Robust Online Composition, Routing and NF Placement for NFV-Enabled Services

Alhussein, Omar; Zhuang, Weihua

doi:10.1109/jsac.2020.2986612

Cited by 23 publications

(5 citation statements)

References 40 publications

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“…In the literature, many request arrival models have been studied and applied to queuing based systems such as the Poison processes [23], statistic arrivals based on observing the state of the system to build an arrival model [24], [25] and stochastic arrival models [26]- [28]. The impact of the request arrival model is crucial, especially when investigating an online-related problem such as in [10]. In [10], the authors considered that the requests arrive in a sequence but did not consider a precise model.…”

Section: B Service Requestsmentioning

confidence: 99%

“…The impact of the request arrival model is crucial, especially when investigating an online-related problem such as in [10]. In [10], the authors considered that the requests arrive in a sequence but did not consider a precise model. However, the requests arrival model has a crucial role in studying resource allocation in a MEC architecture.…”

Section: B Service Requestsmentioning

confidence: 99%

“…In order to evaluate the performance of the algorithms, we compared them to benchmarks of existing approaches from the literature. In particular, we compared the approach to an meta-heuristicbased approach, and precisely the genetic algorithm (GA) [17], and integer linear-programming (ILP) [10]. The simulation setup consists in variable set of EN generated with values between 3 and 15, varying VNFs between 1 and 1000 per EN.…”

Section: B System Evaluationmentioning

confidence: 99%

“…The VNF/SFC resource allocation approaches can be grouped into three main categories, theoretical games approaches [9], [35], linear programming variant approaches [10]- [12], the machine learning approaches [13], [14] and the heuristic approaches [15], [16].…”

Section: Related Workmentioning

confidence: 99%

“…Compared to our work, we considered a many-to-one matching game to schedule multiple VNFs at a time. Work in [10] proposed an online provisioning for NFV considering both unicast and multicast services with two types of VNFs, mandatory and best-effort instances. In addition, the work considered only the computational and transmission resources.…”

Section: Related Workmentioning

confidence: 99%

See 4 more Smart Citations

Service Function Chaining in MEC: A Mean-Field Game and Reinforcement Learning Approach

Abouaomar¹,

Cherkaoui²,

Mlika³

et al. 2021

Preprint

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Multi-access edge computing (MEC) and network virtualization technologies are important enablers for fifthgeneration (5G) networks to deliver diverse applications and services. Services are often provided as fully connected virtual network functions (VNF)s, through service function chaining (SFC). However, the problem of allocating SFC resources at the network edge still faces many challenges related to the way VNFs are placed, chained and scheduled. In this paper, to solve these problems, we propose a game theory-based approach with the objective to reduce service latency in the context of SFC at the network edge. The problem of allocating SFC resources can be divided into two subproblems. 1) The VNF placement and routing subproblem, and 2) the VNF scheduling subproblem. For the former subproblem, we formulate it as a mean field game (MFG) in which VNFs are modeled as entities contending over edge resources with the goal of reducing the resource consumption of MEC nodes and reducing latency for users. We propose a on a reinforcement learning-based technique, where the Ishikawa-Mann learning algorithm (IMLA) is used. For the later subproblem we formulate it as a matching game between the VFNs and an edge resources in order to find the execution order of the VNFs while reducing the latency. To efficiently solve it, we propose a modified version of the many-to-one deferred acceptance algorithm (DAA), called the enhanced multi-step deferred acceptance algorithm (eMSDA). To illustrate the performance of the proposed approaches, we perform extensive simulations. The obtained results show that the proposed approaches outperform the benchmarks other state-ofthe-art methods.

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