Energy-efficient Job Stealing for CPU-intensive processing in Mobile Devices

Abstract

Mobile devices have evolved from simple electronic agendas and mobile phones to small computers with great computational capabilities. In addition, there are more than 2 billion mobile devices around the world. Taking these facts into account, mobile devices are a potential source of computational resources for clusters and computational Grids. In this work, we present an analysis of different schedulers based on job stealing for mobile computational Grids. These job stealing techniques have been designed to consider energy consumption and battery status. As a result of this work, we present empirical evidence showing that energy-aware job stealing is more efficient than traditional random stealing in this context. In particular, our results show that mobile Grids using energy-aware job stealing might finish up to 11% more jobs than when using random stealing, and up to 24% more jobs than when not using any job stealing technique. This means that using energy-aware job stealing increases the energy efficiency of mobile computational Grids because it increases the number of jobs that can be executed using the same amount of energy.