Optimized assignment patterns in Mobile Edge Cloud networks. (June 2019)

• Record Type: Journal Article
• Authors: Ceselli, Alberto; Fiore, Marco; Premoli, Marco; Secci, Stefano
• Is Part Of: Computers & operations research. Volume 106(2019)
• Page Start: 246
• Page End: 259
• Publication Date: 2019-06
• Subjects: Mobile Edge Computing -- Prescriptive analytics -- Generalized Assignment -- Branch-and-Price; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2018.02.022
• Languages: English
• ISSNs: 0305-0548

Abstract

Highlights: We face a prescriptive analytics problem in Mobile Edge Computing. We propose a general data-driven framework including an optimization core. It includes an exact algorithm for a variant of the Generalized Assignment Problem. Our algorithm proves to be computationally effective on synthetic instances. Our model provides accurate solutions on real-world datasets. Abstract: Given an existing Mobile Edge Cloud (MEC) network including virtualization facilities of limited capacity, and a set of mobile Access Points (AP) whose data traffic demand changes over time, we aim at finding plans for assigning APs traffic to MEC facilities so that the demand of each AP is satisfied and MEC facility capacities are not exceeded, yielding high level of service to the users. Since demands are dynamic we allow each AP to be assigned to different MEC facilities at different points in time, accounting for suitable switching costs. We propose a general data-driven framework for our application including an optimization core, a data pre-processing module, and a validation module to test plans accuracy. Our optimization core entails a combinatorial problem that is a multi-period variant of the Generalized Assignment Problem: we design a Branch-and-Price algorithm that, although exact in nature, performs well also as a matheuristics when combined with early stopping. Extensive experiments on both synthetic and real-world datasets demonstrate that our approach is both computationally effective and accurate when employed for prescriptive analytics.