In this section
@article{Rankothge:2017:10.1109/TNSM.2017.2686979,
author = {Rankothge, W and Le, F and Russo, A and Lobo, J},
doi = {10.1109/TNSM.2017.2686979},
journal = {IEEE Transactions on Network and Service Management},
pages = {343--356},
title = {Optimizing resource allocation for virtualized network functions in a cloud center using genetic algorithms},
url = {http://dx.doi.org/10.1109/TNSM.2017.2686979},
volume = {14},
year = {2017}
}
TY - JOUR
AB - With the introduction of network function virtualization technology, migrating entire enterprise data centers into the cloud has become a possibility. However, for a cloud service provider (CSP) to offer such services, several research problems still need to be addressed. In previous work, we have introduced a platform, called network function center (NFC), to study research issues related to virtualized network functions (VNFs). In an NFC, we assume VNFs to be implemented on virtual machines that can be deployed in any server in the CSP network. We have proposed a resource allocation algorithm for VNFs based on genetic algorithms (GAs). In this paper, we present a comprehensive analysis of two resource allocation algorithms based on GA for: 1) the initial placement of VNFs and 2) the scaling of VNFs to support traffic changes. We compare the performance of the proposed algorithms with a traditional integer linear programming resource allocation technique. We then combine data from previous empirical analyses to generate realistic VNF chains and traffic patterns, and evaluate the resource allocation decision making algorithms. We assume different architectures for the data center, implement different fitness functions with GA, and compare their performance when scaling over the time.
AU - Rankothge,W
AU - Le,F
AU - Russo,A
AU - Lobo,J
DO - 10.1109/TNSM.2017.2686979
EP - 356
PY - 2017///
SN - 1932-4537
SP - 343
TI - Optimizing resource allocation for virtualized network functions in a cloud center using genetic algorithms
T2 - IEEE Transactions on Network and Service Management
UR - http://dx.doi.org/10.1109/TNSM.2017.2686979
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000403434900008&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - http://hdl.handle.net/10044/1/49532
VL - 14
ER -
Artificial Intelligence Network
South Kensington Campus
Imperial College London
SW7 2AZ
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