Using Global Behavior Modeling to Improve QoS in Cloud Data Storage Services

Abstract--The cloud computing model aims to make largescale data-intensive computing affordable even for users with limited financial resources, that cannot invest into expensive infrastructures necesssary to run them. In this context, MapReduce is emerging as a highly scalable programming paradigm that enables high-throughput data-intensive processing as a cloud service. Its performance is highly dependent on the underlying storage service, responsible to efficiently support massively parallel data accesses by guaranteeing a high throughput under heavy access concurrency. In this context, quality of service plays a crucial role: the storage service needs to sustain a stable throughput for each individual accesss, in addition to achieving a high aggregated throughput under concurrency. In this paper we propose a technique to address this problem using component monitoring, application-side feedback and behavior pattern analysis to automatically infer useful knowledge about the causes o...

• Type: Journal
• Year: 2010
• Where: CLOUDCOM
• Authors: Jesús Montes, Bogdan Nicolae, Gabriel Antoniu, Alberto Sánchez, María S. Pérez
• Keywords: Cloud Computing Model, CLOUDCOM 2010, Distributed And Parallel Computing, Scalable Programming Paradigm, Storage Service