SD: a Divergence-based Estimation Method for Service Demands in Cloud Systems

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Title: SD: a Divergence-based Estimation Method for Service Demands in Cloud Systems
Authors: Dipietro, S
Casale, G
Item Type: Conference Paper
Abstract: Estimating performance models parameters of cloudsystems presents several challenges due to the distributed natureof the applications, the chains of interactions of requests witharchitectural nodes, and the parallelism and coordination mech-anisms implemented within these systems.In this work, we present a new inference algorithm for modelparameters, calledstate divergence(SD) algorithm, to accuratelyestimate resource demands in a complex cloud application.Differently from existing approaches, SD attempts to minimizethe divergence between observed and modeled marginal stateprobabilities for individual nodes within an application, thereforerequiring the availability of probabilistic measures from both thesystem and the underpinning model.Validation against a case study using the Apache CassandraNoSQL database and random experiments show that SD can ac-curately predict demands and improve system behavior modelingand prediction.
Issue Date: 26-Aug-2019
Date of Acceptance: 26-May-2019
Publisher: IEEE
Journal / Book Title: Proceedings of FiCloud 2019
Copyright Statement: This paper is embargoed until publication.
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: 825040
Conference Name: IEEE FiCloud 2019
Publication Status: Accepted
Start Date: 2019-08-26
Finish Date: 2019-08-28
Conference Place: Istanbul, Turkey
Embargo Date: publication subject to indefinite embargo
Appears in Collections:Computing

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