An evolutionary approach to schedule deadline constrained bag of tasks in a cloud

Abstract

Bag of tasks (BoT) is an application model which consists of a large number of independent tasks. In cloud, computing power is offered as virtual machines (VMs) which differ in terms of speed, memory and cost. When such applications are executed on cloud, an optimal allocation of VMs is needed so that the application executes to completion within the deadline and the cost incurred is minimal. Here, the main challenge is to find an optimal trade-off between execution time and execution cost. Genetic algorithms (GA) are evolutionary algorithms which enable to solve multi-objective problems. This paper proposes a novel deadline constrained bi-objective genetic algorithm based scheduler (DBOGA) to schedule a BoT application onto a cloud. A new fitness function is defined. Exploration and exploitation of search space is carried out based on this. An extensive study on the applicability of DBOGA by considering various scenarios is explored.