Construction of Distributed Optoelectronic Switching Network and OpenFlow Anycast Mechanism of Network on Cloud Computing

Abstract

Traditional electrical switching network faces the problems of limited bandwidth and large energy consumption. The optoelectronic hybrid data network fully combines the advantages of optical switching and electrical switching, and dynamically adjusts the topology and bandwidth in accordance to the demand, thereby effectively improving network performance and reducing power consumption. A high-performance distributed optoelectronic switching network architecture called DOIN_W was proposed in the study, aiming to solve the problems of the scalability of the optoelectronic switching network architecture and the insufficient flexibility of network resource allocation. The architecture was connected topologically according to the method of 2DTorus, thereby effectively improving the network scale of the photoelectric switching network and supporting the dynamic adjustment of the network scale. The optical switching of DOIN_W adopted the "broadcastselect" method to support different forms of broadcasting communication. A multi-dimensional optical signal switch was designed, and the optical signal broadcast of the switch can reach any of the optical switches in DOIN_W, thereby supporting the direct connection between optical switches. Considering the problems that the multi-broadcast service of DOIN_W was unable to access freely, stateful optical signal communication was hard to maintain and the single path weight resulted in lack priority of communication, the OpenFlow network protocol of the cloud computing network was introduced in the DOIN_W architecture. The anycast mechanism of DOIN_W was optimized based on this protocol. Based on the stream-level network simulator for network performance simulation analysis, the DOIN_W architecture can meet the requirements of network scalability and flexibility. Compared with the OVS architecture and Jellyfish architecture, the DOIN_W architecture can effectively reduce the average flow completion time by more than 30% and reduce average energy consumption by over 25%.