The introduction of Network Function Virtualization (NFV) led to a new business model in which the Telecommunication Service Provider needs to rent cloud resources to Infrastructure Provider (InP) at prices as low as possible. Lowest prices can be achieved if the cloud resources, that is long-term Virtual Machines (VM), can be rented in advance. This is in contrast with the short-term VMs that are rented on demand and have higher costs. For this reason we propose a proactive solution in which the cloud resource rent is planned in advance based on a peak traffic knowledge. We illustrate the problem of determining the cloud resources in Cloud Infrastructures managed by different InPs and so as to minimize the cloud resource, bandwidth and deployment costs. In particular to take into account the deployment costs we propose and investigate an heuristic based on the application of the Viterbi algorithm. We show how the proposed proactive approach may allow for a cost reduction in the order of 35% with respect to a reactive approach in which the resources are short-tem rented according to the current traffic demand.

Impact of the deployment costs on the cloud and bandwidth resource problems in multi-providers NFV environment

Lavacca, F. G.
2018-01-01

Abstract

The introduction of Network Function Virtualization (NFV) led to a new business model in which the Telecommunication Service Provider needs to rent cloud resources to Infrastructure Provider (InP) at prices as low as possible. Lowest prices can be achieved if the cloud resources, that is long-term Virtual Machines (VM), can be rented in advance. This is in contrast with the short-term VMs that are rented on demand and have higher costs. For this reason we propose a proactive solution in which the cloud resource rent is planned in advance based on a peak traffic knowledge. We illustrate the problem of determining the cloud resources in Cloud Infrastructures managed by different InPs and so as to minimize the cloud resource, bandwidth and deployment costs. In particular to take into account the deployment costs we propose and investigate an heuristic based on the application of the Viterbi algorithm. We show how the proposed proactive approach may allow for a cost reduction in the order of 35% with respect to a reactive approach in which the resources are short-tem rented according to the current traffic demand.
2018
9788887237405
network function virtualization
short-term virtual machine
viterbi algorithm
control and optimization
computer networks and communications
energy engineering and power technology
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14085/11394
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