Dynamic VNF placement, resource allocation and traffic routing in 5G

Golkarifard, Morteza; Chiasserini, Carla Fabiana; Malandrino, Francesco; Movaghar, Ali

doi:10.1016/j.comnet.2021.107830

Cited by 45 publications

(24 citation statements)

References 26 publications

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“…On the other hand, there are multiple options of edge nodes in the collected papers, because we omit research results on binary offloading decisions. Usually the challenges addressed in these papers stem from the choice of edge nodes, the latency requirements, Online [52], [118]- [124] [59], [125]- [128] [129]- [140] [100], [141]- [146] [141], [143], [147]- [153] [109], [111], [154]- [160] [113] Online with migration [161]- [167] [168]- [170] Hybrid [171], [172] [173]- [179] [180]- [182] [183] and compute capabilities. 5) Cloud-edge-terminal: Finally, the last category might be considered as an extension of the previous one by adding remote clouds as options to the offloading decision.…”

Section: A Platform Componentsmentioning

confidence: 99%

“…The vast of the papers [50], [52], [53], [72], [120]- [123], [171], [172] assume single-operator scenarios, i.e., the resources are owned and managed by a single provider, but some works [118], [119], [124] address the more complex and more challenging multi-operator setups. Of course, the later environments require business related questions as well to be investigated.…”

Section: Multiple Components With Connectionmentioning

confidence: 99%

“…The majority of the proposed algorithms are online methods and only the authors of [50], [52], [53], [72] provide an offline mechanism. In addition, the researchers in [171], [172] pro-pose an online-offline (hybrid) orchestration system, which receives and processes the incoming request in an online manner, and occasionally re-optimizes the total accumulated service deployment. The SGE problem is formalized as a mixedinteger linear program, solved using a greedy backtracking heuristic (online) and as an integer program (offline).…”

Section: Multiple Components With Connectionmentioning

confidence: 99%

“…In terms of Optimization objective(s), usually delay, revenue and utilization or some combination of them is targeted. In [120], [121], [172] only the revenue, in [118] only the delay, in [119] both delay and revenue are considered. In [171] delay and utilization, while in [123], [124] delay, utilization and revenue are taken into account in the optimization objective.…”

Section: Multiple Components With Connectionmentioning

confidence: 99%

“…Graph theory [118] Shortest path [51], [136] Bipartite graph matching Game theory [84] Best response search [97] Matching game Search algorithms [73], [106] Tabu search Evolutionary algorithms [182] Particle swarm optimization Other heuristic solution [54], [58], [74], [83], [90], [103], [105], [107], [111], [121], [163], [177], [180] n/a [49] MILP Solver [57] Linear programming [59] LP with fractal solution rounding [52], [53] Column generation Convex optimization [117] Semidefinite relaxation Stochastic control [127] Cache replacement policies Assignment method [131], [140] Hungarian method Search algorithms [79] Branch and bound Other heuristic solution [91], [146], [169], [171], [172], [174] Nonlinear [56], [60] Hungarian method…”

Section: A Problem Formulationmentioning

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: A Platform Componentsmentioning

confidence: 99%

Section: Multiple Components With Connectionmentioning

confidence: 99%

Section: Multiple Components With Connectionmentioning

confidence: 99%

Section: Multiple Components With Connectionmentioning

confidence: 99%

Section: A Problem Formulationmentioning

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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Dynamic and efficient resource allocation for 5G end‐to‐end network slicing: A multi‐agent deep reinforcement learning approach

Asim Ejaz,

Wu,

Iqbal

2024

Int J Communication

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SummaryThe rapid evolution of user equipment (UE) and 5G networks drives significant transformations, bringing technology closer to end‐users. Managing resources in densely crowded areas such as airports, train stations, and bus terminals poses challenges due to diverse user demands. Integrating mobile edge computing (MEC) and network function virtualization (NFV) becomes vital when the service provider's (SP) primary goal is maximizing profitability while maintaining service level agreement (SLA). Considering these challenges, our study addresses an online resource allocation problem in an MEC network where computing resources are limited, and the SP aims to boost profit by securely admitting more UE requests at each time slot. Each UE request arrival rate is unknown, and the requirement is specific resources with minimum cost and delay. The optimization problem objective is achieved by allocating resources to requests at the MEC network in appropriate cloudlets, utilizing abandoned instances, reutilizing idle and soft slice instances to shorten delay and reduce costs, and immediately scaling inappropriate instances, thus minimizing the instantiation of new instances. This paper proposes a deep reinforcement learning (DRL) method for request prediction and resource allocation to mitigate unnecessary resource waste. Simulation results demonstrate that the proposed approach effectively accepts network slice requests to maximize profit by leveraging resource availability, reutilizing instantiated resources, and upholding goodwill and SLA. Through extensive simulations, we show that our proposed DRL‐based approach outperforms other state‐of‐the‐art techniques, namely, MaxSR, DQN, and DDPG, by 76%, 33%, and 23%, respectively.

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A-DDPG: Attention Mechanism-Based Deep Reinforcement Learning for NFV

Yang

et al. 2022

Resource Allocation in Network Function Virtualization

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The efficacy of Network Function Virtualization (NFV) depends critically on (1) where the virtual network functions (VNFs) are placed and (2) how the traffic is routed. Unfortunately, these aspects are not easily optimized, especially under time-varying network states with different quality of service (QoS) requirements. Given the importance of NFV, many approaches have been proposed to solve the VNF placement and traffic routing problem. However, those prior approaches mainly assume that the state of the network is static and known, disregarding real-time network variations. To bridge that gap, in this paper, we formulate the VNF placement and traffic routing problem as a Markov Decision Process model to capture the dynamic network state transitions. In order to jointly minimize the delay and cost of NFV providers and maximize the revenue, we devise a customized Deep Reinforcement Learning (DRL) algorithm, called A-DDPG, for VNF placement and traffic routing in a real-time network. A-DDPG uses the attention mechanism to ascertain smooth network behavior within the general framework of network utility maximization (NUM). The simulation results show that A-DDPG outperforms the state-ofthe-art in terms of network utility, delay, and cost.

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Dynamic VNF placement, resource allocation and traffic routing in 5G

Cited by 45 publications

References 26 publications

Survey on Placement Methods in the Edge and Beyond

Survey on Placement Methods in the Edge and Beyond

Dynamic and efficient resource allocation for 5G end‐to‐end network slicing: A multi‐agent deep reinforcement learning approach

A-DDPG: Attention Mechanism-Based Deep Reinforcement Learning for NFV

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