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Volume 6 Issue 4
Jul.  2019

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Qiang Fan and Nirwan Ansari, "On Cost Aware Cloudlet Placement for Mobile Edge Computing," IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 926-937, July 2019. doi: 10.1109/JAS.2019.1911564
Citation: Qiang Fan and Nirwan Ansari, "On Cost Aware Cloudlet Placement for Mobile Edge Computing," IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 926-937, July 2019. doi: 10.1109/JAS.2019.1911564

On Cost Aware Cloudlet Placement for Mobile Edge Computing

doi: 10.1109/JAS.2019.1911564
Funds:  This work was supported in part by the National Science Foundation (CNS-1647170)
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  • As accessing computing resources from the remote cloud inherently incurs high end-to-end (E2E) delay for mobile users, cloudlets, which are deployed at the edge of a network, can potentially mitigate this problem. Although some research works focus on allocating workloads among cloudlets, the cloudlet placement aiming to minimize the deployment cost (i.e., consisting of both the cloudlet cost and average E2E delay cost) has not been addressed effectively so far. The locations and number of cloudlets have a crucial impact on both the cloudlet cost in the network and average E2E delay of users. Therefore, in this paper, we propose the Cost Aware cloudlet PlAcement in moBiLe Edge computing (CAPABLE) strategy, where both the cloudlet cost and average E2E delay are considered in the cloudlet placement. To solve this problem, a Lagrangian heuristic algorithm is developed to achieve the suboptimal solution. After cloudlets are placed in the network, we also design a workload allocation scheme to minimize the E2E delay between users and their cloudlets by considering the user mobility. The performance of CAPABLE has been validated by extensive simulations.


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  • 1$ p_{kj} = \frac{the\ amount\ of\ time\ that\ user\ j\ is\ associated\ with\ BS\ k}{the\ total\ time\ period} $
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    • Optimize the tradeoff between cloudlet deployment cost and E2E delay in cloudlet placement.
    • Determine locations and quantities of cloudlets and servers to reduce cloudlet deployment cost.
    • Minimize average E2E delays of users in placing cloudlets to improve quality of user experience.
    • Dynamically set the tradeoff coefficient to meet cloudlet providers’ practical requirements.
    • The proposed algorithm has been demonstrated to achieve solutions close to the optimal ones.


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