A fast robust optimization-based heuristic for the deployment of green virtual network functions

Marotta, Antonio; Zola, Enrica; D’Andreagiovanni, Fabio; Kassler, Andreas

doi:10.1016/j.jnca.2017.07.014

Cited by 44 publications

(16 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We compare the performance of E 2 D 2 PG with a heuristic approach, First-Fit-Dijkstra (FFD), and DDPG-HFA. FFD approach adopts First-Fit algorithm for allocating VNFs to the substrate network and Dijkstra algorithm (the shortest path in terms of the number of hops) to define the has been used as one of the baselines to assess the performance in [57]- [59]. With given VNF allocation, Dijkstra algorithm could be adopted to determine the path to connect VNFs [60].…”

Section: B Impact Of Hfamentioning

confidence: 99%

A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding

Quang

Hadjadj‐Aoul

Outtagarts

2019

IEEE Trans. Netw. Serv. Manage.

121

View full text Add to dashboard Cite

Network Function Virtualization (NFV) and service orchestration simplify the deployment and management of network and telecommunication services. The deployment of these services requires, typically, the allocation of Virtual Network Function -Forwarding Graph (VNF-FG), which implies not only the fulfillment of the service's requirements in terms of Quality of Service (QoS), but also considering the constraints of the underlying infrastructure. This topic has been well-studied in existing literature, however, its complexity and uncertainty of available information unveil challenges for researchers and engineers. In this paper, we explore the potential of reinforcement learning techniques for the placement of VNF-FGs. However, it turns out that even the most well-known learning technique is ineffective in the context of a large-scale action space. In this respect, we propose approaches to find out feasible solutions while improving significantly the exploration of the action space. The simulation results clearly show the effectiveness of the proposed learning approach for this category of problems. Moreover, thanks to the deep learning process, the performance of the proposed approach is improved over time.

show abstract

Section: B Impact Of Hfamentioning

confidence: 99%

A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding

Quang

Hadjadj‐Aoul

Outtagarts

2019

IEEE Trans. Netw. Serv. Manage.

121

View full text Add to dashboard Cite

show abstract

“…Évora supports user NSCs across the edge VNFaaS and has a broader problem space. Leveraging network softwarization, system administrators may configure the networks to adhere to a specific set of policies, such as energy and carbon efficiency . The system and user goals can be complementary or even contradictory to one another.…”

Section: Related Workmentioning

confidence: 99%

Composing network service chains at the edge: A Resilient and adaptive software‐defined approach

Kathiravelu

Roy

Veiga

2018

Trans Emerging Tel Tech

View full text Add to dashboard Cite

The scale of data and the demand for high data rates in applications are rapidly increasing over the years. By moving the computing resources closer to the users, edge computing minimizes the latency caused by otherwise pulling the data between the user and a centralized platform. Thus, data centers and computing nodes at the edge continue to overtake or replace traditional centralized cloud platforms in various 5G applications and use cases. Network functions virtualization virtualizes dedicated hardware network middleboxes into virtual network functions (VNFs) and deploys them on top of servers and data centers. Due to the inherent bandwidth cost associated with the distance between the network services and the end user, more and more third‐party VNF providers choose to deploy their services at the edge, rather than in a centralized cloud. A typical network service chaining (NSC) consumes various network services, where one's output becomes the input of another. As network services are often latency sensitive, NSC needs to be carried out, maintaining proximity to the user as well as among the services composing the NSC. In this paper, we present the design of a resilient and adaptive framework, following an approach inspired by software‐defined networking, for the remote users to construct network service chains in the mobile and edge computing environments. Our evaluations highlight the efficiency and optimality of the framework in finding the best fit among several third‐party edge VNFs in deploying the user network service chains, abiding by various user policies.

show abstract

“…In basic separation architecture, an MBS manages the control signal and an SBS manages both low rate data traffic and high rate data traffic. In the considered modified Works on the optimization models and solution algorithms for the design and management of 5G networks have been carried out actively recently [34][35][36][37][38]. In [34], the authors design reusable functional block (RFB)-based 5G network architecture and consider the optimization problem of dynamically managing the RFBs by either maximizing user throughput or minimizing the number of used nodes subject to RFBs placement, 5G node capacity, user coverage and user data constraints.…”

Section: Proposed Energy Saving Schemementioning

confidence: 99%

“…In [34], the authors design reusable functional block (RFB)-based 5G network architecture and consider the optimization problem of dynamically managing the RFBs by either maximizing user throughput or minimizing the number of used nodes subject to RFBs placement, 5G node capacity, user coverage and user data constraints. In [35], the authors develop a robust mixed integer optimization model for minimizing the power consumption of network function virtualization (NFV) infrastructure with uncertain resource demands by the virtual network functions (VNFs). Since it takes a very long time to solve the robust optimization problem using an exact solver, the authors develop a fast three-phase heuristic, i.e., green and robust VNFs placement.…”

Section: Proposed Energy Saving Schemementioning

confidence: 99%

An Efficient Energy Saving Scheme for Base Stations in 5G Networks with Separated Data and Control Planes Using Particle Swarm Optimization

Kang

Chung

2017

Energies

View full text Add to dashboard Cite

Abstract:Reducing energy consumption of mobile communication networks has gained significant attentions since it takes a major part of the total energy consumption of information and communication technology (ICT). In this paper, we consider 5G networks with heterogeneous macro cells and small cells, where data and control planes are separated. We consider two types of data traffic, i.e., low rate data traffic and high rate data traffic. In basic separation architecture, a macro cell base station (MBS) manages control signals, while a small cell base station (SBS) manages both low rate data traffic and high rate data traffic. In the considered modified separation architecture, an MBS manages control signals and low rate data traffic, while an SBS manages high rate data traffic. Then, an efficient energy saving scheme for base stations (BSs) is proposed, where the state of a BS is determined depending on the number of user equipments (UEs) that request high rate data traffic and the number of UEs that exist under the overlapping areas commonly covered by the considered BS and the neighbor BSs. We formulate an optimization problem for the proposed energy saving scheme and obtain the solution using particle swarm optimization (PSO). Numerical results show that the proposed energy saving scheme in the modified separated network architecture has better energy efficiency compared to the conventional energy saving schemes in both basic and modified separated network architectures. Also, the proposed energy saving scheme has lower aggregate delay.

show abstract

A fast robust optimization-based heuristic for the deployment of green virtual network functions

Cited by 44 publications

References 33 publications

A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding

A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding

Composing network service chains at the edge: A Resilient and adaptive software‐defined approach

An Efficient Energy Saving Scheme for Base Stations in 5G Networks with Separated Data and Control Planes Using Particle Swarm Optimization

Contact Info

Product

Resources

About