Energy-Efficient and QoS-Optimized Adaptive Task Scheduling and Management in Clouds

Abstract

The enormous energy consumed by clouds becomes a significant challenge for cloud providers and smart grid operators. Due to performance concerns, applications typically run in different clouds located in multiple sites. In different clouds, many factors, including electricity prices, available servers, and task service rates, exhibit spatial variations. Therefore, it is important to manage and schedule tasks among multiple clouds in a high-quality-of-service and low-energy-cost manner. This work proposes a task scheduling method to jointly minimize energy cost and average task loss possibility (ATLP) of clouds. A problem is formulated and tackled with an adaptive biobjective differential evolution based on simulated annealing to determine a real-time and near-optimal set of solutions. A final knee solution is further chosen to specify suitable servers in clouds and task allocation among web portals. Simulation results based on realistic data prove that less average loss possibility of tasks, and smaller energy cost is obtained with it than its widely used peers. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work considers joint optimization of both ATLP and average energy cost of all clouds. It is of great significance to execute tasks among multiple clouds by jointly allocating all tasks among multiple web portals and specifying suitable servers in different clouds. Yet, it is challenging to achieve joint optimization in a market where factors, including prices of electricity and available servers, show spatial variations. Current studies are coarse-grained and fail to jointly achieve average energy cost minimization and quality-of-service optimization of tasks. In this work, a novel algorithm named adaptive simulated-annealing-based biobjective differential evolution is proposed for an energy cost and quality-of-service-optimized task scheduling strategy in a real-time manner. Experiments prove that it realizes lower energy cost and ATLP compared with its typical widely used peers. It can also be applied to other industrial areas, including smart manufacturing, Internet of Things, and smart city.