A new meta‐heuristic approach for load aware‐cost effective workflow scheduling

Abstract

AbstractWorkflow scheduling is an important way to manage the execution of a workflow. It introduces the concept of providing suitable resources to workflow tasks in order to finish workflow execution and meet the user's objectives. However, the problem becomes more complex when scheduling must balance two conflicting objectives, such as minimizing execution cost and maximizing load across all computing resources. A workflow has many interdependent tasks, and the cloud datacenter has many computing resources to execute the workflow. There can be an asymptotically infinite number of mappings of tasks‐to‐computing resources. Every mapping produces different execution costs with different workloads on computing resources. The main challenge for the researcher is to develop an intelligent scheduling algorithm to identify an optimal mapping that produces minimal execution cost with fair workload distribution on resources. We developed a novel meta‐heuristic algorithm named Investment‐Based Optimization (IBO) to identify an optimal mapping. The IBO algorithm was first tested on optimization benchmark functions and then simulated in CloudSim to see its performance for scheduling workflows. Finally, IBO was tested over Montage, Epigenomics, Sipht, and a sample workflow, and it was found that IBO reduces execution costs by 33%, 16%, 16.36%, and 20% with a fair workload distribution.