Scheduling of Dependent Tasks Application using Random Search Technique
Deepak.c.vegda, Harshad.B.Prajapati

TL;DR
This paper explores using Genetic Algorithms to efficiently schedule dependent tasks in grid computing environments, aiming to minimize make-span despite the NP-complete nature of the problem.
Contribution
It introduces a GA-based scheduling method with specific operators and initial population strategies tailored for dependent tasks in heterogeneous grid resources.
Findings
GA can generate schedules in reasonable time
SimGrid supports DAG applications effectively
Scheduling minimizes make-span with heuristic approach
Abstract
Since beginning of Grid computing, scheduling of dependent tasks application has attracted attention of researchers due to NP-Complete nature of the problem. In Grid environment, scheduling is deciding about assignment of tasks to available resources. Scheduling in Grid is challenging when the tasks have dependencies and resources are heterogeneous. The main objective in scheduling of dependent tasks is minimizing make-span. Due to NP-complete nature of scheduling problem, exact solutions cannot generate schedule efficiently. Therefore, researchers apply heuristic or random search techniques to get optimal or near to optimal solution of such problems. In this paper, we show how Genetic Algorithm can be used to solve dependent task scheduling problem. We describe how initial population can be generated using random assignment and height based approaches. We also present design of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
