Joint Optimization of Chain Placement and Request Scheduling for Network Function Virtualization

Zhang, Qixia; Xiao, Yikai; Liu, Fangming; Lui, John C. S.; Guo, Jian; Tao, Wang

doi:10.1109/icdcs.2017.232

Cited by 133 publications

(58 citation statements)

References 14 publications

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“…For instance, the authors in [11] assumed that each physical link has a particular delay which is not completely true. A similar assumption of loss rate can be found in [14]. In fact, latency and traffic traversing through the link have a non-linear relation [15].…”

Section: A Mathematical Optimisation-based Approachesmentioning

confidence: 73%

A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding

Quang

Hadjadj‐Aoul

Outtagarts

2019

IEEE Trans. Netw. Serv. Manage.

123

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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 Mathematical Optimisation-based Approachesmentioning

confidence: 73%

A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding

Quang

Hadjadj‐Aoul

Outtagarts

2019

IEEE Trans. Netw. Serv. Manage.

123

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“…To ensure those priorities, function deployment plays one of the most important roles in this architecture. We know from existing researches [5][6][7][8][9][10][11][12] that different network function deployment schemes have a significant impact on traffic processing performance as well as cost of system resources and under the constraint of resources, the optimal function deployment to meet all SFCs is very complicated and difficult.…”

Section: Related Workmentioning

confidence: 99%

“…As for our knowledge, there exists no architecture like ours that not only could provide data plane of switch with flexible and customized network functions, but also could implement management of network functions and packet scheduling from level of architecture. Other existing researches only solve sub-problems [5][6][7][8]. In this paper, Firstly, we design RPSA architecture, which solves the problem of poor scalability and flexibility of switch by attaching NFV technology to it.…”

Section: Introductionmentioning

confidence: 99%

Optimized Deployment of Network Function for Resource Pooling Switch

2018

2018 IEEE 4th International Conference on Computer and Communications (ICCC)

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The disadvantages of the combination of traditional switches and middleboxes have being exposed under the condition of increasingly various network function demands,such as function flexibility, performance scalability and resource utilization. To solve this problem, we design Resource Pooling Switch Architecture (RPSA), which separates some non-essential functions from line card and allocate them in Network Function Pool (NFP) to provide flexible services for data plane in the form of Service Function Chains (SFC). As the performance of the whole system could be decided by whether function deployment is reasonable or not, we purpose heuristic algorithm called Modified Fiduccia-Mattheyses based Two Phase Algorithm (MFMTP) to optimize the deployment of functions. The simulation results show that this algorithm performs well in throughput and convergence.

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“…Service requirements in NFV are characterized by network flows, and the packets need to pass through the ordered set of VNFs before reaching the destination. Such specific order is studied as the so-called Service Function Chain (SFC) in the NFV literature (see [12], [18], [19]). Vast works on NFV are done under the SFC constraints.…”

Section: Introductionmentioning

confidence: 99%

Online Joint Placement and Allocation of Virtual Network Functions With Heterogeneous Servers

Chau

et al. 2020

IEEE Internet Things J.

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Network Function Virtualization (NFV) is a promising virtualization technology that has the potential to significantly reduce the expenses and improve the service agility. NFV makes it possible for Internet Service Providers (ISPs) to employ various Virtual Network Functions (VNFs) without installing new equipments. One of the most attractive approaches in NFV technology is a so-called Joint Placement and Allocation of Virtual Network Functions (JPA-VNF) which considers the balance between VNF investment with Quality of Services (QoS). We introduce a novel capability function to measure the potential of locating VNF instances for each server in the proposed OJPA-HS model. This model allows the servers in the network to be heterogeneous, at the same time combines and generalizes many classical JPA-VNF models. Despite its NP-hardness, we present a provable best-possible deterministic online algorithm based on dynamic programming (DP). To conquer the high complexity of DP, we propose two additional randomized heuristics, the Las Vegas (LV) and Monte Carlo (MC) randomized algorithms, which performs even as good as DP with much smaller complexity. Besides, MC is a promising heuristic in practice as it has the advantage to deal with big data environment. Extensive numerical experiments are constructed for the proposed algorithms in the paper.

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Joint Optimization of Chain Placement and Request Scheduling for Network Function Virtualization

Cited by 133 publications

References 14 publications

A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding

A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding

Optimized Deployment of Network Function for Resource Pooling Switch

Online Joint Placement and Allocation of Virtual Network Functions With Heterogeneous Servers

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