Customizable Mapping of Virtualized Network Services in Multi-datacenter Environments Based on Genetic Metaheuristics

Abstract

One of the major challenges of the Network Functions Virtualization (NFV) paradigm is to properly deploy functions and services across the network. In particular, current solutions for multi-domain service mapping present several restrictions in terms of the choice of optimization models and metrics. This lack of flexibility ultimately leads to sub-optimized mappings that do not meet the (often conflicting) requirements of all the parties involved in the deployment process (e.g., network operators, clients, providers). This work proposes GeSeMa (Genetic Service Mapping), a new intelligent mapping solution based on genetic algorithms. GeSeMa enables flexible configuration of the evaluation setup, which is used to generate candidate mappings. The solution allows the specification of arbitrary optimization metrics, constraints, and different evaluation policies. A genetic algorithm processes mapping requests and iteratively creates/evolves candidate mappings. We evaluate GeSeMa through comprehensive case studies, including a comparison with other classic and state-of-the-art alternatives.