Deadline Constrained Cloud Computing Resources Scheduling Through An Ant Colony System Approach

Abstract

Cloud computing resources scheduling is essential for executing workflows in the cloud platform because it relates to both the execution time and execution costs. In solving the problem of optimizing the execution costs while meeting deadline constraints, we developed an efficient approach based on ant colony system (ACS). For scheduling T tasks on R resources, an ant in ACS represents a solution with T dimensions, with each dimension being a task and the value of each dimension being an integer ranges in [1, R] to indicate scheduling the task on which resource. With such solution encoding, the ant in ACS constructs a solution in T steps, with each step optimally selecting one resource from the R resources, according to both the pheromone and heuristic information. Therefore, the solution encoding is very simple and straight to reflect the mapping relation of tasks and resources. Moreover, the solution construct process is very natural to find optimal solution based on the encoding scheme. We have conducted extensive experiments based on workflows with various scales and various cloud resources. We compare the results with those of particle swarm optimization (PSO) and dynamic objective genetic algorithm (DOGA) approaches. The experimental results show that ACS is able to find better solutions with a lower cost than both PSO and DOGA do on various scheduling scales and deadline conditions.