Resource Scheduling Based on Improved Spectral Clustering Algorithm in Edge Computing

Li, Guangshun; Xu, Shuzhen; Wu, Junhua; Ding, Heng

doi:10.1155/2018/6860359

Cited by 16 publications

(8 citation statements)

References 38 publications

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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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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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“…Aura allows mobile clients to create ad hoc and flexible clouds using the IoT and other computing devices in the nearby physical environment. 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.…”

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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“…Li et al [22] proposed a resource scheduling method to improve the efficiency of resources in fog computing. Li et al [23] proposed a user-oriented spectral clustering scheduling algorithm based on the k-means algorithm to improve the satisfaction of users in a fog computing environment. Yu et al proposed a connected k-coverage working sets construction algorithm (CWSC) based on Euclidean distance to prolong the lifetime of sensor networks [24].…”

Section: Related Workmentioning

confidence: 99%

Energy Consumption Optimization With a Delay Threshold in Cloud-Fog Cooperation Computing

Yan

Chen

et al. 2019

IEEE Access

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With the rapid development of the Internet of Things (IoT), the number of mobile terminal devices is increasing. Massive data are generated by mobile terminal devices, resulting in high delay and high energy consumption. In most cases, however, a low delay means high energy consumption. To balance energy consumption and delay, we adopt a tradeoff strategy that can realize optimal energy consumption with a delay threshold in this paper. First, we introduce the role of the delay threshold in reducing delay. Then, we describe the delay and energy consumption of the mobile terminal layer, fog node layer and cloud server layer with queue theory. Nonlinear programming is used to solve the energy optimization problem by calculating the optimal workload of each layer. We design a cloud-fog cooperation scheduling algorithm to reduce energy consumption. A task offloading algorithm is also designed to complete tasks when their nodes leave. The experimental results show that the energy consumption is reduced by approximately 22%, while the delay is 12.5% less than the first come first served (FCFS) approach. INDEX TERMS Energy consumption optimization, fog computing, tasks scheduling.

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Resource Scheduling Based on Improved Spectral Clustering Algorithm in Edge Computing

Cited by 16 publications

References 38 publications

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

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

Energy Consumption Optimization With a Delay Threshold in Cloud-Fog Cooperation Computing

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