Delay Minimized Task Scheduling in Fog-Enabled IoT Networks

Zhang, Guowei; Shen, Fei; Zhang, Yueyue; Yang, Rong; Yang, Yang; Jorswieck, Eduard A.

doi:10.1109/wcsp.2018.8555532

Cited by 18 publications

(6 citation statements)

References 13 publications

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“…Therefore, migration is promising are of research in FC. The authors in References 91‐93 have given some solutions in terms of VM framework and policies for balancing resource usage, and fault management, but still there is a considerable scope of improvement. More adaptive and fault‐tolerant load balancing schemes are required which can predict workload for effective resource utilization. Optimal task‐resource pairing: The performance of task scheduling relies heavily on optimal task‐resource pairing where a task is scheduled on a resource‐efficient Fog node.…”

Section: Issues Existing Solutions and Future Directions: A Discussionmentioning

confidence: 99%

A systematic review on task scheduling in Fog computing: Taxonomy, tools, challenges, and future directions

Kaur

Kumar

2021

Concurrency and Computation

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The biggest challenge of task scheduling in Fog computing is to satisfy users' dynamic requirements in real‐time with Fog nodes' limited resource capacities. Fog nodes' heterogeneity and an obligation to complete tasks by the deadline while minimizing cost and energy consumption makes the scheduling process more challenging. This article facilitates a deeper understanding of the research issues through a detailed taxonomy and distinguishes significant challenges in existing work. Furthermore, the paper investigates existing solutions for various challenges, presents a meta‐analysis on quality of service parameters and tools used to implement Fog task scheduling algorithms. This systematic review will help potential researchers easily identify specific research problems and future directions to enhance scheduling efficiency.

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Section: Issues Existing Solutions and Future Directions: A Discussionmentioning

confidence: 99%

A systematic review on task scheduling in Fog computing: Taxonomy, tools, challenges, and future directions

Kaur

Kumar

2021

Concurrency and Computation

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“…Delay-sensitive task is considered. DMTO is proposed to identify the optimal subtask size and the TN transmission power [49].…”

Section: Search Algorithm -Essence Of Optimizationmentioning

confidence: 99%

An Adaptive Task Scheduling in Fog Computing

Harkut¹,

Thakar²,

Mutneja³

2023

Search Algorithm - Essence of Optimization

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Internet applications generate massive amount of data. For processing the data, it is transmitted to cloud. Time-sensitive applications require faster access. However, the limitation with the cloud is the connectivity with the end devices. Fog was developed by Cisco to overcome this limitation. Fog has better connectivity with the end devices, with some limitations. Fog works as intermediate layer between the end devices and the cloud. When providing the quality of service to end users, scheduling plays an important role. Scheduling a task based on the end users requirement is a tedious thing. In this paper, we proposed a cloud-fog task scheduling model, which provides quality of service to end devices with proper security.

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“…M. Mukherjee et al introduced a deadline-aware task scheduling algorithm for fog computing to complete as many tasks as possible before their deadlines [26]. G. Zhang et al proposed a task offloading algorithm for fog computing systems to reduce the service delay [27]. 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].…”

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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Delay Minimized Task Scheduling in Fog-Enabled IoT Networks

Cited by 18 publications

References 13 publications

A systematic review on task scheduling in Fog computing: Taxonomy, tools, challenges, and future directions

A systematic review on task scheduling in Fog computing: Taxonomy, tools, challenges, and future directions

An Adaptive Task Scheduling in Fog Computing

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

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