An Optimal Task Scheduling Towards Minimized Cost and Response Time in Fog Computing Infrastructure

Apat, Hemant Kumar

doi:10.1109/icit48102.2019.00035

Cited by 14 publications

(8 citation statements)

References 24 publications

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“…To improve the response time of tasks, the method proposed by Apat et al (2019) iteratively assigned the task with the least slack time to the edge server closest to the user. Tasks are assigned to the cloud when they cannot be finished by edges.…”

Section: Related Workmentioning

confidence: 99%

“…Task scheduling or offloading is an effective way for optimizing the task performance and the resource efficiency for DE3C, which decides the location (the corresponding device, an edge or a cloud) where each task to be processed (offloading decision) and the computing resources which each task performs on in a specified order (task assignment and ordering) ( Wang et al, 2020b ; Islam et al, 2021 ). Therefore, several works have proposed various task scheduling methods trying to optimize the response time ( Han et al, 2019 ; Meng et al, 2019 ; Meng et al, 2020 ; Apat et al, 2019 ; Ren et al, 2019 ; Liu et al, 2019a ; Wang et al, 2021 ), the resource cost ( Mahmud et al, 2020 ; Gao et al, 2019 ; Chen et al, 2019 ) or the profit ( Chen et al, 2020 ; Yuan & Zhou, in press ) for providing services in DE3C. These works were concerned on addressing only one or two sub-problems of task scheduling, e.g.…”

Section: Introductionmentioning

confidence: 99%

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A three-stage heuristic task scheduling for optimizing the service level agreement satisfaction in device-edge-cloud cooperative computing

Sang

Cheng²,

Wang

et al. 2022

PeerJ Computer Science

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Device-edge-cloud cooperative computing is increasingly popular as it can effectively address the problem of the resource scarcity of user devices. It is one of the most challenging issues to improve the resource efficiency by task scheduling in such computing environments. Existing works used limited resources of devices and edge servers in preference, which can lead to not full use of the abundance of cloud resources. This article studies the task scheduling problem to optimize the service level agreement satisfaction in terms of the number of tasks whose hard-deadlines are met for device-edge-cloud cooperative computing. This article first formulates the problem into a binary nonlinear programming, and then proposes a heuristic scheduling method with three stages to solve the problem in polynomial time. The first stage is trying to fully exploit the abundant cloud resources, by pre-scheduling user tasks in the resource priority order of clouds, edge servers, and local devices. In the second stage, the proposed heuristic method reschedules some tasks from edges to devices, to provide more available shared edge resources for other tasks cannot be completed locally, and schedules these tasks to edge servers. At the last stage, our method reschedules as many tasks as possible from clouds to edges or devices, to improve the resource cost. Experiment results show that our method has up to 59% better performance in service level agreement satisfaction without decreasing the resource efficiency, compared with eight of classical methods and state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A three-stage heuristic task scheduling for optimizing the service level agreement satisfaction in device-edge-cloud cooperative computing

Sang

Cheng²,

Wang

et al. 2022

PeerJ Computer Science

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“…G. Zhang et al put forward a fog task offloading algorithm named DOTS to decrease latency with the help of the voluntary nodes (VNs) [28]. H. Apat et al proposed a three-layered priority-based fog task scheduling model to meet the different deadline requirement of tasks [29].…”

Section: Related Workmentioning

confidence: 99%

Blockchain-Enhanced Fair Task Scheduling for Cloud-Fog-Edge Coordination Environments: Model and Algorithm

Cao

et al. 2021

Security and Communication Networks

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The cloud-fog-edge hybrid system is the evolution of the traditional centralized cloud computing model. Through the combination of different levels of resources, it is able to handle service requests from terminal users with a lower latency. However, it is accompanied by greater uncertainty, unreliability, and instability due to the decentralization and regionalization of service processing, as well as the unreasonable and unfairness in resource allocation, task scheduling, and coordination, caused by the autonomy of node distribution. Therefore, this paper introduces blockchain technology to construct a trust-enabled interaction framework in a cloud-fog-edge environment, and through a double-chain structure, it improves the reliability and verifiability of task processing without a big management overhead. Furthermore, in order to fully consider the reasonability and load balance in service coordination and task scheduling, Berger’s model and the conception of service justice are introduced to perform reasonable matching of tasks and resources. We have developed a trust-based cloud-fog-edge service simulation system based on iFogsim, and through a large number of experiments, the performance of the proposed model is verified in terms of makespan, scheduling success rate, latency, and user satisfaction with some classical scheduling models.

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“…(1) Initialize random order set: a random order assignment vector x, velocity vector v and frequency of each order Γ, order signal strength G, order signal pulse rate z. In addition, I has a random variable η ∈ (0, 1); (2) Use formula (7) to calculate total energy cost ξ;…”

Section: Retrieval Efficiency Evaluationmentioning

confidence: 99%

“…It is better than retrieval based only on cloud services. erefore, fog computing based IoMT emerges [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Privacy‐Preserving Efficient Data Retrieval in IoMT Based on Low‐Cost Fog Computing

Wang

Cai

et al. 2021

Complexity

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The rapid development of Internet of Medical Things (IoMT) is remarkable. However, IoMT faces many problems including privacy disclosure, long delay of service orders, low retrieval efficiency of medical data, and high energy cost of fog computing. For these, this paper proposes a data privacy protection and efficient retrieval scheme for IoMT based on low-cost fog computing. First, a fog computing system is located between a cloud server and medical workers, for processing data retrieval requests of medical workers and orders for controlling medical devices. Simultaneously, it preprocesses physiological data of patients uploaded by IoMT, collates them into various data sets, and transmits them to medical institutions in this way. It makes the entire execution process of low latency and efficient. Second, multidimensional physiological data are of great value, and we use ciphertext retrieval to protect privacy of patient data in this paper. In addition, this paper uses range tree to build an index for storing physiological data vectors, and meanwhile a range retrieval method is also proposed to improve data search efficiency. Finally, bat algorithm (BA) is designed to allocate cost on a fog server group for significant energy cost reduction. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme.

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An Optimal Task Scheduling Towards Minimized Cost and Response Time in Fog Computing Infrastructure

Cited by 14 publications

References 24 publications

A three-stage heuristic task scheduling for optimizing the service level agreement satisfaction in device-edge-cloud cooperative computing

A three-stage heuristic task scheduling for optimizing the service level agreement satisfaction in device-edge-cloud cooperative computing

Blockchain-Enhanced Fair Task Scheduling for Cloud-Fog-Edge Coordination Environments: Model and Algorithm

Privacy‐Preserving Efficient Data Retrieval in IoMT Based on Low‐Cost Fog Computing

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