A Heuristic Task Scheduling Algorithm for Heterogeneous Virtual Clusters

Abstract EN

Cloud computing provides on-demand computing and storage services with high performance and high scalability.

However, the rising energy consumption of cloud data centers has become a prominent problem.

In this paper, we first introduce an energy-aware framework for task scheduling in virtual clusters.

The framework consists of a task resource requirements prediction module, an energy estimate module, and a scheduler with a task buffer.

Secondly, based on this framework, we propose a virtual machine power efficiency-aware greedy scheduling algorithm (VPEGS).

As a heuristic algorithm, VPEGS estimates task energy by considering factors including task resource demands, VM power efficiency, and server workload before scheduling tasks in a greedy manner.

We simulated a heterogeneous VM cluster and conducted experiment to evaluate the effectiveness of VPEGS.

Simulation results show that VPEGS effectively reduced total energy consumption by more than 20% without producing large scheduling overheads.

With the similar heuristic ideology, it outperformed Min-Min and RASA with respect to energy saving by about 29% and 28%, respectively.