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Robust and Cost-effective Resource Allocation for Complex IoT Applications in Edge-Cloud Collaboration

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Abstract

The rapid increasing of the Internet-of-Things (IoT) applications make it convenient to sense and collect real-world information in our daily life. To ensure the performance of these IoT applications, researchers established an edge-cloud collaboration application system based on the multi-access edge computing (MEC) paradigm where the IoT data can be processed not only on the cloud but also on nearby edge servers. However, as the edge servers are resource-limited, we should be more careful in allocating the edge resource to the application, especially when it is composed by several micro-services. In this paper, we considered how edge-cloud cooperation can help running these service composition based IoT applications and proposed an efficient resource allocation approach to balance performance, robustness, and cost-effectiveness of IoT applications in MEC environments. We mathematically modeled the cost-effective performance optimization problem in robust edge-cloud application systems and proved the convexity of the approximated problem so that they can be solved in tractable ways with existing solvers to generate the resource allocation strategies. Meanwhile, we carried out a series of experiments to evaluate our approach. The experiment results showed that our approach was powerful in managing the performance, cost and robustness compared with representative baselines.

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Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Notes

  1. https://www.gsmaintelligence.com

  2. https://docs.kubeedge.io/en/latest/modules/edgesite.html

  3. https://kubernetes.io/

  4. https://aws.amazon.com/ecs/pricing/?nc1=h_ls

  5. https://www.ibm.com/analytics/cplex-optimizer

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Acknowledgements

This research was partially supported by the National Natural Science Foundation of China (No.62102350, No. 62072402), Natural Science Foundation of Zhejiang Province (No. LQ21F020007, No. LQ20F020015).

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Authors and Affiliations

  1. School of Computer and Computing Science, Zhejiang University City College, Hangzhou, China

    Zhengzhe Xiang, Mengzhu He & Zengwei Zheng

  2. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Yuhang Zheng, Cheng Zhang & Zengwei Zheng

  3. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

    Dongjing Wang

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  1. Zhengzhe Xiang
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  6. Zengwei Zheng
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Correspondence to Zengwei Zheng.

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Xiang, Z., Zheng, Y., Wang, D. et al. Robust and Cost-effective Resource Allocation for Complex IoT Applications in Edge-Cloud Collaboration. Mobile Netw Appl 27, 1506–1519 (2022). https://doi.org/10.1007/s11036-022-01977-9

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