Model-driven development of data intensive applications over cloud resources

Tolosana-Calasanz, Rafael (Universidad de Zaragoza) ; Bañares, José Ángel (Universidad de Zaragoza) ; Colom, José Manuel (Universidad de Zaragoza)

Resumen: The proliferation of sensors over the last years has generated large amounts of raw data, forming data streams that need to be processed. In many cases, cloud resources are used for such processing, exploiting their flexibility, but these sensor streaming applications often need to support operational and control actions that have real-time and low-latency requirements that go beyond the cost effective and flexible solutions supported by existing cloud frameworks, such as Apache Kafka, Apache Spark Streaming, or Map-Reduce Streams. In this paper, we describe a model-driven and stepwise refinement methodological approach for streaming applications executed over clouds. The central role is assigned to a set of Petri Net models for specifying functional and non-functional requirements. They support model reuse, and a way to combine formal analysis, simulation, and approximate computation of minimal and maximal boundaries of non-functional requirements when the problem is either mathematically or computationally intractable. We show how our proposal can assist developers in their design and implementation decisions from a performance perspective. Our methodology allows to conduct performance analysis: The methodology is intended for all the engineering process stages, and we can (i) analyse how it can be mapped onto cloud resources, and (ii) obtain key performance indicators, including throughput or economic cost, so that developers are assisted in their development tasks and in their decision taking. In order to illustrate our approach, we make use of the pipelined wavefront array.

Idioma: Inglés

DOI: 10.1016/j.future.2017.12.046

Año: 2018

Publicado en: Future Generation Computer Systems-The International Journal of Grid Computing Theory Methods and Applications 87 (2018), 888 - 909
ISSN: 0167-739X

Factor impacto JCR: 5.768 (2018)

Categ. JCR: COMPUTER SCIENCE, THEORY & METHODS rank: 8 / 104 = 0.077 (2018) - Q1 - T1

Factor impacto SCIMAGO: 0.835 - Computer Networks and Communications (Q1) - Software (Q1) - Hardware and Architecture (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/T93
Financiación: info:eu-repo/grantAgreement/ES/MINECO/TIN2013-40809-R

Tipo y forma: Artículo (PrePrint)

Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)