Adaptive large neighborhood search heuristics for multi-tier service deployment problems in clouds
Abstract
This paper proposes adaptive large neighborhood search (ALNS) heuristics for two service deployment problems in
a cloud computing context. The problems under study consider the deployment problem of a provider of softwareas-
a-service applications, and include decisions related to the replication and placement of the provided services.
A novel feature of the proposed algorithms is a local search layer on top of the destroy and repair operators. In
addition, we use a mixed integer programming-based repair operator in conjunction with other faster heuristic
operators. Because of the di erent time consumption of the repair operators, we need to account for the time usage
in the scoring mechanism of the adaptive operator selection. The computational study investigates the benefits of
implementing a local search operator on top of the standard ALNS framework. Moreover, we also compare the
proposed algorithms with a branch and price (B&P) approach previously developed for the same problems. The
results of our experiments show that the benefits of the local search operators increase with the problem size. We
also observe that the ALNS with the local search operators outperforms the B&P on larger problems, but it is also
comparable with the B&P on smaller problems with a short run time.