Online Distributed Edge Caching for Mobile Data Offloading in 5G Networks

Zeng, Yiming; Huang, Yaodong; Liu, Zhenhua; Yang, Yuanyuan

doi:10.1109/iwqos49365.2020.9213007

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…Deep reinforcement learning has been adopted in [16], while their model confines service to identically sized content, and only minimizes the traffic cost. Zeng et al [17] utilize primal-dual decomposition to translate the joint problem into bi-problems, and design an approximation algorithm to solve each sub-problem. Zhao et al [18] highlight the cost of forwarding requests and downloading services in a homogeneous edge-cloud setting where all services consume the same amount of storage capacity and only resides in one edge node.…”

Section: Literature Reviewmentioning

confidence: 99%

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Online Service Placement and Request Scheduling in MEC Networks

Su¹,

Wang²,

Zhou³

et al. 2021

Preprint

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Mobile edge computing (MEC) emerges as a promising solution for servicing delay-sensitive tasks at the edge network. A body of recent literature started to focus on cost-efficient service placement and request scheduling. This work investigates the joint optimization of service placement and request scheduling in a dense MEC network, and develops an efficient online algorithm that achieves closeto-optimal performance. Our online algorithm consists of two basic modules: (1) a regularization with look-ahead approach from competitive online convex optimization, for decomposing the offline relaxed minimization problem into multiple sub-problems, each of which can be efficiently solved in each time slot; (2) a randomized rounding method to transform the fractional solution of offline relaxed problem into integer solution of the original minimization problem, guaranteeing a low competitive ratio. Both theoretical analysis and simulation studies corroborate the efficacy of our proposed online MEC optimization algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

“…Primal-dual algorithm (PD) [17]: it resorts to primal-dual method to minimize the total cost; Greedy algorithm (GA): it greedily make the joint decision to minimize the holistic cost;…”

Section: Comparison With Other Schemesmentioning

confidence: 99%