Single and Multi-Domain Adaptive Allocation Algorithms for VNF Forwarding Graph Embedding

Quang, Pham Tran Anh; Bradai, Abbas; Singh, Kamal Deep; Picard, Gauthier; Riggio, Roberto

doi:10.1109/tnsm.2018.2876623

Cited by 45 publications

(44 citation statements)

References 26 publications

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“…In existing literature [17], [35], only bandwidth has been taken into account in the problem formulation. In this paper, we also consider other QoS requirements since they also impact the success of VNF-FG embedding.…”

Section: A Problem Formulationmentioning

confidence: 99%

“…(1), (2), (3), (4), (5), (6), (7), (8) Solving the above optimization problem is not trivial due to the difficulties in estimations of the real latency and loss rate in (4) and (5). Without considering the QoS constraints, this optimization problem can be solved by using ILP solvers or heuristic algorithms [8], [35]. However, it is non-trivial to model QoS metrics such as loss rate and latency.…”

Section: Thus We Havementioning

confidence: 99%

“…The requests of VNFs and VLs at time-step t are described in s t and allocation of VNFs and VLs in the substrate network is described in a t . The acceptance ratio (AR) has been adopted to assess the performance of VNF-FG embedding algorithms [35], [45]. The reward at time-step t (r t ) depends on the requests (s t ) and allocation (a t ).…”

Section: E Rewardsmentioning

confidence: 99%

See 2 more Smart Citations

A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding

Quang

Hadjadj‐Aoul

Outtagarts

2019

IEEE Trans. Netw. Serv. Manage.

Self Cite

111

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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.

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Section: A Problem Formulationmentioning

confidence: 99%

Section: Thus We Havementioning

confidence: 99%

See 1 more Smart Citation

A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding

Quang

Hadjadj‐Aoul

Outtagarts

2019

IEEE Trans. Netw. Serv. Manage.

Self Cite

111

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show abstract

“…Moreover, it requires a continuous substrate path in order to connect the substrate nodes n and m which are the host of VNFs n and m , respectively. Following the model proposed in [12], we have the following constraints…”

Section: Problem Formulationmentioning

confidence: 99%

“…(1), (2), (3), (4), (5), (6), (7) Due to the difficulties in determining the model of the latency and loss rate ((4) and (5)) in multi-hop network, it is not trivial to solve the above optimization problem. Without considering the QoS constraints, this optimization problem can be solved by using Integer Linear Programming (ILP) solvers or heuristic algorithms [12], [13]. Thanks to abilities of DRL in learning complex characteristics of networking [14], it is able to determine the sub-optimal VNF-FG allocation.…”

Section: Problem Formulationmentioning

confidence: 99%

Evolutionary Actor-Multi-Critic Model for VNF-FG Embedding

Quang

Hadjadj‐Aoul

Outtagarts

2020

2020 IEEE 17th Annual Consumer Communications &Amp; Networking Conference (CCNC)

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The placement of Virtual Network Function -Forwarding Graphs (VNF-FGs) is one of the basic operations in the networks of the future. Being NP-hard, several heuristics and metaheuristics have been proposed. However, these approaches are inefficient due to the need to recalculate the solution at each service placement. In this paper, we adapt one of the most advanced approaches in Deep Reinforcement Learning (DRL), in order to improve exploration by generalizing the neural network calculating action values. We also propose an evolutionary algorithm to evolve these neural networks in order to discover better ones, which also avoids getting stuck in local minima. In order to avoid going through the almost innumerable number of infeasible solutions, we propose a heuristic, which combined with our DRL, makes it possible to guarantee the feasibility of the solutions and therefore to make the placement much more efficient. The simulation results we obtained confirm the quality of the solutions obtained as well as the superiority of the proposed solution over the existing one.

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The intelligent multi‐domain service function chain deployment: Architecture, challenges and solutions

Zhang

Wang

Dong

et al. 2020

Int J Communication

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Network function virtualization (NFV) technology achieves flexible service deployment by replacing the middleboxes with virtual network functions (VNFs). In NFV, a set of VNFs are chained in a given order, called service function chain (SFC), and accordingly, data flow is steered to traverse all the VNFs in order to offer a service. With a large number of network devices and end users being connected into Internet, there is a growing demand for largescale multi-domain networks to dynamically deploy the SFC across multiple network domains, in order to support efficient service provisioning. To this end, in this paper, we first investigate the state of the art of multi-domain SFC deployment, and then propose an intelligent multi-domain SFC deployment (IMSD) architecture by leveraging software-defined networking (SDN), NFV, and deep learning technologies. Furthermore, we discuss the potential challenges to realize the IMSD and provide some promising solutions. K E Y W O R D S deep learning, multi-domain networks, SDN, SFC deployment 1 | INTRODUCTION Recently, a great variety of middleboxes have been designed to efficiently provide various network services. 1 However, the service provisioning method is not flexible and leads to large capital expenditure (CAPEX) and operational expenditure (OPEX) since the middleboxes are heavily dependent on specific hardware devices. Fortunately, the emergence of network function virtualization (NFV) technology provides a promising solution to address the issues. NFV decouples network functions from hardware devices and implements them in software, called virtual network functions (VNFs). 2,3 Unlike the middleboxes, the VNFs can be flexibly deployed in the commercial-off-the-shelf devices. NFV

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Single and Multi-Domain Adaptive Allocation Algorithms for VNF Forwarding Graph Embedding

Cited by 45 publications

References 26 publications

A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding

A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding

Evolutionary Actor-Multi-Critic Model for VNF-FG Embedding

The intelligent multi‐domain service function chain deployment: Architecture, challenges and solutions

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