TY - JOUR
T1 - Spatio-Temporal Edge Service Placement
T2 - A Bandit Learning Approach
AU - Chen, Lixing
AU - Xu, Jie
AU - Ren, Shaolei
AU - Zhou, Pan
N1 - Funding Information:
Manuscript received April 23, 2018; revised August 22, 2018; accepted October 6, 2018. Date of publication October 25, 2018; date of current version December 10, 2018. The work of L. Chen and J. Xu was supported by the Army Research Office under Grant W911NF-18-1-0343. The work of S. Ren was supported by NSF under Grants CNS-1551661 and ECCS-1610471. The associate editor coordinating the review of this paper and approving it for publication was X. Wang. (Corresponding author: Jie Xu.) L. Chen and J. Xu are with the Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33146 USA (e-mail: lx.chen@miami.edu; jiexu@miami.edu).
PY - 2018/12
Y1 - 2018/12
N2 - Shared edge computing platforms deployed at the radio access network are expected to significantly improve the quality-of-service delivered by application service providers (ASPs) in a flexible and economic way. However, placing edge service in every possible edge site by an ASP is practically infeasible due to the ASP's prohibitive budget requirement. In this paper, we investigate the edge service placement problem of an ASP under a limited budget, where the ASP dynamically rents computing/storage resources in edge sites to host its applications in close proximity to end users. Since the benefit of placing edge service in a specific site is usually unknown to the ASP a priori, optimal placement decisions must be made while learning this benefit. We pose this problem as a novel combinatorial contextual bandit learning problem. It is "combinatorial" because only a limited number of edge sites can be rented to provide the edge service given the ASP's budget. It is "contextual" because we utilize user context information to enable finer-grained learning and decision-making. To solve this problem and optimize the edge computing performance, we propose SEEN, a Spatial-temporal Edge sErvice placemeNt algorithm. Furthermore, SEEN is extended to scenarios with overlapping service coverage by incorporating a disjunctively constrained knapsack problem. In both cases, we prove that our algorithm achieves a sublinear regret bound when it is compared with an Oracle algorithm that knows the exact benefit information. Simulations are carried out on a real-world dataset, whose results show that SEEN significantly outperforms benchmark solutions.
AB - Shared edge computing platforms deployed at the radio access network are expected to significantly improve the quality-of-service delivered by application service providers (ASPs) in a flexible and economic way. However, placing edge service in every possible edge site by an ASP is practically infeasible due to the ASP's prohibitive budget requirement. In this paper, we investigate the edge service placement problem of an ASP under a limited budget, where the ASP dynamically rents computing/storage resources in edge sites to host its applications in close proximity to end users. Since the benefit of placing edge service in a specific site is usually unknown to the ASP a priori, optimal placement decisions must be made while learning this benefit. We pose this problem as a novel combinatorial contextual bandit learning problem. It is "combinatorial" because only a limited number of edge sites can be rented to provide the edge service given the ASP's budget. It is "contextual" because we utilize user context information to enable finer-grained learning and decision-making. To solve this problem and optimize the edge computing performance, we propose SEEN, a Spatial-temporal Edge sErvice placemeNt algorithm. Furthermore, SEEN is extended to scenarios with overlapping service coverage by incorporating a disjunctively constrained knapsack problem. In both cases, we prove that our algorithm achieves a sublinear regret bound when it is compared with an Oracle algorithm that knows the exact benefit information. Simulations are carried out on a real-world dataset, whose results show that SEEN significantly outperforms benchmark solutions.
KW - Edge computing
KW - context awareness
KW - multi-armed bandit
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U2 - 10.1109/TWC.2018.2876823
DO - 10.1109/TWC.2018.2876823
M3 - Article
AN - SCOPUS:85055683336
VL - 17
SP - 8388
EP - 8401
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
SN - 1536-1276
IS - 12
M1 - 8509631
ER -