EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks

By Yuxuan Sun, Sheng Zhou, Jie Xu. IEEE Journal on Selected Areas in Communications, 2017

374 citations recorded by the source corpus.

Merging mobile edge computing (MEC) functionality with the dense deployment of base stations (BSs) provides enormous benefits such as a real proximity, low latency access to computing resources. However, the envisioned integration creates many new challenges, among which mobility management (MM) is a critical one. Simply applying existing radio access-oriented MM schemes leads to poor performance mainly due to the co-provisioning of radio access and computing services of the MEC-enabled BSs. In this paper, we develop a novel user-centric energy-aware mobility management (EMM) scheme, in order to optimize the delay due to both radio access and computation, under the long-term energy consumption constraint of the user. Based on Lyapunov optimization and multi-armed bandit theories, EMM works in an online fashion without future system state information, and effectively handles the imperfect system state information. Theoretical analysis explicitly takes radio handover and computation migration cost into consideration and proves a bounded deviation on both the delay performance and energy consumption compared with the oracle solution with exact and complete future system information. The proposed algorithm also effectively handles the scenario in which candidate BSs randomly switch ON/OFF during the offloading process of a task. Simulations show that the proposed algorithms can achieve close-to-optimal delay performance while satisfying the user energy consumption constraint.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/104fc7a5cd2c71aae0dd70d158577bb746b8b663

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Citations in the corpus

Toward Edge Intelligence: Multiaccess Edge Computing for 5G and Internet of Things (2020, background): “Some other works, such as [175] and [176] deal with mobility management issues for the MEC architecture from the energy-efﬁcient perspective.”
Mobile Edge Intelligence and Computing for the Internet of Vehicles (2019, methodology): “Task migration among edge servers may be applied for mobility management [103], [109].”
Delay-Aware Microservice Coordination in Mobile Edge Computing: A Reinforcement Learning Approach (2019, background): “According to [33], [43], each edge cloud is equipped with multiple CPU cores, and the sum frequency is 25 Ghz.”
Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing (2018, methodology): “Taking into the long-term available energy constraint, an online energy-aware base station association and handover algorithm was proposed to minimize the average end-to-end delay by incorporating Lyapunov optimization and multi-armed bandit theory [14].”
Survey on Multi-Access Edge Computing for Internet of Things Realization (2018, methodology): “[133] developed a novel user-centric Energy-aware Mobility Management (EMM) scheme based on Lyapunov optimization and multi-armed bandit theories.”
Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing (2018, methodology): “(4) On efficient algorithmic design, the work [21] is based on multiarm bandit optimization, while we blend the advantages of Lyapunov optimization and Markov approximation techniques.”
Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network (2018, background): “[36] developed a novel usercentric mobility management scheme for mobile edge comput-”
Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems (2019, background): “Tasks of larger workloads can be further partitioned into multiple subtasks [18], [28], so that each subtask is offloaded to and processed by a SeV within one time period.”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, neutral): “into consideration, the study in [213] optimized the association (which MEC server is selected for remote execution) and handover (i.”