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Title: Resource Usage Prediction in Distributed Key-Value Datastores
Authors: Francisco Miguel Cruz
Francisco Almeida Maia
Miguel Marques Matos
Rui Carlos Oliveira
João Tiago Paulo
José Orlando Pereira
Ricardo Pereira Vilaça
Issue Date: 2016
Abstract: In order to attain the promises of the Cloud Computing paradigm, systems need to be able to transparently adapt to environment changes. Such behavior benefits from the ability to predict those changes in order to handle them seamlessly. In this paper, we present a mechanism to accurately predict the resource usage of distributed key-value datastores. Our mechanism requires offline training but, in contrast with other approaches, it is sufficient to run it only once per hardware configuration and subsequently use it for online prediction of database performance under any circumstance. The mechanism accurately estimates the database resource usage for any request distribution with an average accuracy of 94 %, only by knowing two parameters: (i) cache hit ratio; and (ii) incoming throughput. Both input values can be observed in real time or synthesized for request allocation decisions. This novel approach is sufficiently simple and generic, while simultaneously being suitable for other practical applications.
metadata.dc.type: conferenceObject
Appears in Collections:HASLab - Indexed Articles in Conferences

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