal order for task execution on each server while accounting for job interdependencies and processing times on the individual servers. To put the Johnson Sequencing algorithm into practice for cloud computing job scheduling, we propose a three-step approach. First, we construct a precedence graph by analyzing the relationships among jobs. Subsequently, the precedence graph is transformed into a two-machine Johnson Sequencing problem by allocating jobs to servers. Finally, we employ the Dynamic Heuristic Johnson Sequencing method to determine the best order of jobs on each server, effectively minimizing the makespan. Through comprehensive simulations and testing, we compare the performance of our suggested Dynamic Heuristic Johnson Sequencing technique with existing scheduling algorithms. The results demonstrate significant improvements in terms of makespan reduction and resource utilization when employing our proposed method with three servers. Furthermore, our approach exhibits remarkable scalability and effectiveness in resolving complex job scheduling challenges within cloud computing settings. The outcomes of this research contribute to the optimization of resource allocation and task management in cloud systems, offering potential benefits to a wide range of industries and applications.