Mobility-Aware Deep Reinforcement Learning with Glimpse Mobility Prediction in Edge Computing

Wu, Chao-Lun; Chiu, Te-Chuan; Wang, Chih-Yu; Pang, Ai‐Chun

doi:10.1109/icc40277.2020.9149185

Cited by 28 publications

(17 citation statements)

References 13 publications

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“…Differently from the above-discussed methodologies, solutions based on machine learning promises to better anticipate network behaviors and dynamics, also in heterogeneous and large scale scenarios [46], [47]. For example, the prediction of trajectory and location is performed through deep learning architectures, as Long Short-Term Memorys (LSTMs) [29], [30], [32], [33], LSTMs with attention mechanism [34], Convolutional Neural Networks (CNNs) [31], and a combination of recurrent and CNNs with Markov Chains [35]. Furthermore, the number of users in a given geographical area is predicted through machine learning-based Regressors in [36] and a combination of deep learning and Bayesian networks in [37].…”

Section: State Of the Artmentioning

confidence: 99%

“…Optimization problems willing to minimize delay [32] and energy consumption [35] are formulated in [32], [35]. Finally, the work discussed in [33] adopts deep reinforcement learning to manage computation offloading tasks among different remote MEC servers in order to minimize the delay. [26] 1 [27] [28]…”

Section: State Of the Artmentioning

confidence: 99%

“…Moreover, an intrinsic characteristic of many 5G services (e.g., autonomous driving, virtual/augmented reality assisting museum tours) is mobility. Therefore, the communication and computational resources must be managed by using a mobilityaware approach, which is considered one of the most critical and challenging issues for network orchestration [6], [33].…”

Section: Reference Scenariomentioning

confidence: 99%

“…Solutions based on machine and deep learning also promise to better anticipate network behaviors and dynamics in heterogeneous and large scale scenarios [46], [47]. Nevertheless, resulting network optimization problems (including those presented in [26]- [28], [30]- [33], [35], [40]- [43], [45]) fail to take advantage of the joint prediction of both users' mobility and service demands over a look-ahead temporal horizon and within a standard compliant ETSI-MEC context.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Anticipatory Allocation of Communication and Computational Resources at the Edge Using Spatio-Temporal Dynamics of Mobile Users

Rago

Piro

Boggia

et al. 2021

IEEE Trans. Netw. Serv. Manage.

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Multi-access Edge Computing represents a key enabling technology for emerging mobile networks. It offers intensive computational resources very close to the end-users, useful for task offloading purposes. Many scientific contributions already proposed approaches for optimally allocating these resources over time. However, most of them fail to take advantage of the prediction of both users' mobility and service demands over a look-ahead temporal horizon. To bridge this gap, this paper formulates a novel methodology for anticipatorily allocating communication and computational resources at the network edge, based on the prediction of spatio-temporal dynamics of mobile users. The conceived architecture exploits a Software-Defined Networking approach to monitor users' mobility, a Convolutional Long Short-Term Memory to predict over different look-ahead horizons the number of users within a given number of cells and their related service demands, and Dynamic Programming to optimally allocate users' requests among available Multi-access Edge Computing servers. Computer simulations investigate the effectiveness of the proposed approach in a realistic autonomous driving use case and compare its behavior against a baseline solution. Obtained results demonstrate its unique ability to dynamically and fairly distribute users' requests among the resources available at the network edge, while ensuring the targeted quality of service level.

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: Reference Scenariomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Anticipatory Allocation of Communication and Computational Resources at the Edge Using Spatio-Temporal Dynamics of Mobile Users

Rago

Piro

Boggia

et al. 2021

IEEE Trans. Netw. Serv. Manage.

View full text Add to dashboard Cite

show abstract

“…Because of the limited resources of servers, the mobility of users, and the low latency requirements of service requests, computing offloading and service migrations are expected to occur in MEC systems regularly. The authors in [17] proposed a glimpse mobility prediction model based on the seq2seq model, which provides useful coarse-grain mobility information for Mobility-Aware Deep Reinforcement Learning training. However, the traditional offloading approaches (e.g., auction-based and game-theory approaches) cannot adjust the strategy according to changing environment when dealing with the mobility problem, in order to keep the system performance for a long time, an online user-centric mobility management scheme is proposed in [18] to maximize the edge computation performance while keeping the energy consumption of user's communication low by using Lyapunov optimization and multiarmed bandit theories.…”

Section: Related Workmentioning

confidence: 99%

A Mobility‐Aware and Sociality‐Associate Computation Offloading Strategy for IoT

Chen

Gao

et al. 2021

Wireless Communications and Mobile Computing

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Mobile edge computing, a promising paradigm, brings services closer to a user by leveraging the available resources in an edge network. The crux of MEC is to reasonably allocate resources to satisfy the computing requirements of each node in the network. In this paper, we investigate the service migration problem of the offloading scheme in a power-constrained network consisting of multiple mobile users and fixed edge servers. We propose an affinity propagation-based clustering-assisted offloading scheme by taking into account the users’ mobility prediction and sociality association between mobile users and edge servers. The clustering results provide the candidate edge servers, which greatly reduces the complexity of observing all edge servers and decreases the rate of service migration. Besides, the available resource of candidate edge servers and the channel conditions are considered to optimize the offloading scheme to guarantee the quality of service. Numerical simulation results demonstrate that our offloading strategy can enhance the data processing capability of power-constrained networks and reach computing load balance.

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Resource Optimization in MEC-Based B5G Networks for Indoor Robotics Environment

Prastowo

Shah

Palopoli

2022

Lecture Notes in Electrical Engineering

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Mobility-Aware Deep Reinforcement Learning with Glimpse Mobility Prediction in Edge Computing

Cited by 28 publications

References 13 publications

Anticipatory Allocation of Communication and Computational Resources at the Edge Using Spatio-Temporal Dynamics of Mobile Users

Anticipatory Allocation of Communication and Computational Resources at the Edge Using Spatio-Temporal Dynamics of Mobile Users

A Mobility‐Aware and Sociality‐Associate Computation Offloading Strategy for IoT

Resource Optimization in MEC-Based B5G Networks for Indoor Robotics Environment

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