Traffic-Aware Service Relocation in Cloud-Oriented Elastic Optical Networks

• URL: http://arxiv.org/abs/2105.07653v1
• Date: Mon, 17 May 2021 08:00:55 GMT
• Title: Traffic-Aware Service Relocation in Cloud-Oriented Elastic Optical Networks
• Authors: R\'o\.za Go\'scie\'n
• Abstract summary: We study problem of efficient service relocation in elastic optical networks (EONs) in order to increase network performance. We first propose novel traffic model for cloud ready transport networks. We introduce 21 different relocation policies, which use three types of data for decision making.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we study problem of efficient service relocation (i.e., changing assigned data center for a selected client node) in elastic optical networks (EONs) in order to increase network performance (measured by the volume of accepted traffic). To this end, we first propose novel traffic model for cloud ready transport networks. The model takes into account four flow types (i.e., city-to-city, city-to-data center, data center-to-data center and data center-to-data center) while the flow characteristics are based on real economical and geographical parameters of the cities related to network nodes. Then, we propose dedicated flow allocation algorithm that can be supported by the service relocation process. We also introduce 21 different relocation policies, which use three types of data for decision making - network topological characteristics, rejection history and traffic prediction. Eventually, we perform extensive numerical experiments in order to: (i) tune proposed optimization approaches and (ii) evaluate and compare their efficiency and select the best one. The results of the investigation prove high efficiency of the proposed policies. The propoerly designed relocation policy allowed to allocate up to 3% more traffic (compared to the allocation without that policy). The results also reveal that the most efficient relocation policy bases its decisions on two types of data simultaneously - the rejection history and traffic prediction.

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