Joint Reliability-aware and Cost Efficient Path Allocation and VNF Placement using Sharing Scheme
Network Function Virtualization (NFV) is a vital player of modern networks providing different types of services such as traffic optimization, content filtering, and load balancing. More precisely, NFV is a provisioning technology aims at reducing the large Capital Expenditure (CAPEX) of network pro...
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Zusammenfassung: | Network Function Virtualization (NFV) is a vital player of modern networks
providing different types of services such as traffic optimization, content
filtering, and load balancing. More precisely, NFV is a provisioning technology
aims at reducing the large Capital Expenditure (CAPEX) of network providers by
moving services from dedicated hardware to commodity servers using Virtualized
Network Functions (VNF). A sequence of VNFs/services following a logical goal
is referred to as a Service Function Chain (SFC). The movement toward SFC
introduces new challenges to those network services which require high
reliability. To address this challenge, redundancy schemes are introduced.
Existing redundancy schemes using dedicated protection enhance the reliability
of services, however, they do not consider the cost of redundant VNFs. In this
paper, we propose a novel reliability enhancement method using a shared
protection scheme to reduce the cost of redundant VNFs. To this end, We
mathematically formulate the problem as a Mixed Integer Linear Programming
(MILP). The objective is to determine optimal reliability that could be
achieved with minimum cost. Although the corresponding optimization problem can
be solved using existing MILP solvers, the computational complexity is not
rational for realistic scenarios. Thereafter, we propose a Reliability-aware
and minimum-Cost based Genetic (RCG) algorithm to solve this problem with low
computational complexity. In order to evaluate the proposed solution, We have
compared it with four different solutions. Simulation results show that RCG
achieves near-optimal performance at a much lower complexity compared with the
optimal solution. |
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DOI: | 10.48550/arxiv.1912.06742 |