Rankothge W, Le F, Russo A, Lobo J. Optimizing Resources Allocation for Virtualized Network Functions in a Cloud Center using Genetic Algorithms. IEEE Transactions on Network and Service Management ( Volume: PP, Issue: 99 )
List of results published directly linked with the projects co-funded by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Program (MDM-2015-0502).
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Rankothge W, Le F, Russo A, Lobo J. Optimizing Resources Allocation for Virtualized Network Functions in a Cloud Center using Genetic Algorithms. IEEE Transactions on Network and Service Management ( Volume: PP, Issue: 99 )
Rankothge W, Le F, Russo A, Lobo J. Optimizing Resources Allocation for Virtualized Network Functions in a Cloud Center using Genetic Algorithms. IEEE Transactions on Network and Service Management ( Volume: PP, Issue: 99 )
With the introduction of Network Function Virtualization (NFV) 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 a 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.
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