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Elastic optical networks (EONs) offer tremendous benefits to deal with the exponential increase of the data center traffic. The granularity offered in spectrum allocation supports efficient management of available bandwidth and accommodates multiple traffic to be routed through common links. However, this brings the inherent challenges of routing and spectrum allocation (RSA) constraints. This becomes more complex for elastic optical data center networks (EODCNs), wherein multiple requests arrive at the same time, requiring identical or different bandwidths and each request may have the same or different destination and paths. Also, data requested by different users could be of varying importance levels. Under such a scenario, maintaining the quality of service (QoS) by minimizing the probability of traffic failure and bandwidth blocking is a major task for service providers. To address these problems, we propose an enhanced methodology using path prediction and link-state analysis for efficient allocation of frequency slots and reuse of bandwidth for data centers connected through EONs. Our proposed strategy intents to minimize the number of blocked requests due to non-availability of resources and reduce the failure probability. We introduce here the concept of connectivity degree and Kuhn-Munkres multi-objective optimization for spectrum allocation. We also evaluate the call request blocking probability varying the number of data centers and traffic load. The obtained results show that the proposed algorithm is highly effective in reducing the traffic failure and blocking probability for EODCNs.

A novel strategy to enhance the quality of service (QoS) for data center traffic in elastic optical networks