An effective meta-heuristic based multi-objective hybrid optimization method for workflow scheduling in cloud computing environment

Cloud computing is an emerging distributed computing model that offers computational capability over internet. Cloud provides a huge level collection of powerful and scalable computational resources for computation and data-intensive large scale workflow deployment. For business as well as scientific applications, optimal scheduling of workflow is emerged as a major concern. Optimization of scheduling process leads to the reduction of execution time, cost, etc. So, this paper presents an enhanced recent ant-lion optimization (ALO) algorithm hybridized with popular particle swarm optimization (PSO) algorithm to optimize a workflow scheduling specifically for cloud. A security approach called Data Encryption Standard (DES) is used for encoding the cloud information while scheduling is carried out. The research aims to contribute an enhanced workflow scheduling more safely than the existing frameworks. Enhancement procedures are evaluated in terms of cost, load, and makespan. The simulation procedures are done by utilizing the CloudSim tool. The proposed hybrid optimization results contrasted with well-known existing approaches. The existing round-robin (RR), ALO and PSO methods are selected to compare and identify the potency of the proposed system. The outcomes indicated that the proposed technique minimizes the cost by 9.8% of GA-PSO, 10% of PSO, 20% of ALO, 30% of RR and 12% of GA. Load balancing and makespan of the proposed method reduces by 8% than GA-PSO, 10% than ALO, 20% than PSO, 35% than RR and 45% than GA. The energy consumption and reliability performance are also reasonably well.