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|Title: ||Dynamic network function chain composition for mitigating network latency|
|Authors: ||Hajji, Wajdi|
Genez, Thiago A.
Tso, Fung Po
Phillips, Iain W.
|Issue Date: ||2018|
|Publisher: ||© IEEE|
|Citation: ||HAJJI, W. ... et al, 2018. Dynamic network function chain composition for mitigating network latency. Presented at the 2018 IEEE Symposium on Computers and Communications (ISCC), Natal, Brazil, 25-28 June 2018, pp.316-321.|
|Abstract: ||Network Function Virtualisation (NFV) enables rapid deployment of new services in networks on an on-demand basis using general purpose servers. Multiple virtual network functions (VNFs) can be dynamically chained in an ordered sequence for the delivery of end-to-end services. Nevertheless, network latency caused by the sequential order of packet processing on every VNF can hurt the performance of latency-sensitive applications. To reduce such network latency, existing solutions only consider the maximum capacity of individual virtual network functions (VNFs) and do not take into account the fact that performance of VNFs, as with any software applications, is bottlenecked by either CPU or I/O peripheral capacity of the server they run on and their underneath implementation such as singleor multi-threaded.By exploiting this knowledge, we can better determine the number of required VNF instances and distribute the network traffic among them for any given VNF chain. In this paper, we formulate the VNF Scaling and Traffic Distribution problem and prove that it is NP-hard. We then present the design and implementation of Natif, an efficient VNF-Aware VNF insTantIation and traFfic distribution scheme. Through our OpenStack-based testbed evaluations, we demonstrate that Natif can significantly improve the network latency by 188% on average as compared to other approaches. As a chain composition scheme, Natif can effectively work with any VNF chaining algorithms.|
|Description: ||© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|Sponsor: ||This work has been partially supported in part by the UK Engineering and Physical Sciences Research Council (EPSRC) grants EP/P004407/2 and EP/P004024/1; the Chinese National Research Fund (NSFC) No. 61772235
and 61402200; the Fundamental Research Funds for the Central Universities (21617409).|
|Version: ||Accepted for publication|
|Publisher Link: ||https://doi.org/10.1109/ISCC.2018.8538646|
|Appears in Collections:||Conference Papers and Presentations (Computer Science)|
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