Joint Optimization of Task Scheduling and Image Placement in Fog Computing Supported Software-Defined Embedded System

TLDR

Embedded systems are limited in functionality, flexibility, and scalability, and fog computing—by moving cloud services to the network edge—offers a promising way to enhance them, though resource management remains a critical performance issue. The study aims to design an efficient task scheduling and resource management strategy that minimizes task completion time and improves user experience by addressing workload balancing, image placement, and I/O interrupt distribution. We formulate the joint task scheduling, image placement, and I/O balancing as a mixed‑integer nonlinear programming problem and propose a computation‑efficient solution validated by extensive simulations. The proposed solution demonstrates computational efficiency and effectiveness in simulations, confirming its suitability for fog‑enabled software‑defined embedded systems.

Abstract

Traditional standalone embedded system is limited in their functionality, flexibility, and scalability. Fog computing platform, characterized by pushing the cloud services to the network edge, is a promising solution to support and strengthen traditional embedded system. Resource management is always a critical issue to the system performance. In this paper, we consider a fog computing supported software-defined embedded system, where task images lay in the storage server while computations can be conducted on either embedded device or a computation server. It is significant to design an efficient task scheduling and resource management strategy with minimized task completion time for promoting the user experience. To this end, three issues are investigated in this paper: 1) how to balance the workload on a client device and computation servers, i.e., task scheduling, 2) how to place task images on storage servers, i.e., resource management, and 3) how to balance the I/O interrupt requests among the storage servers. They are jointly considered and formulated as a mixed-integer nonlinear programming problem. To deal with its high computation complexity, a computation-efficient solution is proposed based on our formulation and validated by extensive simulation based studies.