Chaotic improved PICEA-g-based multi-objective optimization for workflow scheduling in cloud environment

Abstract Mapping of workflow tasks to computational resources in the cloud environment has engendered research interest in workflow scheduling. As workflow scheduling belongs to NP-complete problem, so building an optimum workflow scheduler with reasonable performance and computation speed is very challenging in the heterogeneous distributed environment of clouds. Many existing studies deal with cloud workflow scheduling as a single or bi-objective optimization problem without considering some important requirements of the users or the providers. Therefore, it is highly desirable to formulate scheduling of the workflow applications as a Multi-objective Optimization Problem (MOP) taking into account the requirements from the user and the service provider. For example, the cloud workflow scheduler might wish to consider user’s Quality of Service (QoS) objectives, such as makespan and cost, as well as provider’s objectives, such as energy efficiency over the Virtual Machines (VMs). In addition, early convergence in existing algorithms is a problem that increases the number of repetitions for reaching a global optimum. To overcome these drawbacks, in this paper, an enhanced multi-objective co-evolutionary algorithm, called ch-PICEA-g, is proposed as an effective heuristic algorithm, where the logistic and tent maps as two chaotic systems are applied in generating chaotic values to overcome the permute convergence in the initial population and the genetic operators. Also, an improved fitness function is applied to increase the performance of original PICEA-g. The functionality of the proposed algorithm is validated by extensive experiments. The obtained results indicate that this proposed algorithm outperforms its counterparts in terms of different performance metrics.