A Softwarized Service Infrastructure for the Dynamic Orchestration of IT Resources in 5G Deployments

Rago, Arcangela; Piro, Giuseppe; Boggia, Gennaro; Dini, Paolo

doi:10.1109/icton51198.2020.9203757

Cited by 1 publication

(3 citation statements)

References 13 publications

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“…To bridge this gap, this work formulates an innovative methodology for the anticipatory allocation of communication and computational resources at the network edge (i.e., task offloading), based on the knowledge of spatio-temporal dynamics of mobile users. The conceived approach significantly extends the very preliminary contributions presented, by the same authors of this work, in [48] and [49]. The considered architecture adopts a Software-Defined Networking (SDN) approach to monitor users' mobility over time.…”

Section: Introductionmentioning

confidence: 80%

“…This work mainly refers to the task offloading problem, according to which it is necessary to deploy (and properly use) available communication and intensive computational capabilities at the network edge for offering new heavy demanding and latency-critical services with challenging user expectations [1], [4], [49], [54].…”

Section: Reference Scenariomentioning

confidence: 99%

“…The considered architecture adopts a Software-Defined Networking (SDN) approach to monitor users' mobility over time. Then, starting from the outcomes of the preliminary contributions presented by the same authors in [48] and [49], it exploits a deep learning architecture based on Convolutional Long Short-Term Memory (ConvLSTM) [50] for predicting the distribution of users among cells and their related service demands over a look-ahead temporal horizon. A centralized Multi-access Edge Orchestrator uses this information to anticipatorily distribute users' demands among available MEC servers, while satisfying communication and computational constraints at the network edge and the upper bound for latency expected by mobile users.…”

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.

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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: Introductionmentioning

confidence: 80%