Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing

By Ouyang Tao, Zhi Zhou, Xu Chen. International Workshop on Quality of Service, 2018

435 citations recorded by the source corpus.

Mobile edge computing is a new computing paradigm in which cloud computing capabilities are pushed from the network core to the network edge to serve the end-user in proximity. However, with the sinking of computing capabilities, the new challenge incurred by user mobility arises: since end-users typically move erratically, the services should be dynamically migrated among multiple edges to maintain the service performance, i.e., user-perceived latency. Tackling this problem is non-trivial since frequent service migration would greatly increase the operational cost. To address this challenge in terms performance-cost trade-off, in this paper we study the mobile edge service performance optimization problem under long-term cost budget constraint. To address user mobility which is typically unpredictable, we first apply Lyapunov optimization to decompose the long-term optimization problem into a series of real-time optimization problems which do not require a priori knowledge such as user mobility. As the decomposed problem is NP-hard, we further propose an efficient heuristic based on the Markov approximation technique. Rigorous theoretical analysis and extensive evaluations demonstrate the efficacy of the proposed solution.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/eb6adb8bee9dcc7591810f6a66fecba6933ed4ba

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In Computer Networks and Communications

Citations in the corpus

Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing Systems (2020, neutral): “Since we focus on characterizing the load level dynamics at the edge nodes, we consider a constant transmission capacity in the system model, as in some of the existing works such as [12], [14], [22], [23].”
CoEdge: Cooperative DNN Inference With Adaptive Workload Partitioning Over Heterogeneous Edge Devices (2020, background): “Yet the Quality-of-Service (QoS) can still be poor and unsatisfactory due to the unreliable and delay-signiﬁcant wide-area network connection between edge devices and the remote cloud [11], [12].”
An Overview of Service Placement Problem in Fog and Edge Computing (2020, background): “Others Matching game [71] Machine learning [57, 107] Stochastic optimization / Petri nets / Potential games / Quadratic Assignment Problem / General convex optimization / Linear programming [91, 119] / [120] / [92] / [88] / [69]”
Delay-Aware Microservice Coordination in Mobile Edge Computing: A Reinforcement Learning Approach (2019, background): “To address this issue, service migration is proposed, which deals with whether, how, and where the service should be migrated when users change their locations [17], [18], [19], [20].”
A Survey on Mobile Augmented Reality With 5G Mobile Edge Computing: Architectures, Applications, and Technical Aspects (2021, background): “The mobility aware dynamic service placement mechanisms [233] can corporate with multiple servers and optimize communication delay and computing delay to minimize the user-perceived latency.”
Mobile Edge Intelligence and Computing for the Internet of Vehicles (2019, background): “In [98], performance optimization under a long-term cost budget constraint was investigated.”
Intelligent Edge Computing in Internet of Vehicles: A Joint Computation Offloading and Caching Solution (2021, methodology): “In [17], user mobility is integrated with server placement to specify the ofﬂoading decision for delay-sensitive computational tasks.”
Latency Minimization for Intelligent Reflecting Surface Aided Mobile Edge Computing (2019, background): “Recently, a specific user association scheme was also developed for multi-user systems served by multiple edge computing nodes [13], while a mobility-aware dynamic service scheduling algorithm was proposed for MEC systems [14].”