A Machine Learning Approach for Load Balancing in a Multi-cloud Environment

Abstract

A multi-cloud environment makes use of two or more cloud computing services from different cloud vendors. A typical multi-cloud environment can consist of either only private clouds or only public clouds or a combination of both. Load balancing mechanism is essential in such a computing environment to distribute user requests or network load efficiently across multiple servers or virtual machines, ensuring high availability and reliability. Scalability is also achieved by sending requests only to those servers that are healthy and available to take up the computing workload and thus providing the flexibility to scale up and scale down to satisfy QoS requirements as well, in order to save costs. In our proposed model, a time series-based approach as well as predictive load balancing has been experimented and the results are presented.