Optimal placement of virtual network functions in software defined networks: A survey

Demirci, Sedef; Sağıroğlu, Şeref

doi:10.1016/j.jnca.2019.102424

Cited by 32 publications

(9 citation statements)

References 40 publications

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“…Note that the problem under study in this work belongs to the first type of problem as we consider a single type of network functions. We refer the reader to Demirci and Sagiroglu (2019) for a general overview of the literature on the optimal placement of virtual network functions and focus in what follows on the specific context of DDoS mitigation.…”

Section: Optimal Placement Of Virtual Network Functionsmentioning

confidence: 99%

Optimal deployment of virtual network functions for securing telecommunication networks against distributed denial of service attacks: A robust optimization approach

Haddad-Vanier

Gicquel²,

Papadimitriou³

2022

Computers & Operations Research

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Section: Optimal Placement Of Virtual Network Functionsmentioning

confidence: 99%

Optimal deployment of virtual network functions for securing telecommunication networks against distributed denial of service attacks: A robust optimization approach

Haddad-Vanier

Gicquel²,

Papadimitriou³

2022

Computers & Operations Research

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“…Further, a realistic vCDN delay model must incorporate VNF instantiation times, as they may notably augment the starting delay of any video-streaming session. Finally, both instantiation time and resource consumption may differ significantly depending on the specific characteristics of each VNF [3].…”

Section: Problem Definitionmentioning

confidence: 99%

“…In other words, if i is a CP node, then z r i,j,cache will be 1 only if f cache j was not ingesting content from i at the time of receiving the assignation of f cache r . On the other hand, whenever f k r is assigned to f k i , but f k i is not instantiated at the beginning of t, then the VNO will instantiate f k i , but adequate delay penalties are added to RTT r , as shown in (3). Notice that we approximate the VNF instantiation states in the following manner: Any VNF that is not instantiated during t and receives a VNF request to manage starts its own instantiation and finishes such instantiation process at the beginning of the t + 1.…”

Section: Penalty Terms and Feasibility Constraintsmentioning

confidence: 99%

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Online Service Function Chain Deployment for Live-Streaming in Virtualized Content Delivery Networks: A Deep Reinforcement Learning Approach

et al. 2021

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Video delivery is exploiting 5G networks to enable higher server consolidation and deployment flexibility. Performance optimization is also a key target in such network systems. We present a multi-objective optimization framework for service function chain deployment in the particular context of Live-Streaming in virtualized content delivery networks using deep reinforcement learning. We use an Enhanced Exploration, Dense-reward mechanism over a Dueling Double Deep Q Network (E2-D4QN). Our model assumes to use network function virtualization at the container level. We carefully model processing times as a function of current resource utilization in data ingestion and streaming processes. We assess the performance of our algorithm under bounded network resource conditions to build a safe exploration strategy that enables the market entry of new bounded-budget vCDN players. Trace-driven simulations with real-world data reveal that our approach is the only one to adapt to the complexity of the particular context of Live-Video delivery concerning the state-of-art algorithms designed for general-case service function chain deployment. In particular, our simulation test revealed a substantial QoS/QoE performance improvement in terms of session acceptance ratio against the compared algorithms while keeping operational costs within proper bounds.

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“…NFV proposes the separation between hardware and Virtualized Network Functions (VNFs) that are comparable to classic networking components in NV. Recently, NFV has raised the challenges of VNF placement (VNF-P) [10].…”

Section: Introductionmentioning

confidence: 99%

Dynamic and Parallel Resource Allocation with Analytical Performance Estimation for Virtualized Environment

Qiao¹

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In this thesis, we propose an integrated approach for estimating the performance of virtual network resource allocation and a Genetic Algorithm (GA) based mechanism for online dynamic resource allocation in virtualized environment.The integrated approach is empowered by a novel loss network model with Dynamic Routing And Random Topology (DRART), which is combined with some existing models to create a synergy across different levels through an effective recursive process. Numerical results show the proposed integrated approach can provide accurate predictions on the performances of general virtual network embedding algorithms.We propose a virtual link mapping solution, i.e., Segment Based Genetic Algorithm (SBGA), which provides new definitions for genes and chromosomes in the Genetic Algorithm. Our SBGA approach enables parallel processing for searching optimal allocations. Our theoretical analysis shows that the execution time of our approach can be reduced to logarithmic time. To map virtual nodes and links to physical ones in one stage, we further develop an approach, named as GAOne. Our proposed GAOne approach applies the two-color graph coloring in graph theory to guide the crossover process in the Genetic Algorithm (GA) for valid solutions. Our simulation results show that the proposed GAOne approach is fast and efficient for online resource allocation applications in virtualized environment. i At this point, my Ph.D. journey is coming to the end. In the past six years, I've thought of giving up many times. Fortunately, I decided to persevere. First and foremost, I would like to express my deep and sincere gratitude to my supervisor Dr. Changcheng Huang. I really appreciate that I had the opportunity to work and study under his guidance. He taught me the research methodology and gave me invaluable suggestions. His guidance and encouragement helped me throughout my Ph.D. study. I am extremely grateful for his patience, enthusiasm and rigor.My sincere thanks go to my thesis committee: Dr.

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Optimal placement of virtual network functions in software defined networks: A survey

Cited by 32 publications

References 40 publications

Optimal deployment of virtual network functions for securing telecommunication networks against distributed denial of service attacks: A robust optimization approach

Optimal deployment of virtual network functions for securing telecommunication networks against distributed denial of service attacks: A robust optimization approach

Online Service Function Chain Deployment for Live-Streaming in Virtualized Content Delivery Networks: A Deep Reinforcement Learning Approach

Dynamic and Parallel Resource Allocation with Analytical Performance Estimation for Virtualized Environment

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