Using machine learning for intelligent shard sizing on the cloud

Abstract

Sharding implementations use conservative approximations for determining the number of cloud instances required and the size of the shards to be stored on each of them. Conservative approximations are often inaccurate and result in overloaded deployments, which need reactive refinement. Reactive refinement results in demand for additional resources from an already overloaded system and is counterproductive.

This paper proposes an algorithm that eliminates the need for conservative approximations and reduces the need for reactive refinement. A multiple linear regression based machine learning algorithm is used to predict the latency of requests for a given application deployed on a cloud machine. The predicted latency helps to decide accurately and with certainty if the capacity of the cloud machine will satisfy the service level agreement for effective operation of the application. Application of the proposed methods on a popular database schema on the cloud resulted in highly accurate predictions. The results of the deployment and the tests performed to establish the accuracy have been presented in detail and are shown to establish the authenticity of the claims.