Optimized assignment patterns in Mobile Edge Cloud networks

Ceselli, Alberto; Fiore, Marco; Premoli, Marco; Secci, Stefano

doi:10.1016/j.cor.2018.02.022

Cited by 10 publications

(19 citation statements)

References 32 publications

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“…Various approaches for addressing VNF resource allocation (VNF-RA) are presented in [19]. The optimization can be conducted by using mixed-integer linear programming models [8], [9], [15], heuristics algorithms [10], [11], game theory [12], [13] or machine learning [14] approaches.…”

Section: Gppmentioning

confidence: 99%

“…In the following, we fully formalize our modeling choices. This mathematical formalization step is inspired by [15], in which a single-slice MEC optimization problem is considered.…”

Section: B Notation and Modelingmentioning

confidence: 99%

“…The main contributions of the paper are threefold: i) We establish a combinatorial optimization model that natively supports multiple network slices, which differ in terms of QoS requirements. The model starts from the single-slice model that is presented in [15] and expands it to incorporate the multi-slice characteristic of modern mobile networks. The algorithm addresses a combinatorial problem that is a multi-period variant of the generalized assignment problem.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Assignment Plan in Sliced Backhaul Networks

et al. 2020

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The 5G mobile network will rely on network slicing to provide a wide variety of services with various quality of service (QoS) requirements. Network slicing is promoted by 3GPP and provides a logical vertical partition of the network that is based on network virtualization technologies, namely, network function virtualization (NFV), software-defined networking (SDN) and ETSI multi-access edge computing (MEC). Despite the undisputed benefits in terms of flexibility and scalability that are pledged by the paradigm, network slicing requires intelligent resource scheduling and allocation algorithms to efficiently use the network resources, especially at the edge of the network, due to their scarcity. In this paper, we propose an optimization algorithm for steering data traffic of multiple slices in the edge backhaul network, which aims at maximizing the QoS. We extensively analyze the realizable grade of QoS by testing various levels of MEC resources, demonstrate the beneficial impact of the approach for mobile operators, and highlight the performance advantage that is realized versus a single-slice approach of undifferentiated traffic.INDEX TERMS Multi-access edge computing, network slices, mathematical optimization.

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Section: Gppmentioning

confidence: 99%

“…In the following, we fully formalize our modeling choices. This mathematical formalization step is inspired by [15], in which a single-slice MEC optimization problem is considered.…”

Section: B Notation and Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Assignment Plan in Sliced Backhaul Networks

et al. 2020

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“…Our MEC orchestrator includes an optimization core for performing prescriptive analytics on a tactical level. We adapt models and methods from [38]. In particular, we build dynamic assignment plans detailing, for each time slot, the set of BSs to be connected to each MEC facility and, as a byproduct, the set of switching operations to be performed between subsequent time slots.…”

Section: B Orchestration Optimization Modelmentioning

confidence: 99%

“…In turn, we are able to obtain a set of optimal multipliers through Dantzig Wolfe Reformulation and Column Generation methods [38]. Strictly speaking, the solution found by means of Dantzig Wolfe Reformulation might be fractional, that is some BSs might be fractionally assigned to more than a single MEC facility in some time slots.…”

Section: B Orchestration Optimization Modelmentioning

confidence: 99%

Prescriptive Analytics for MEC Orchestration

Ceselli

Fiore

Furno

et al. 2018

2018 IFIP Networking Conference (IFIP Networking) and Workshops

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Orchestrating network and computing resources in Mobile Edge Computing (MEC) is an important item in the networking research agenda. In this paper, we propose a novel algorithmic approach to solve the problem of dynamically assigning base stations to MEC facilities, while taking into consideration multiple time-periods, and computing load switching and access latency costs. In particular, leveraging on an existing state of the art on mobile data analytics, we propose a methodology to integrate arbitrary time-period aggregation methods into a network optimization framework. We notably apply simple consecutive time period aggregation and agglomerative hierarchical clustering. Even if the aggregation and optimization methods represent techniques which are different in nature, and whose aim is partially overlapping, we show that they can be integrated in an efficient way. By simulation on real mobile cellular datasets, we show that, thanks to the clustering, we can scale with the number of time-periods considered, that our approach largely outperforms the case without time-period aggregations in terms of MEC access latency, and at which extent the use of clustering and time aggregation affects computing time and solution quality.

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