A Knowledge-Based Ant Colony Optimization for a Grid Workflow Scheduling Problem

Service-oriented grid environment enables a new way of service provisioning based on utility computing models, where users consume services based on their QoS (Quality of Service) requirements. In such “pay-per-use” Grids, workflow execution cost must be considered during scheduling based on users’ QoS constraints. In this paper, we propose a knowledge-based ant colony optimization algorithm (KBACO) for grid workflow scheduling with consideration of two QoS constraints, deadline and budget. The objective of this algorithm is to find a solution that minimizes execution cost while meeting the deadline in terms of users’ QoS requirements. Based on the characteristics of workflow scheduling, we define pheromone in terms of cost and design a heuristic in terms of latest start time of tasks in workflow applications. Moreover, a knowledge matrix is defined for the ACO approach to integrate the ACO model with knowledge model. Experimental results show that our algorithm achieves solutions effectively and efficiently.