Optimizing Quality-Aware Big Data Applications in the Cloud

Autor: Danilo Ardagna, Michele Ciavotta, Eugenio Gianniti
Přispěvatelé: Gianniti, E, Ciavotta, M, Ardagna, D
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Big Data
Optimization
Computer Networks and Communications
Computer science
Test data generation
Process (engineering)
media_common.quotation_subject
Cloud computing
Big Data
Tools
Optimization
Unified modeling language
Big data
Cloud computing
02 engineering and technology
ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI
Data modeling
Tools
performance of system
Nonlinear programming
020204 information systems
0202 electrical engineering
electronic engineering
information engineering
Quality (business)
media_common
business.industry
Quality of service
INF/01 - INFORMATICA
020207 software engineering
distributed system
Data science
Computer Science Applications
Hardware and Architecture
Software deployment
MAT/09 - RICERCA OPERATIVA
business
Software
Unified modeling language
Information Systems
Popis: The last years witnessed a steep rise in data generation worldwide and, consequently, the widespread adoption of software solutions able to support data-intensive application. Competitiveness and innovation have strongly benefited from these new platforms and methodologies, and there is a great deal of interest around the new possibilities that Big Data analytics promise to make reality. Many companies currently engage in data-intensive processes as part of their core businesses; however, fully embracing the data-driven paradigm is still cumbersome, and establishing a production-ready, fine-tuned deployment is time-consuming, expensive, and resource-intensive. This situation calls for innovative models and techniques to streamline the process of deployment configuration for Big Data applications. In particular, the focus in this paper is on the rightsizing of Cloud deployed clusters, which represent a cost-effective alternative to installation on premises. This paper proposes a novel tool, integrated in a wider DevOps-inspired approach, implementing a parallel and distributed simulation-optimization technique that efficiently and effectively explores the space of alternative Cloud configurations, seeking the minimum cost deployment that satisfies quality of service constraints. The soundness of the proposed solution has been thoroughly validated in a vast experimental campaign encompassing different applications and Big Data platforms.
Databáze: OpenAIRE
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