A cost effective‐ secure algorithm for work‐flow scheduling in cloud computing

Abstract

The work‐flow is emerging as the promising paradigm for the different computing infrastructures of distributed nature. It is applied widely in various scientific fields and simple job execution. To manage with the enormous flow of data in the work‐flow, the requirement of huge infrastructure and the execution in the reasonable time. The work ‐flow adapts to the cloud environment. Though cloud environment provides with the large‐scale infrastructure and consistency and efficiency on comparing to the system operated over non‐cloud, a proper scheduling is entailed for the execution of tasks satisfying the specified constraints with the necessary security measure that has to be provided as the cloud does not provide with the counter measures for the security threats. More over as the real world tasks are most probably heterogeneous requiring virtual machines of different instance of series. So the paper proffers a cost‐effective algorithm based on the multi‐populated genetic algorithm (Multi‐populated GA) implanted with particle swarm optimization (PSO) for the heterogeneous networks so as to minimize the cost of the execution and meet out the QOS constraints, the results serve as the guiding principle for the security level improvement for the (sensitive) critical tasks. The performance evaluation of the scheduling using the workflowsim is performed to evidence the maximum reduction in the cost of the execution and the QOS constraints met using the proposed system.