Service Degradability Supported by Forecasting System in Optical Data Center Networks

Abstract

A large-scale distributed scientific computing develops rapidly, making the interconnection of data centers (DCs) become the focus in both academic and industrial communities. Since the fiber transmission has the characteristics of high capacity, low power consumption, and strong reliability, establishing the optical data center network (ODCN)—where DCs are interconnected via optical backbone—is an inevitable trend. In ODCNs, the oversize inter-DC traffic demand suffers from the denial-of-service caused by network overload. Therefore, we propose a provident service degradation (service degradability) framework based on the traffic-demand forecasting system. This forecasting system helps us to precisely know when service degradability becomes necessary, together with the exact location of overload. As a result, service degradability can be adopted at appropriate times and locations. Specifically, we perform bandwidth degradability to overcome the overload of optical backbone, while utilizing computing-resource degradability if there is a DC overload. We simulate and analyze the performance of our prediction system and service degradability algorithm. The simulation results show that the system's prediction result well matches actual traffic demand, and the average absolute relative error is merely 0.0507; and compared with benchmarks, our algorithm has the better performance on maximizing service provider profit via mitigating network congestion.