Work in Progress: Topology-based Multilevel Algorithm for Large-scale Task Scheduling in Clouds

Abstract

Task scheduling in cloud environments is the problem of assigning and executing computational tasks on the available cloud resources. Effective task scheduling can improve processor utilization, reduce processor energy consumption, and improve user experience. Large-scale task scheduling under multiple constraints is an NP-complete problem. The traditional task scheduling algorithm cannot be applied to large-scale scheduling, either because of high time complexity or because its heuristic algorithm cannot be applied to complexly large-scale scenarios. The problem of large-scale task scheduling is gradually becoming a challenge in cloud computing. The article proposes a topology-based multilevel algorithm for large-scale task scheduling in clouds. Based on the topological order of the graph, multi-level coarsening is performed on the large-scale graphs, and then uses the traditional scheduling algorithm for the initial scheduling of the coarse graph, and then refine the initial scheduling result during its uncoarsen phrase. It can perform fast and efficient scheduling of large-scale task graphs. At the same time, it has good compatibility, which can be combined with excellent traditional scheduling algorithms.