Joint NFV placement and routing for multicast service on SDN

Zhang, Sai Qian; Tizghadam, Ali; Park, Byungchul; Bannazadeh, Hadi; Leon-Garcia, Alberto

doi:10.1109/noms.2016.7502829

Cited by 29 publications

(14 citation statements)

References 12 publications

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“…Next, we compare the proposed heuristic algorithm (JPR) with the heuristic algorithm named HA-TAA 2 in [16]. For a fair comparison, we consider only the single-path scenario.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In [16], the HA-TAA algorithm first finds the shortest path from the source to the NFV nodes that can host the required NFs sequentially, and the shortest path from the last NFV node to the respective closest destination. Second, it connects the destinations through an MST.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…, |V r |}, in packet/s. An NF instance belongs to one service request, and it cannot be shared among multiple NF chains [15], [16], [19], [21].…”

Section: Multicast Nf Chainsmentioning

confidence: 99%

“…B(l) ≥ d r then allocate transmission resource for each kth path (W t,k i,i+1 ) using (28); success ← true; break; 15 end 16 if success = false then break; 17 end 18 if success = false then G v ← none; 19 else return G v ; throughput with least provisioning cost. Here, we consider a static algorithm, i.e., service requests are available a priori.…”

Section: B Multi-service Scenariomentioning

confidence: 99%

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A Virtual Network Customization Framework for Multicast Services in NFV-Enabled Core Networks

Alhussein

Thinh

et al. 2020

IEEE J. Select. Areas Commun.

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The paradigm of network function virtualization (NFV) with the support of software defined networking (SDN) emerges as a promising approach for customizing network services in fifth generation (5G) networks. In this paper, a multicast service orchestration framework is presented, where joint traffic routing and virtual network function (NF) placement are studied for accommodating multicast services over an NFVenabled physical substrate network. First, we investigate a joint routing and NF placement problem for a single multicast request accommodated over a physical substrate network, with both single-path and multipath traffic routing. The joint problem is formulated as a mixed integer linear programming (MILP) problem to minimize the function and link provisioning costs, under the physical network resource constraints, flow conservation constraints, and NF placement rules; Second, we develop an MILP formulation that jointly handles the static embedding of multiple service requests over the physical substrate network, where we determine the optimal combination of multiple services for embedding and their joint routing and placement configurations, such that the aggregate throughput of the physical substrate is maximized, while the function and link provisioning costs are minimized. Since the presented problem formulations are NPhard, low complexity heuristic algorithms are proposed to find an efficient solution for both single-path and multipath routing scenarios. Simulation results are presented to demonstrate the effectiveness and accuracy of the proposed heuristic algorithms.

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“…Next, we compare the proposed heuristic algorithm (JPR) with the heuristic algorithm named HA-TAA 2 in [16]. For a fair comparison, we consider only the single-path scenario.…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

“…, |V r |}, in packet/s. An NF instance belongs to one service request, and it cannot be shared among multiple NF chains [15], [16], [19], [21].…”

Section: Multicast Nf Chainsmentioning

confidence: 99%

Section: B Multi-service Scenariomentioning

confidence: 99%

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A Virtual Network Customization Framework for Multicast Services in NFV-Enabled Core Networks

Alhussein

Thinh

et al. 2020

IEEE J. Select. Areas Commun.

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“…Therefore, the competitive-ratio of the admission mechanism for the multicast services is O(2 max{ϕ, φ}) = O(max{ϕ, φ}). Algorithm 2: JRP algorithm for a single service request for n k ∈ F k do 8 Add l ← (n k , n k+1 ) to L I ; 9 C l (r) = C(n k ); 10 end 11 end 12 For multicast services, find an MST-based Steiner tree from s 0 to D | V | , while utilizing the cost functions in (25) and (26), and save on P; 13 For unicast services, find a Dijkstra shortest path from s 0 to t | V | , while utilizing the cost functions in (25) and (26), and save on P; 14 return P; component of the runtime is due to the Dijkstra shortest path for unicast services and MST-based Steiner tree for multicast services that is run over the network transformation which has |N |(K + 1) nodes.…”

Section: A Auxiliary Network Transformation and Routing And Nf Placementioning

confidence: 99%

Robust Online Composition, Routing and NF Placement for NFV-Enabled Services

Alhussein

Zhuang

2020

IEEE J. Select. Areas Commun.

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Network function virtualization (NFV) fosters innovation in the networking field and reduces the complexity involved in managing modern-day conventional networks. Via NFV, the provisioning of a network service becomes more agile, whereby virtual network functions can be instantiated on commodity servers and data centers on demand. Network functions can be either mandatory or best-effort. The former type is strictly necessary for the correctness of a network service, whereas the latter is preferrable yet not necessary. In this paper, we study the online provisioning of NFV-enabled network services. We consider both unicast and multicast NFV-enabled services with multiple mandatory and best-effort NF instances. We propose a primal-dual based online approximation algorithm that allocates both processing and transmission resources to maximize a profit function, subject to resource constraints on physical links and NFV nodes. The online algorithm resembles a joint admission mechanism and an online composition, routing and NF placement framework. The online algorithm is derived from an offline formulation through a primal-dual based analysis. Such analysis offers direct insights and a fundamental understanding on the nature of the profit-maximization problem for NFV-enabled services with multiple resource types.

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Cost minimization in placing service chains for virtualized network functions

Wang

Lin

Wang

et al. 2019

Int J Communication

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Network function virtualization (NFV) places network functions onto the virtual machines (VMs) of physical machines (PMs) located in data centers. In practice, a data flow may pass through multiple network functions, which collectively form a service chain across multiple VMs residing on the same or different PMs. Given a set of service chains, network operators have two options for placing them: (a) minimizing the number of VMs and PMs so as to reduce the server rental cost or (b) placing VMs running network functions belonging to the same service chain on the same or nearby PMs so as to reduce the network delay. In determining the optimal service chain placement, operators face the problem of minimizing the server cost while still satisfying the end-to-end delay constraint. The present study proposes an optimization model to solve this problem using a nonlinear programming (NLP) approach. The proposed model is used to explore various operational problems in the service chain placement field. The results suggest that the optimal cost ratio for PMs with high, hybrid, and low capacity, respectively, is equal to 4:2:1. Meanwhile, the maximum operating utilization rate should be limited to 55% in order to minimize the rental cost. Regarding quality of service (QoS) relaxation, the server cost reduces by 20%, 30%, and 32% as the end-to-end delay constraint is relaxed from 40 to 60, 80, and 100 ms, respectively. For the server location, the cost decreases by 25% when the high-capacity PMs are decentralized rather than centralized. Finally, the cost reduces by 40% as the repetition rate in the service chain increases from 0 to 2.A heuristic algorithm, designated as common sub chain placement first (CPF), is proposed to solve the service chain placement problem for large-scale problems (eg, 256 PMs). It is shown that the proposed algorithm reduces the solution time by up to 86% compared with the NLP optimization model, with an accuracy reduction of just 8%. KEYWORDSnetwork function placement, network function virtualization, nonlinear programming, service chains Int J Commun Syst. 2020;33:e4222.wileyonlinelibrary.com/journal/dac

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Joint NFV placement and routing for multicast service on SDN

Cited by 29 publications

References 12 publications

A Virtual Network Customization Framework for Multicast Services in NFV-Enabled Core Networks

A Virtual Network Customization Framework for Multicast Services in NFV-Enabled Core Networks

Robust Online Composition, Routing and NF Placement for NFV-Enabled Services

Cost minimization in placing service chains for virtualized network functions

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