SDN-Based Load Balancing Service for Cloud Servers

Abdelltif, Ahmed Abdelaziz and Ahmed, Ejaz and Ang, Tan Fong and Gani, Abdullah and Imran, Muhammad (2018) SDN-Based Load Balancing Service for Cloud Servers. IEEE Communications Magazine, 56 (8). pp. 106-111. ISSN 0163-6804, DOI

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With the continuous growth, heterogeneity, and ever increasing demand of services, load balancing of cloud servers is an emerging challenge to meet highly demanding requirements (e.g., data rates, latency, quality of service) of 5G network applications. Although various load balancing techniques have been proposed, some of these techniques either require installation of dedicated additional load balancers for each service, or manual reconfiguration of the device to handle new services is desired. These techniques are expensive, time-consuming, and impractical. Moreover, most of the existing load balancing schemes do not consider service types. This article presents an SDN-based load balancing (SBLB) service for cloud servers to maximize resource utilization and minimize response time of users. The constituents of the proposed scheme are an application module that runs on top of an SDN controller and server pools that connect to the controller through OpenFlow switches. The application module consists of a service classification module, a dynamic load balancing module, and a monitoring module. The controller handles all messages, manages host pools, and maintains the load of host in real time. Experimental results validate the performance of the proposed scheme. Through experimental results, SBLB demonstrates significant decrease in average response time and reply time.

Item Type: Article
Funders: Deanship of Scientific Research, King Saud University through Research Group No. RG-1435-051
Uncontrolled Keywords: Additional loads; Application module; Cloud servers; Load balancing technique; Load-balancing schemes; Openflow switches; Resource utilizations; Sdn controllers
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 14 Oct 2019 09:00
Last Modified: 17 Jul 2020 01:21

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