An Efficient Service Function Chain Placement Algorithm in a MEC-NFV Environment

Wang, Meng; Cheng, Bo; Feng, Wendi; Chen, Junliang

doi:10.1109/globecom38437.2019.9013235

Cited by 25 publications

(13 citation statements)

References 12 publications

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“…Following our Platform components dimension, we distinguish the cloud-edge and the multi-edge scenarios depending on the placement options. The authors of [58], [61], [62], [65]- [67], [73], [76], [125], [126], [128], [162]- [164], [167], [175], [179] consider the cloud-edge scenario where the service components can be run either in the available edge domains or in the central cloud. Other research papers [83], [88], [89], [91], [92], [96], [97], [134], [138] investigate the multi-edge option where the central cloud cannot be used as a runtime environment.…”

Section: Multiple Components With Connectionmentioning

confidence: 99%

“…In some papers, these entities are VMs, containers, or for example, pods in the realm of Kubernetes [6]. Nevertheless, the models are the abstract translations of the actual entities into, most often, combinatorial problems, in which the placement decision is to be made on possibly multiple layers of our architecture Convex optimization [80] Interior-point method [142] Alternating direction method of multipliers Assignment method [126], [128] Hungarian method ILP Solver [58], [63], [120], [175] Linear programming [88], [176], [178] LP with fractal solution rounding…”

Section: A Problem Formulationmentioning

confidence: 99%

“…Alternatively, other papers do reveal the number of iterations, but not the complexity of all steps [54]; or only certain substeps are analyzed [75], [81], [111]. In the third category denoted by Form E, one sub-step is typically a linear programming method: [93] builds on the polynomial-time solvability of LP problems, whereas [126], [128], [133] explicitly rely on the complexity of the Simplex method. Furthermore, the total complexity shown in [178] is based solely on an LP solver.…”

Section: E Complexity Analysismentioning

confidence: 99%

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Survey on Placement Methods in the Edge and Beyond

Sonkoly

Czentye

Szalay

et al. 2021

IEEE Commun. Surv. Tutorials

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Edge computing is a (r)evolutionary extension of traditional cloud computing. It expands central cloud infrastructure with execution environments close to the users in terms of latency in order to enable a new generation of cloud applications. This paradigm shift has opened the door for telecommunications operators, mobile and fixed network vendors: they have joined the cloud ecosystem as essential stakeholders considerably influencing the future success of the technology. A key problem in edge computing is the optimal placement of computational units (virtual machines, containers, tasks or functions) of novel distributed applications. These components are deployed to a geographically distributed virtualized infrastructure and heterogeneous networking technologies are invoked to connect them while respecting quality requirements. The optimal hosting environment should be selected based on multiple criteria by novel scheduler algorithms which can cope with the new challenges of distributed cloud architecture where networking aspects cannot be ignored. The research community has dedicated significant efforts to this topic during recent years and a vast number of theoretical results have been published addressing different variants of the related mathematical problems. However, a comprehensive survey focusing on the technical and analytical aspects of the placement problem in various edge architectures is still missing. This survey provides a comprehensive summary and a structured taxonomy of the vast research on placement of computational entities in emerging edge infrastructures. Following the given taxonomy, the research papers are analyzed and categorized according to several dimensions, such as the capabilities of the underlying platforms, the structure of the supported services, the problem formulation, the applied mathematical methods, the objectives and constraints incorporated in the optimization problems, and the complexity of the proposed methods. We summarize the gained insights and important lessons learned, and finally, we reveal some important research gaps in the current literature.

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Section: Multiple Components With Connectionmentioning

confidence: 99%

Section: A Problem Formulationmentioning

confidence: 99%

Section: E Complexity Analysismentioning

confidence: 99%

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Survey on Placement Methods in the Edge and Beyond

Sonkoly

Czentye

Szalay

et al. 2021

IEEE Commun. Surv. Tutorials

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“…The proposed method here reduces the complexity and convergence times that essentially depend only on the physical graph sizes. Finally, the work in [13] formulates the physical network and SFC request as two weighted graphs and formulates the SFC placement problem in the NFV environment consisting of graph matching and VNF mapping. Then authors propose an LP-based approach and a Hungarian based algorithm to solve the graph matching and SFC mapping problem.…”

Section: Background Survey a Survey Of Sfc Provisioning Schemes In Cloud Architecturesmentioning

confidence: 99%

“…The computational complexity of the different SFC provisioning schemes in cloud and fog computing architectures are classified into the following. First, graph-based heuristics such as the work in [11]- [13] and [18] implement graph centrality, graph-clustering and multi-stage graphs to find the best path for request r in the SFC placement. Generally, the proposed methods here reduce the complexity and convergence times, since the computational complexity here essentially depends only on the physical graph sizes.…”

Section: E Computational Complexitymentioning

confidence: 99%

Delay-Aware SFC Provisioning in Hybrid Fog-Cloud Computing Architectures

et al. 2020

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Network function virtualization (NFV) technology enables service providers to implement software-based network processing functionalities on standard computing servers (nodes). As such, this approach mandates the need for mapping virtual network functions (VNFs) in service function chains (SFCs) on these nodes for incoming service requests. Now traditional VNF mapping schemes use cloud nodes for its abundant available resources, at the detriment of prolonged network delays. Alternative schemes use fog nodes that return reduced delays, at the detriment of limited resources. Hence in this work, a novel SFC provisioning scheme is proposed for a hybrid fog-cloud architecture of various resources. The architecture is composed of a single fog and single cloud layer, in order to accommodate both delay-sensitive and delay-tolerant requirements for large number of incoming requests, respectively. This scheme yields a tradeoff between standalone cloud and fog solutions when implemented on the proposed hybrid architecture, in terms of the number of satisfied requests, network delay, resources consumption, energy consumption, and realization cost at large traffic volumes. The proposed scheme achieves 15-40% higher traffic capacity than fog solutions, 21-43% reduced delay, 45-52% less energy consumption levels and 28-30% less cost as compared to cloud solutions.INDEX TERMS Cloud computing, fog computing, hybrid fog-cloud architecture, network function virtualization, service function chain

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