Provably Efficient Algorithms for Placement of Service Function Chains with Ordering Constraints

Tomassilli, Andrea; Giroire, Frédéric; Huin, Nicolas; Pérennès, Stéphane

doi:10.1109/infocom.2018.8486275

Cited by 75 publications

(37 citation statements)

References 25 publications

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“…The conventional service placement, which in reality comes down to a problem of bin-packing or knap-sack, is one of the most studied problems in the literature [9]. This case, although very simplified compared to the use cases we are considering in this paper, is known to be NP-hard [10].…”

Section: Related Workmentioning

confidence: 99%

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: Related Workmentioning

confidence: 99%

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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“…The problem of how to meet the demand from all of the flows with a minimum cost (e.g., in terms of the number of instantiated instances) is considered in [4], [10]. An extension of these work considers the setting where each flow must traverse a chain of network functions instead of just one function [11]. A similar problem is also considered in [7], [12] but for an online setting where flows arrive and leave in an online fashion.…”

Section: Related Workmentioning

confidence: 99%

Joint Placement and Allocation of Virtual Network Functions with Budget and Capacity Constraints

Sallam

2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

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With the advent of Network Function Virtualization (NFV), network services that traditionally run on proprietary dedicated hardware can now be realized using Virtual Network Functions (VNFs) that are hosted on general-purpose commodity hardware. This new network paradigm offers a great flexibility to Internet service providers (ISPs) for efficiently operating their networks (collecting network statistics, enforcing management policies, etc.). However, introducing NFV requires an investment to deploy VNFs at certain network nodes (called VNF-nodes), which has to account for practical constraints such as the deployment budget and the VNF-node capacity. To that end, it is important to design a joint VNF-nodes placement and capacity allocation algorithm that can maximize the total amount of network flows that are fully processed by the VNF-nodes while respecting such practical constraints. In contrast to most prior work that often neglects either the budget constraint or the capacity constraint, we explicitly consider both of them. We prove that accounting for these constraints introduces several new challenges. Specifically, we prove that the studied problem is not only NP-hard but also non-submodular. To address these challenges, we introduce a novel relaxation method such that the objective function of the relaxed placement subproblem becomes submodular. Leveraging this useful submodular property, we propose two algorithms that achieve an approximation ratio of 1 2 (1 − 1/e) and 1 3 (1 − 1/e) for the original non-relaxed problem, respectively. Finally, we corroborate the effectiveness of the proposed algorithms through extensive evaluations using both trace-driven simulations and simulations based on synthesized network settings.

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“…The placement problem has been considered in different domains such as NFV (e.g., [5]), SDN (e.g., [3]), and edge cloud computing (e.g., [12]). In NFV, several studies (e.g., [4], [13], [14]) consider the placement of a minimum number of VNF instances to cover all flows. A single type of network functions is considered in [4], [13], [15], [16], and the case of multiple network functions is considered in [14], [17], [18], [19], [20].…”

Section: Related Workmentioning

confidence: 99%

“…They consider a budget constraint but neglect the limited resources constraint. Similarly, in the work on edge cloud computing [14], although the budget and resource constraints are considered, their proposed solution is only for a special case, and the overall problem does not consider the multidimensional setting. To the best of our knowledge, the multidimensional setting has rarely been considered except in a limited number of studies.…”

Section: Related Workmentioning

confidence: 99%

Placement and Allocation of Virtual Network Functions: Multi-dimensional Case

Sallam

Zheng

2019

2019 IEEE 27th International Conference on Network Protocols (ICNP)

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Network function virtualization (NFV) is an emerging design paradigm that replaces physical middlebox devices with software modules running on general purpose commodity servers. While gradually transitioning to NFV, Internet service providers face the problem of where to introduce NFV in order to make the most benefit of that; here, we measure the benefit by the amount of traffic that can be serviced through the NFV. This problem is non-trivial as it is composed of two challenging subproblems: 1) placement of nodes to support virtual network functions (referred to as VNF-nodes); and 2) allocation of the VNF-nodes resources to network flows; the two subproblems need to be considered jointly to satisfy the objective of serving the maximum amount of traffic. This problem has been studied recently but for the one-dimensional setting, where all network flows require one network function, which requires a unit of resource to process a unit of flow. In this work, we extend to the multi-dimensional setting, where flows can require multiple network functions, which can also require a different amount of each resource to process a unit of flow. The multi-dimensional setting introduces new challenges in addition to those of the onedimensional setting (e.g., NP-hardness and non-submodularity) and also makes the resource allocation a multi-dimensional generalization of the generalized assignment problem with assignment restrictions. To address these difficulties, we propose a novel two-level relaxation method and utilize the primal-dual technique to design two approximation algorithms that achieve an approximation ratio of (e−1)(Z−1) 2e 2 Z(kR) 1/(Z−1) and (e−1)(Z−1) 2e(Z−1+eZR 1/(Z−1) ) , where k (resp. R) is the number of VNF-nodes (resp. resources), and Z is a measure of the available resource compared to flow demand. Finally, we perform extensive trace-driven simulations to show the effectiveness of the proposed algorithms.

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Provably Efficient Algorithms for Placement of Service Function Chains with Ordering Constraints

Cited by 75 publications

References 25 publications

A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding

A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding

Joint Placement and Allocation of Virtual Network Functions with Budget and Capacity Constraints

Placement and Allocation of Virtual Network Functions: Multi-dimensional Case

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