Balanced task allocation in the on-demand computing-based transaction processing system using social spider optimization

Abstract

Balanced task allocation is one of the methods that can be used to maximize the performance and reliability in the on-demand computing-based transaction processing system. On-demand computing is an increasingly popular enterprise model. It provides computing resources to the user as needed, which may be maintained within the user's enterprise, or made available by a service provider. The balanced task allocation in such environment is known to be an NP hard. The reliability is a measure of trustworthiness of the system while executing the task. So we derive the reliability formula for the on-demand computing-based transaction processing system considering resource availability. We propose the balanced task allocation based on social spider optimization (LBTA_SSO) method for this problem. The LBTA_SSO is based on the cooperative behavior of social-spiders to find a collection of task allocation solutions. We modified five existing algorithms to obtain the task allocation algorithms; Honey Bee Optimization (HBO), Ant Colony Optimization (ACO), Hierarchical Load Balanced Algorithm (HLBA), Dynamic and Decentralized Load Balancing (DLB), and Randomized Algorithm respectively. Then, we compared the proposed algorithm with these modified algorithms. The results show that our algorithm works better than the modified existing algorithms. Copyright © 2017 John Wiley & Sons, Ltd.