Heuristic for Lowering Electricity Costs for Routing in Optical Data Center Networks

Optical data centers consume a large quantity of energy and the cost of that energy has a significant contribution to the operational cost in data centers. The amount of electricity consumption in data centers and their related costs are increasing day by day. Data centers are geographically distributed all around the continents and the growing numbers of data replicas have made it possible to find more cost effective network routing. Besides flat-rate prices, today, there are companies which offers real-time pricing. In order to address the energy consumption cost problem, we propose an energy efficient routing scheme to find least cost path to the replicas based on real-time pricing model called energy price aware routing (EPAR). Our research considers anycast data transmission model to find the suitable replica as well as the fixed window traffic allocation model for demand request to reduce the energy consumption cost of data center networks

Resource Allocation for Periodic Traffic Demands in WDM Networks

Recent research has clearly established that holding-time-aware routing and wavelength assignment (RWA) schemes lead to significant improvements in resource utilization for scheduled traffic. By exploiting the knowledge of the demand holding times, this thesis proposes new traffic grooming techniques to achieve more efficient resource utilization with the goal of minimizing resources such as bandwidth, wavelength channels, transceivers, and energy consumption. This thesis also introduces a new model, the segmented sliding window model, where a demand may be decomposed into two or more components and each component can be sent separately. This technique is suitable for applications where continuous data transmission is not strictly required such as large file transfers for grid computing. Integer linear program (ILP) formulations and an efficient heuristic are put forward for resource allocation under the proposed segmented sliding window model. It is shown that the proposed model can lead to significantly higher throughput, even over existing holding-time-aware models