Energy Efficient Priority-Based Task Scheduling for Computation Offloading in Fog Computing

TL;DR

This paper introduces PIER, a scalable priority-based scheduling policy for fog computing that optimizes energy efficiency in IoT task offloading, demonstrating significant improvements over benchmarks through extensive simulations.

Contribution

The paper proposes PIER, a novel priority-based index policy for energy-efficient task scheduling in fog computing, with proven asymptotic optimality in specific cases and superior performance in general scenarios.

Findings

PIER outperforms benchmark policies in over 78.6% of simulations.

PIER is asymptotically optimal for high-volume homogeneous tasks.

Extensive simulations confirm PIER's robustness across different task distributions.

Abstract

Fog computing offers a flexible solution for computational offloading for Internet of Things (IoT) services at the edge of wireless networks. It serves as a complement to traditional cloud computing, which is not cost-efficient for most offloaded tasks in IoT applications involving small-to-medium levels of computing tasks. Given the heterogeneity of tasks and resources in fog computing, it is vital to offload each task to an appropriate destination to fully utilize the potential benefit of this promising technology. In this paper, we propose a scalable priority-based index policy, referred to as the Prioritized Incremental Energy Rate (PIER), to optimize the energy efficiency of the network. We demonstrate that PIER is asymptotically optimal in a special case applicable for local areas with high volumes of homogeneous offloaded tasks and exponentially distributed task durations. In more general cases with statistically different offloaded tasks, we further demonstrate the improvement of PIER over benchmark policies in terms of energy efficiency and the robustness of PIER to different task duration distributions by extensive simulations. Our results show that PIER can perform better than benchmark policies in more than 78.6% of all simulation runs.