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%.
Highlights
In recent years, the Internet of Things (IoT) has evolved as one of the leading technologies which generate a massive amount of data stored in distributed data sources
SETSA (SDN-Empowered Task Scheduler Algorithm) that is based on the software defined network (SDN) capabilities to schedule tasks on the virtual machine that is available to maximize the use of the bandwidth
The SETSA algorithm, which is used by ASETS, uses the bandwidth of the computational cloud to more efficiently increase the performance of the HPC as a Service architecture (HPCaaS) architecture related to the response time of the jobs submitted to the cloud
Summary
The Internet of Things (IoT) has evolved as one of the leading technologies which generate a massive amount of data stored in distributed data sources. IoT devices transfer the generated data to big data systems located in distributed data centers for further analysis. Organizations and users can perform all kinds of processing and analysis on the basis of massive IoT data, adding to their value [1]. Big data analytics (BDA) refers to the strategy of analyzing large volumes of data, or big data. These big data are gathered from a wide variety of sources, including social networks, videos, digital images, sales transaction records, end-user activities, environmental monitoring, sensors (IoT devices), among others. With the use of BDA, a variety of these IoT data are examined to reveal trends, unseen
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
