TeraScaler ELB-an Algorithm of Prediction-Based Elastic Load Balancing Resource Management in Cloud Computing

• Main Authors: He-Sheng Wu, Chong-Jun Wang, Jun-Yuan Xie
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithm design and analysis; Cloud computing; elastic load balancing; load balancing; Load management; Prediction algorithms; Resource management; server cluster; Servers; virtual machine; Virtual machining
• DOI: 10.1109/WAINA.2013.79

Load balancing is the core of virtual resource management and scheduling in cloud computing. For network applications, the cost of user would be greatly saved if load balancer could dynamically adjust cluster resources in accordance with the current applied load. The current load balancing products of cloud, such as Amazon's ELB, can be used to manage virtual machines in the cloud. The main drawbacks are still only supporting template-based deployment of new virtual machines, not supporting the trend prediction, failing to gain resources dynamically, and not sufficiently providing the elastic management of resources. Since the virtual machine for load balancing management in cloud computing can be dynamically applied and released, an algorithm of prediction-based elastic load balancing resource management (TeraScaler ELB) is presented to overcome the drawbacks. Experiments have shown that the required number of virtual machines change in compliance with the change of network load, thus TeraScaler ELB is able to dynamically adjust the processing capacity of back-end server cluster with the applied load. Besides it could make full use of the 'use on demand' feature of cloud computing, TeraScaler ELB leads to a better application of prediction based load balancing in cloud computing. It concludes that compared with the traditional elastic resource management algorithm, TeraScaler ELB is more reasonable for providing scalability and high availability.