Distributed Service Function Chaining

A service-function chain, or simply a chain, is an ordered sequence of service functions, e.g., firewalls and load balancers, composing a service. A chain deployment involves selecting and instantiating a number of virtual network functions (VNFs), i.e., softwarized service functions, placing VNF in...

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Veröffentlicht in:IEEE journal on selected areas in communications 2017-11, Vol.35 (11), p.2479-2489
Hauptverfasser: Ghaznavi, Milad, Shahriar, Nashid, Kamali, Shahin, Ahmed, Reaz, Boutaba, Raouf
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container_end_page 2489
container_issue 11
container_start_page 2479
container_title IEEE journal on selected areas in communications
container_volume 35
creator Ghaznavi, Milad
Shahriar, Nashid
Kamali, Shahin
Ahmed, Reaz
Boutaba, Raouf
description A service-function chain, or simply a chain, is an ordered sequence of service functions, e.g., firewalls and load balancers, composing a service. A chain deployment involves selecting and instantiating a number of virtual network functions (VNFs), i.e., softwarized service functions, placing VNF instances, and routing traffic through them. In the current optimization-models of a chain deployment, the instances of the same function are assumed to be identical, while typical service providers offer VNFs with heterogeneous throughput and resource configurations. The VNF instances of the same function are installed in a single physical machine, which limits a chain to the throughput of a few instances that can be installed in one physical machine. Furthermore, the selection, placement, and routing problems are solved in isolation. We present distributed service function chaining that coordinates these operations, places VNF-instances of the same function distributedly, and selects appropriate instances from typical VNF offerings. Such a deployment uses network resources more efficiently and decouples a chain's throughput from that of physical machines. We formulate this deployment as a mixed integer programming (MIP) model, prove its NP-Hardness, and develop a local search heuristic called Kariz. Extensive experiments demonstrate that Kariz achieves a competitive acceptance-ratio of 76%-100% with an extra cost of less than 24% compared with the MIP model.
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ispartof IEEE journal on selected areas in communications, 2017-11, Vol.35 (11), p.2479-2489
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subjects Bandwidth
Chaining
Chains
Firewalls
Integer programming
Linear programming
Load modeling
Mixed integer
Network function virtualization
Optimization
placement
routing
Routing (machining)
Service function chaining
Throughput
Traffic models
virtual network function
Virtualization
title Distributed Service Function Chaining
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