Methods of Resource Scheduling Based on Optimized Fuzzy Clustering in Fog Computing

Li, Guangshun; Liu, Yuncui; Wu, Junhua; Lin, Dong‐Tsamn; Zhao, Shanshan

doi:10.3390/s19092122

Cited by 94 publications

(62 citation statements)

References 27 publications

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“…Li et al 84 proposed a resource scheduling method for fog computing in which the features of resources are standardized and normalized, and then, the fuzzy clustering methods combined with PSO are used for resource categorization, which reduces the search space of resources. Indeed, the proposed Fuzzy Clustering Algorithm with PSO (FCAP) algorithm is based on the traditional Fuzzy C‐Means (FCM) clustering algorithm and PSO algorithm, and its main idea is to incorporate PSO into the FCM algorithm.…”

Section: Organization Of the Task Schedulingmentioning

confidence: 99%

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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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Section: Organization Of the Task Schedulingmentioning

confidence: 99%

“…The simulations required in Li et al 84 were done in MATLAB, and the numbers of resource nodes and user requests were randomly initialized. Resource nodes in the experiment were classified based on their capabilities: computing power, bandwidth capability, and storage capability.…”

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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“…Our team has done a lot of work on edge computing and fog computing. For example, the resource scheduling method for fog computing is studied [19], the data processing delay optimization in mobile edge computing [20], and the resource scheduling in edge computing [21], etc. In this paper, We focus on the load balancing problem of edge computing.…”

Section: Related Workmentioning

confidence: 99%

A new load balancing strategy by task allocation in edge computing based on intermediary nodes

Yao

et al. 2020

J Wireless Com Network

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The latency of cloud computing is high for the reason that it is far from terminal users. Edge computing can transfer computing from the center to the network edge. However, the problem of load balancing among different edge nodes still needs to be solved. In this paper, we propose a load balancing strategy by task allocation in edge computing based on intermediary nodes. The intermediary node is used to monitor the global information to obtain the real-time attributes of the edge nodes and complete the classification evaluation. First, edge nodes can be classified to three categories (light-load, normal-load, and heavy-load), according to their inherent attributes and realtime attributes. Then, we propose a task assignment model and allocate new tasks to the relatively lightest load node. Experiments show that our method can balance load among edge nodes and reduce the completion time of tasks.

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“…Li et al [15] have presented a user-oriented improved spectral clustering scheduling algorithm to solve the problem of resource scheduling and improve the satisfaction of users. And in [16], the methods of fuzzy clustering were combined with particle swarm optimization to divide the resources, which improves user satisfaction and the efficiency of resource scheduling. However, mobile cloud computing also requires a high quality of network connections with remote infrastructures.…”

Section: Related Workmentioning

confidence: 99%

Resource Management Framework Based on the Stackelberg Game in Vehicular Edge Computing

Zhang

Wang

et al. 2020

Complexity

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With the emergence and development of the Internet of Vehicles (IoV), quick response time and ultralow delay are required. Cloud computing services are unfavorable for reducing delay and response time. Mobile edge computing (MEC) is a promising solution to address this problem. In this paper, we combined MEC and IoV to propose a specific vehicle edge resource management framework, which consists of fog nodes (FNs), data service agents (DSAs), and cars. A dynamic service area partitioning algorithm is designed to balance the load of DSA and improve the quality of service. A resource allocation framework based on the Stackelberg game model is proposed to analyze the pricing problem of FNs and the data resource strategy of DSA with a distributed iteration algorithm. The simulation results show that the proposed framework can ensure the allocation efficiency of FN resources among the cars. The framework achieves the optimal strategy of the participants and subgame perfect Nash equilibrium.

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Methods of Resource Scheduling Based on Optimized Fuzzy Clustering in Fog Computing

Cited by 94 publications

References 27 publications

Task scheduling approaches in fog computing: A systematic review

Task scheduling approaches in fog computing: A systematic review

A new load balancing strategy by task allocation in edge computing based on intermediary nodes

Resource Management Framework Based on the Stackelberg Game in Vehicular Edge Computing

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