Deadline and Cost Aware Dynamic Task Scheduling in Cloud Computing Based on Stackelberg Game

Abstract

Cloud computing has become an essential technology in many industries due to its scalability and costeffectiveness. The dynamic nature of cloud computing, including elasticity, on-demand provisioning, diverse resource types, and varied pricing model, presents a significant challenge in scheduling tasks for cloud-based systems, especially when considering user quality of service (QoS) constraints such as deadlines and budgets. Therefore, in order to optimize the performance of the cloud systems and end-user satisfaction, an efficient budget and deadline-aware scheduling model is necessary. Game theory provides a framework for modeling and analyzing the strategic interactions between self-interested entities, which makes it an ideal tool for task scheduling in cloud computing. Additionally, the versatility of game models enables the analysis of various cloud computing architectures. This paper proposes a dynamic Stackelberg (leader-follower) game model for modeling the interactions between tasks, scheduler, and cloud resources to find an equilibrium for the game under both budget and deadline constraints. The proposed dynamic task scheduling based on Stackelberg game (DTSSG) model is assisted by the pricing model and satisfaction factors to select the optimal virtual machine for processing the user task. To achieve high average resource utilization, the utilization factor of the cloud resources is considered in the proposed work. Experimental results show that the Stackelberg model equilibrium has been very effective in scheduling the user tasks across the data center resources by selecting the optimal virtual machines. The results demonstrate improved execution efficiency in terms of decreased makespan by 30%, reduced number of deadline violations by 52%, decreased total gain cost by 27.13% and increased provider profit by 19.15 % on average as compared to existing deadline budget scheduling (DBS), genetic algorithm, and MAX-MIN methods. Also, the results show the effectiveness of the proposed work in terms of increased throughput by 59.4 % and decreased makespan by 27.95% using Google cloud jobs dataset (GoCJ) as compared to existing gradient-based optimization (GBO), multi-verse optimizer (MVO), enhanced multi-verse optimizer (EMVO).

Task Scheduling Optimization in Cloud-Fog-MCS Environment Using Genetic Algorithm and Game Theory

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