DOTS: Delay-Optimal Task Scheduling Among Voluntary Nodes in Fog Networks

Zhang, Guowei; Shen, Fei; Chen, Nanxi; Zhu, Pengcheng; Dai, Xuewu; Yang, Yang

doi:10.1109/jiot.2018.2887264

Cited by 39 publications

(23 citation statements)

References 31 publications

(41 reference statements)

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“…On the other hand, the authors of [15] proposed an optimization problem solution to minimize the delay when multiple users offload tasks to multiple edge cloud nodes performing complex IoT event processing, such as speech and face recognition. The author of [16] researched a voluntary-node-based task scheduling technique that autonomously determines which resources to allocate to user nodes according to their current resource allowance. This method can effectively increase idle resource utilization.…”

Section: Discussionmentioning

confidence: 99%

Latency-Classification-Based Deadline-Aware Task Offloading Algorithm in Mobile Edge Computing Environments

Choi

Lee

2019

Applied Sciences

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In this study, we consider an edge cloud server in which a lightweight server is placed near a user device for the rapid processing and storage of large amounts of data. For the edge cloud server, we propose a latency classification algorithm based on deadlines and urgency levels (i.e., latency-sensitive and latency-tolerant). Furthermore, we design a task offloading algorithm to reduce the execution time of latency-sensitive tasks without violating deadlines. Unlike prior studies on task offloading or scheduling that have applied no deadlines or task-based deadlines, we focus on a comprehensive deadline-aware task scheduling scheme that performs task offloading by considering the real-time properties of latency-sensitive tasks. Specifically, when a task is offloaded to the edge cloud server due to a lack of resources on the user device, services could be provided without delay by offloading latency-tolerant tasks first, which are presumed to perform relatively important functions. When offloading a task, the type of the task, weight of the task, task size, estimated execution time, and offloading time are considered. By distributing and offloading latency-sensitive tasks as much as possible, the performance degradation of the system can be minimized. Based on experimental performance evaluations, we prove that our latency-based task offloading algorithm achieves a significant execution time reduction compared to previous solutions without incurring deadline violations. Unlike existing research, we applied delays with various network types in the MEC (mobile edge computing) environment for verification, and the experimental result was measured not only by the total response time but also by the cause of the task failure rate.

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Section: Discussionmentioning

confidence: 99%

Latency-Classification-Based Deadline-Aware Task Offloading Algorithm in Mobile Edge Computing Environments

Choi

Lee

2019

Applied Sciences

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“…One of the uses of FC is in the factories, many studies [24–26] research in this field. Yin et al in [27] presented smart manufacturing scheduling to optimise resource utilisation and delay.…”

Section: Related Workmentioning

confidence: 99%

Fault‐tolerant with load balancing scheduling in a fog‐based IoT application

Sharif

Nickray²,

Shahidinejad

2020

IET Communications

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“…In [17], Ali et al studied a cloudlet selection problem to minimize the delay of fog networks which can be converted into a manyto-one matching game, they proposed a distributed and selforganizing resource allocation algorithm to solve it. In [18], Zhang et al established a general analysis framework in a fog network under voluntary mode and proposed a delay-optimal task scheduling algorithm. In [19], Lee et al studied joint fog network formulation and task offloading problems in a hybrid fog-cloud network and proposed a novel online optimization framework to minimize the maximum delay of the fog nodes in the considered network.…”

Section: A Related Workmentioning

confidence: 99%

“…(15) and (16) are the constraints on the user association decision and corresponding weight of the user-centric parameters. (17) and (18) are the constraints on computing resource allocation for each fog node.…”

Section: Problem Formulationmentioning

confidence: 99%

UCAA: User-Centric User Association and Resource Allocation in Fog Computing Networks

et al. 2020

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In recent years, with the eruptive popularity of mobile Internet and the emergence of various new IoT applications, fog computing is proposed to shift the cloud computing services towards the edge, making up for its lack of mobility support and high delay. Fog computing is customized for scenarios with scarce resources and unpredictable environments, but there is no user-centric joint optimization fog computing models designed for such scenarios. In this paper, we aim to maximize the user experience and overall system performance by jointly optimizing user association and resource allocation in the scenarios mentioned above, which can be formulated as a mix-integer non-linear programming problem. To solve the NP-hard problem, we propose a low-complexity two-step interactive optimal algorithm, named UCAA algorithm. For the user association problem, we propose a semi-definite programming based algorithm, and then further propose a Kuhn-Munkres algorithm based user association decision approximation algorithm. For the resource allocation problem, we first prove that it can be decoupled into two sub-problems, ie., transmission power selection problem and computing resource allocation problem, and solve them individually, in addition, we have given a rigorous proof that the optimal solution of the two subproblems is the optimal solution to the original problem as well. The numerical results show that the proposed UCAA algorithm achieves better performance than conventional algorithms in terms of the value of average user-centric utility, especially in case of more user equipments (UEs), fewer fog nodes, limited computing capacity of fog nodes, lower delay tolerance, lower local computation capacity, etc., which presented to illustrate that the UCAA algorithm can significantly improve user experience and system performance in the considering fog computing scenarios.

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DOTS: Delay-Optimal Task Scheduling Among Voluntary Nodes in Fog Networks

Cited by 39 publications

References 31 publications

Latency-Classification-Based Deadline-Aware Task Offloading Algorithm in Mobile Edge Computing Environments

Latency-Classification-Based Deadline-Aware Task Offloading Algorithm in Mobile Edge Computing Environments

Fault‐tolerant with load balancing scheduling in a fog‐based IoT application

UCAA: User-Centric User Association and Resource Allocation in Fog Computing Networks

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