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

By Deze Zeng, Lin Gu, Song Guo, Zixue Cheng, Shui Yu. IEEE transactions on computers, 2016

391 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/3a24b88f19af28b59fc799685202401291ee2bfb

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Citations in the corpus

Fog Computing: A Taxonomy, Survey and Future Directions (2016, background): “[20]) and can be changed according to the existing load.”
Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study (2017, background): “However, how to enforce device cooperation through the dynamic setup and self-management of suitable inter-thing “social” networks is still a big challenge [3], [13], [23], [24].”
A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges (2017, methodology): “The problem of task scheduling with a minimum latency is studied by Agarwal et al. [25], Oueis et al. [84], and Zeng et al. [87].”
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey (2018, background): “The problem of task scheduling and dispatching in edge clouds has been studied extensively by several researchers in the area [231, 262, 288, 303, 311].”
An Overview of Service Placement Problem in Fog and Edge Computing (2020, methodology): “C/On/Dy/M Heuristic [142] Minimizes the cost of execution, delays, and location constraints .”
The Internet of Things, Fog and Cloud Continuum: Integration and Challenges (2018, background): “input has been carried out in the last few years [55, 2].”
Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges (2018, background): “3) Others: Furthermore, a resource management and task image placement are considered in fog computing-based software-defined embedded system [133].”
Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers (2018, neutral): “[126] proposed a fog computing supported software-”