QoSComm: A Data Flow Allocation Strategy among SDN-Based Data Centers for IoT Big Data Analytics

Abstract

When Internet of Things (IoT) big data analytics (BDA) require to transfer data streams among software defined network (SDN)-based distributed data centers, the data flow forwarding in the communication network is typically done by an SDN controller using a traditional shortest path algorithm or just considering bandwidth requirements by the applications. In BDA, this scheme could affect their performance resulting in a longer job completion time because additional metrics were not considered, such as end-to-end delay, jitter, and packet loss rate in the data transfer path. These metrics are quality of service (QoS) parameters in the communication network. This research proposes a solution called QoSComm, an SDN strategy to allocate QoS-based data flows for BDA running across distributed data centers to minimize their job completion time. QoSComm operates in two phases: (i) based on the current communication network conditions, it calculates the feasible paths for each data center using a multi-objective optimization method; (ii) it distributes the resultant paths among data centers configuring their openflow Switches (OFS) dynamically. Simulation results show that QoSComm can improve BDA job completion time by an average of 18%.

Assessment of Innovative Architectures, Challenges and Solutions of Edge Intelligence

“…Multilayer architecture built on SDN that keeps track of Internet of Things (IoT) traffic, uses congestion prevention methods, and disperses analysis of IoT data among a Data Center (DC) and the network's edge has been suggested by Lozano-Rizk et al [16]. The structure of this design is as follows: Connections to DCs at the network's edge is given, and the median bandwidths of IoTs data flows is tracked at the architecture tier of the networks.…”