Performance Prediction of Cloud-Based Big Data Applications
| Autor: | Jussara M. Almeida, Enrico Barbierato, Eugenio Gianniti, Túlio B. M. Pinto, Danilo Ardagna, Anna Guimarães, Athanasia Evangelinou, Ana Paula Couto da Silva, Marco Gribaudo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Big Data
020203 distributed computing Queueing theory Fluid models Big Data Cloud Computing Performance evaluation business.industry Computer science Distributed computing Big data Settore ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI 020206 networking & telecommunications Cloud computing 02 engineering and technology Cloud Computing Fluid models performance Software Elasticity (cloud computing) Approximation error 0202 electrical engineering electronic engineering information engineering Performance prediction Performance evaluation business Natural approach performance |
| Zdroj: | ICPE |
| Popis: | Data heterogeneity and irregularity are key characteristics of big data applications that often overwhelm the existing software and hardware infrastructures. In such context, the exibility and elasticity provided by the cloud computing paradigm over a natural approach to cost-effectively adapting the allocated resources to the application's current needs. Yet, the same characteristics impose extra challenges to predicting the performance of cloud-based big data applications, a central step in proper management and planning. This paper explores two modeling approaches for performance prediction of cloud-based big data applications. We evaluate a queuing-based analytical model and a novel fast ad-hoc simulator in various scenarios based on different applications and infrastructure setups. Our results show that our approaches can predict average application execution times with 26% relative error in the very worst case and about 12% on average. Moreover, our simulator provides performance estimates 70 times faster than state of the art simulation tools. |
| Databáze: | OpenAIRE |
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