Journal
IEEE TRANSACTIONS ON CLOUD COMPUTING
Volume 9, Issue 3, Pages 968-980Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCC.2019.2903240
Keywords
mobile edge computing; resource provisioning; computation offloading
Categories
Funding
- National Key R&D Program of China [2018YFB1004801]
- National Science Foundation of China [61602054, 61801011]
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Mobile edge computing is a new computing paradigm that enhances user experience through low-latency connections and increased computation capacity. The Cloud Assisted Mobile Edge (CAME) computing framework improves the scalability of the edge platform by outsourcing mobile requests to clouds with various instances. The Optimal Resource Provisioning (ORP) algorithms optimize the computation capacity of edge hosts and adjust the cloud tenancy strategy while maintaining polynomial computational complexity and outperforming benchmark algorithms in system flexibility and cost-efficiency.
Mobile edge computing is emerging as a new computing paradigm that provides enhanced experience to mobile users via low latency connections and augmented computation capacity. As the amount of user requests is time-varying, while the computation capacity of edge hosts is limited, Cloud Assisted Mobile Edge (CAME) computing framework is introduced to improve the scalability of the edge platform. By outsourcing mobile requests to clouds with various types of instances, the CAME framework can accommodate dynamic mobile requests with diverse quality of service requirements. In order to provide guaranteed services at minimal system cost, the edge resource provisioning and cloud outsourcing of the CAME framework should be carefully designed in a cost-efficient manner. Specifically, two fundamental issues should be answered: (1) what is the optimal edge computation capacity configuration? and (2) what types of cloud instances should be tenanted and what is the amount of each type? To solve these issues, we formulate the resource provisioning in CAME framework as an optimization problem. By exploiting the piecewise convex property of this problem, the Optimal Resource Provisioning (ORP) algorithms with different instances are proposed, so as to optimize the computation capacity of edge hosts and meanwhile dynamically adjust the cloud tenancy strategy. The proposed algorithms are proved to be with polynomial computational complexity. To evaluate the performance of the ORP algorithms, extensive simulations and experiments are conducted based on both the widely-used traffic models and the Google cluster usage tracelogs, respectively. It is shown that the proposed ORP algorithms outperform the local-first and cloud-first benchmark algorithms in system flexibility and cost-efficiency.
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