# QoSComm: A Data Flow Allocation Strategy among SDN-Based Data Centers for IoT Big Data Analytics
## 2 Related Work
BWARE (Bandwidth-Aware) [18] authors improve the backup task scheduling policy for Hadoop clusters running in an SDN network. It uses SDN bandwidth parameter to get real network bandwidth from the input data source node and the execution destination node. This network parameter is used for data transfer in the backup tasks process from the source node to the implementation node. Results showed that BWARE can improve the time elapsed for the backup tasks process when considering the SDN bandwidth parameter. As a summary of the reviewed proposals, GSBT [13] gets available routes in the SDN and calculates the shortest path for use in the reduction tree for MPI communications process without considering traffic congestion, delay, and packet loss that can exist in some routes. BASS [15], ASETS [16], CLS [17], and BWARE [18] consider the network bandwidth parameter in their application job scheduling process. In BLAR [14], the bandwidth and latency parameters were considered in their MPI job scheduling process for path selection to compute nodes, and achieved an improvement in application performance. The results of some of the research works showed that there were cases where the performance was affected [13; 14], and this could be due to several network conditions that were not considered in data flow allocation process, such as available bandwidth, point to point delay, packet loss, and jitter. Therefore, the opportunity in these research works would be to consider these network parameters to provide QoS in the flow allocation process to improve application performance. Regarding QoS in SDN, we identified proposals considering three main aspects: architecture, path selection algorithm, and if the proposal works within a single domain or among several domains. Examples of these proposals are OpenQoS [19] and VSDN [20], where, in addition to consulting the distance of the nodes, in some cases, they also consider the delay among them. The focus of the most QoS proposals is forwarding data flows to control bandwidth allocation and improve multimedia transmissions (video streaming) and even VoIP [21; 22; 23], but none of them consider other applications such as Big Data, so it is not clear under which circumstances these proposals can benefit BDA performance. In CECT, Ref. [24] proposed a scheme to minimize network congestion and reallocate network resources according to applications' requirements. Their algorithm was designed for a software-defined cloud data center, where the dynamic data flow reallocation of some services such as virtual machine motion needs special QoS guarantees that can generate overhead in the reallocation process. CECT uses a genetic algorithm to calculate the routes, also considering only available bandwidth as a constraint, in addition to using a single controller within the software-defined data center. In AmoebaNet [9], authors proposed a service to apply SDN providing QoS-guaranteed network services in LAN (Local Area Network) or campus networks for Big Data science, using WAN (Wide Area Network) path reservation systems such as ESNet (Energy Science Network) OSCARS (On-Demand Secure Circuits and Advance Reservation System) and I2 (Internet 2) specialized networks. In addition, it uses a Dijkstra shortest path variant algorithm to compute an end-to-end network path, using only a bandwidth parameter as a constraint and considering a single SDN controller for communication between the different domains. Proposals analyzed describe experiments considering QoS within a single domain and one controller for the SDN, except in AmoebaNet [9], but it is only used in specialized networks. None of the works considered a QoS proposal under an architecture where BDA jobs are running between two or more distributed data centers, or considering the four most essential networks parameters to provide QoS, as QoSComm does. Considering the research works described above, our proposal QoSComm differs from them, mainly in its architecture.