Resource Allocation and Load Balancing Strategy in Cloud-fog Hybrid Computing Based on Cluster-collaboration

YANG Shouyi; CHENG Haoze; DANG Yaping

doi:10.11999/JEIT220719

Volume 45 Issue 7

Jul. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(7): 2423-2431

YANG Shouyi, CHENG Haoze, DANG Yaping. Resource Allocation and Load Balancing Strategy in Cloud-fog Hybrid Computing Based on Cluster-collaboration[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2423-2431. doi: 10.11999/JEIT220719

Citation:

YANG Shouyi, CHENG Haoze, DANG Yaping. Resource Allocation and Load Balancing Strategy in Cloud-fog Hybrid Computing Based on Cluster-collaboration[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2423-2431. doi: 10.11999/JEIT220719

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Resource Allocation and Load Balancing Strategy in Cloud-fog Hybrid Computing Based on Cluster-collaboration

doi: 10.11999/JEIT220719

School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China

Funds: The National Key R&D Program Intergovernmental Cooperation Special Project (2016YFE0118400), The Natural Science Foundation of Henan Province (202300410482), Zhengzhou Major Science and Technology Innovation Special (2019CXZX0037)

Received Date: 2022-06-01
Rev Recd Date: 2022-10-14

Available Online: 2022-10-19

Publish Date: 2023-07-10

Abstract

Abstract

Considering the problem of data congestion in mobile networks caused by the rapid growth of smart applications in Internet of Things (IoT), a cloud-fog hybrid computing model based on cluster-collaboration is constructed. The cluster load balancing is considered while introducing weighting factors to balance the computational latency and energy consumption, and finally the minimum weighted sum of system latency and energy consumption is achieved. In order to solve this mixed integer nonlinear programming problem, the original problem is decomposed to optimize the resource allocation using Karush-Kuhn-Tucker (KKT) condition and bisection search iterative method. Then an Overhead Minimization Offloading Algorithm based on Branch and Brand (BB-OMOA) is proposed to obtain the optimal offloading decision. Simulation results show that the cluster-collaboration model improves significantly the system load balancing degree and the proposed strategy outperforms significantly other benchmark schemes.
- Fog Computing (FC),
- Cluster,
- Resource allocation,
- Load balancing,
- Branch brand

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References(18)

References

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