Achieving Energy Efficiency in Cloud Brokering

TL;DR

This paper proposes a novel approach for cloud brokering that enhances energy efficiency through optimized job assignment and scheduling, significantly reducing energy consumption in large-scale public cloud systems.

Contribution

It introduces a new energy-efficient job scheduling framework for cloud brokers, including problem formulation, complexity analysis, and efficient algorithms for data transfer and job execution phases.

Findings

Significant energy reduction achieved in experiments.

Improved job admission rates in large-scale systems.

Effective scheduling algorithms for energy efficiency.

Abstract

The proliferation of cloud providers has brought substantial interoperability complexity to the public cloud market, in which cloud brokering has been playing an important role. However, energy-related issues for public clouds have not been well addressed in the literature. In this paper, we claim that the broker is also situated in a perfect position where necessary actions can be taken to achieve energy efficiency for public cloud systems, particularly through job assignment and scheduling. We formulate the problem by a mixed integer program and prove its NP-hardness. Based on the complexity analysis, we simplify the problem by introducing admission control on jobs. In the sequel, optimal job assignment can be done straightforwardly and the problem is transformed into improving job admission rate by scheduling on two coupled phases: data transfer and job execution. The two scheduling phases are further decoupled and we develop efficient scheduling algorithm for each of them. Experimental results show that the proposed solution can achieve significant reduction on energy consumption with admission rates improved as well, even in large-scale public cloud systems.