VNF Placement and Resource Allocation in SDN/NFV-Enabled MEC Networks

Kiran, Nahida; Liu, Xuanlin; Wang, Sihua; Yin, Changchuan

doi:10.1109/wcncw48565.2020.9124910

Cited by 37 publications

(30 citation statements)

References 10 publications

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“…2) Resource allocation in edge and fog computing systems: This section is devoted to the research works addressing services consisting of multiple components, such as VMs, containers or tasks, but there is no relation among the constituent elements taken into account. Following our Platform components dimension, the underlying infrastructure, where the service components are mapped to, can be multi-edge [80], [95], [129], [131]- [133], [135], [137], [140], [169], [180], [182], cloud-edge [59], [64], [68], [70], [75], [127], [161], [170], [174], [176], [178] or multi-cloud [51] but the core problem to be tackled, i.e., component placement, is similar.…”

Section: B Multiple Componentsmentioning

confidence: 99%

“…At a first glance, some parts of this problem are related to the task of network planning (which is out-of-scope of this survey), however, if the optimization happens on a shorter time scale assuming highly dynamic environments, that moves those research papers into the focus of this survey due to the similar characteristics of the underlying mathematical problem. For example, in [137], [180], the authors target the problem of dynamic placement of service-hosting nodes over a SDNbased, NFV-enabled MEC architecture in order to minimize operational costs. Authors of [137] focus on VNF replication capabilities, while [180] focuses on satisfying the servicelevel response time requirements.…”

Section: B Multiple Componentsmentioning

confidence: 99%

“…For example, in [137], [180], the authors target the problem of dynamic placement of service-hosting nodes over a SDNbased, NFV-enabled MEC architecture in order to minimize operational costs. Authors of [137] focus on VNF replication capabilities, while [180] focuses on satisfying the servicelevel response time requirements. The latter paper presents an online adaptive greedy heuristic algorithm, which is also capable of managing the service elasticity overhead that comes from auto-scaling and load balancing with a proposed capacity violation detection mechanism.…”

Section: B Multiple Componentsmentioning

confidence: 99%

“…The surveyed research papers dealing with the placement of multi-component services take different aspects into consideration. A large part of the research efforts target applications realized by a configured number of replicas of given service components [51], [64], [70], [77], [132], [133], [137]. Replicas can increase the reliability, availability, dependability, and coverage of a service.…”

Section: B Multiple Componentsmentioning

confidence: 99%

“…Other researchers combine the delay with energy [80], with utilization [51], [70], [169] or with load balancing [135]. Alternatively, in [137] VNF replication is considered as the most characteristic operational cost. In [95], which is driven by a telco use case, reliability is also taken into account besides revenue and delay, which stems from the special requirements of such services.…”

Section: B Multiple Componentsmentioning

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: B Multiple Componentsmentioning

confidence: 99%

Section: B Multiple Componentsmentioning

confidence: 99%

Section: B Multiple Componentsmentioning

confidence: 99%

Section: B Multiple Componentsmentioning

confidence: 99%

Section: B Multiple Componentsmentioning

confidence: 99%

See 3 more Smart Citations

Survey on Placement Methods in the Edge and Beyond

Sonkoly

Czentye

Szalay

et al. 2021

IEEE Commun. Surv. Tutorials

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Discrete spider monkey optimization algorithm for latency‐sensitive VNF deployment and resource allocation

Liu,

Hou,

Liu

2023

Int J Communication

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SummaryThe next‐generation networks are expected to support diverse latency‐sensitive services, that is, auto‐driving, telemedicine, and so on. Deploying these services in current hardware‐based networks faces significant challenges. The emergence of network function virtualization (NFV) and software defined network (SDN) makes network deployment flexible and agile. In order to meet the strict latency constraints, joint VNF deployment and resource allocation problems are needed. In this paper, we consider the resource‐delay interpendency and formulate the joint VNF deployment and resource allocation problems as linear programming (LP) problems. Discrete spider monkey optimization (DSMO) algorithm is proposed to solve this problem, which imitates spider monkeys' fission and fusion social behavior during foraging. The two‐phase decision update mechanism greatly increases the diversity of the spider monkey decision population and promotes the algorithm to perform a global search of the solution space. Evaluation results show that the proposed solutions achieve superior performance in terms of latency, CPU utilization and service acceptance rate.

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vDANE: Using virtualization for improving video quality with Server and Network Assisted DASH

2022

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Summary Network Function Virtualization (NFV) offers flexibility in traffic engineering and network resource management, by taking advantage of Software Defined Networking (SDN). By using these network technologies, it is possible to enhance the performance of video streaming applications by placing network functions in suitable locations and rerouting flows. Our study addresses the “virtual cache placement” problem in dynamic networks, where traffic patterns and attachment points of the clients are changing rapidly. The cache placement is done by determining how many virtual caches are necessary to be able to provide acceptable service to the clients, as well as where to place those caches to meet demand. To this end, we provide a heuristic solution by taking advantage of NFV‐SDN and having the assistance of Server and Network Assisted DASH (SAND). Experimental results show that the proposed algorithms can improve the video client rebuffering by 150%–270% and also can provide an 8%–12% increase in average bitrate received by the client, compared to a number of benchmark algorithms. The obtained results indicate that the co‐operation between the client and the operator of an SDN‐enabled network, by exchanging client and network information, allows network resources to be efficiently used, and as a consequence, the Quality of Experience (QoE) on the client's side is improved.

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VNF Placement and Resource Allocation in SDN/NFV-Enabled MEC Networks

Cited by 37 publications

References 10 publications

Survey on Placement Methods in the Edge and Beyond

Survey on Placement Methods in the Edge and Beyond

Discrete spider monkey optimization algorithm for latency‐sensitive VNF deployment and resource allocation

vDANE: Using virtualization for improving video quality with Server and Network Assisted DASH

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