Immune Scheduling Network Based Method for Task Scheduling in Decentralized Fog Computing

Wang, Yabin; Guo, Chenghao; Yu, Jin Ming

doi:10.1155/2018/2734219

Cited by 19 publications

(18 citation statements)

References 18 publications

(24 reference statements)

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“…The subdivision mechanism of tasks can be further improved by making each task/subtask more independent and loosely coupled in order to run these smoothly on multiple Fog nodes with minimum communication requirements. Balancing resource usage: The optimal use of resources is one of the challenges of task scheduling in the Fog environment because of limited computational capacity of Fog nodes, dynamically changing user requirements and mobility of Fog nodes. Inefficient use of resources may have a severe impact on QoS 9,31,45,54 . Migration of VMs is generally used for balancing load among Fog nodes that consequently improves the utilization of resources, but it introduces network overhead 90 .…”

Section: Issues Existing Solutions and Future Directions: A Discussionmentioning

confidence: 99%

“…Due to these issues, task scheduling in FC had witnessed substantial attention of the researchers. For instance, many researchers proposed task scheduling methods considering various parameters such as execution time, 8‐11 response time, 12‐17 deadline, 18‐23 cost, 14,15,22,24‐29 energy, 12,30‐38 security, 20,23,26,39,40 load balancing, 9,18,31,40 resource reallocation, 21 and optimal task‐resource pairing 24,39,41,42 . Although, a lot of research has already been carried out in task scheduling, but still there is a scope for further improvement.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al 9 focused on dealing with overloaded Fog nodes and balancing the network with immune network theory (INT). The idea of INT is applied to FC network so that a FN can learn about its the adjacent FNs in case of overloading or failure.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Experimental results demonstrate that the proposed algorithm outperforms BA in terms of time-cost tradeoff. Wang et al [111] studied an immune scheduling network-based method for task scheduling in FC. The proposed method uses forward and backward propagation in the ad hoc network along with the power of distributed schedulers to generate the optimized scheduler strategies to deal with computing node overloaded and achieve the optimal task finishing time reducing.…”

Section: A Eas In CC and Ecmentioning

confidence: 99%

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Asim

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users' requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This paper provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.

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“…In another study, Wang et al 77 presented a decentralized algorithm for task scheduling in the fog computing environment using the immune mechanism of the human body. The network is considered as a framework including a set of computing nodes that make independent scheduling decisions and regularly cooperate for synchronization, transfer of network data, and the computing flow of nodes.…”

Section: Organization Of the Task Schedulingmentioning

confidence: 99%

Task scheduling approaches in fog computing: A systematic review

Alizadeh

Khajehvand

Rahmani

et al. 2020

Int J Communication

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SummaryThe Internet of Things (IoT) interconnects billions of physical objects to collect and exchange information and makes available various applications. Despite all the advantages of the IoT, some of its applications are not feasible because of the existing restrictions in the IoT sensors. To overcome these restrictions, cloud computing has been developed in recent years. Although cloud computing has removed some of these restrictions, the cloud itself has faced some other challenges. One of these challenges is the distance between the cloud and the end‐devices, which is not ideal for delay‐sensitive applications and also leads to high communication costs and security problems. To handle these challenges, the fog computing has been introduced in literature, which places the resources and services at the edge of the network, close to the end‐devices. On the other hand, fog nodes suffer from heterogeneity, uncertainty, and dispersion of resources, and also processing, storage, and memory limitations. Thus, there is need for a proper task scheduling approach to use these resources optimally. In this study, the task scheduling algorithms proposed by different researchers for the cloud‐fog environment, their advantages and disadvantages, and also various tools and issues regarding the scheduling methods and their restrictions were discussed. The results indicated that about 58% of the scheduling algorithms use static scheduling and that the other 42% use dynamic scheduling, and the delay metric is the most noteworthy parameter considered in most studies with a share of nearly 17%. Finally, we identified the open issues related to the task scheduling based on cloud‐fog computing and provided some directions for future works.

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Immune Scheduling Network Based Method for Task Scheduling in Decentralized Fog Computing

Cited by 19 publications

References 18 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

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Task scheduling approaches in fog computing: A systematic review

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