TY - JOUR
T1 - On cost aware cloudlet placement for mobile edge computing
AU - Fan, Qiang
AU - Ansari, Nirwan
N1 - Funding Information:
Manuscript received December 7, 2018; revised February 2, 2019; accepted March 27, 2019. This work was supported in part by the National Science Foundation (CNS-1647170). Recommended by Associate Editor MengChu Zhou. (Corresponding author: Qiang Fan.) Citation: Q. Fan and N. Ansari, “On cost aware cloudlet placement for mobile edge computing,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 926–937, Jul. 2019.
Funding Information:
This work was supported in part by the National Science Foundation (CNS-1647170)
Publisher Copyright:
© 2014 Chinese Association of Automation.
PY - 2019/7
Y1 - 2019/7
N2 - 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.
AB - 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.
KW - Cloudlet placement
KW - Mobile cloud computing
KW - Mobile edge computing
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U2 - 10.1109/JAS.2019.1911564
DO - 10.1109/JAS.2019.1911564
M3 - Article
AN - SCOPUS:85068783856
SN - 2329-9266
VL - 6
SP - 926
EP - 937
JO - IEEE/CAA Journal of Automatica Sinica
JF - IEEE/CAA Journal of Automatica Sinica
IS - 4
M1 - 8753750
ER -