000064430 001__ 64430
000064430 005__ 20200609132534.0
000064430 0247_ $$2doi$$a10.1016/j.future.2017.12.046
000064430 0248_ $$2sideral$$a103889
000064430 037__ $$aART-2018-103889
000064430 041__ $$aeng
000064430 100__ $$0(orcid)0000-0003-3057-6273$$aTolosana-Calasanz, Rafael$$uUniversidad de Zaragoza
000064430 245__ $$aModel-driven development of data intensive applications over cloud resources
000064430 260__ $$c2018
000064430 5060_ $$aAccess copy available to the general public$$fUnrestricted
000064430 5203_ $$aThe 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.
000064430 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T93$$9info:eu-repo/grantAgreement/ES/MINECO/TIN2013-40809-R
000064430 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000064430 590__ $$a5.768$$b2018
000064430 591__ $$aCOMPUTER SCIENCE, THEORY & METHODS$$b8 / 104 = 0.077$$c2018$$dQ1$$eT1
000064430 592__ $$a0.835$$b2018
000064430 593__ $$aComputer Networks and Communications$$c2018$$dQ1
000064430 593__ $$aSoftware$$c2018$$dQ1
000064430 593__ $$aHardware and Architecture$$c2018$$dQ1
000064430 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/submittedVersion
000064430 700__ $$0(orcid)0000-0002-4198-8241$$aBañares, José Ángel$$uUniversidad de Zaragoza
000064430 700__ $$0(orcid)0000-0001-5066-4030$$aColom, José Manuel$$uUniversidad de Zaragoza
000064430 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000064430 773__ $$g87 (2018), 888 - 909$$pFuture gener. comput. syst.$$tFuture Generation Computer Systems-The International Journal of Grid Computing Theory Methods and Applications$$x0167-739X
000064430 8564_ $$s2027133$$uhttps://zaguan.unizar.es/record/64430/files/texto_completo.pdf$$yPreprint
000064430 8564_ $$s97184$$uhttps://zaguan.unizar.es/record/64430/files/texto_completo.jpg?subformat=icon$$xicon$$yPreprint
000064430 909CO $$ooai:zaguan.unizar.es:64430$$particulos$$pdriver
000064430 951__ $$a2020-06-09-13:22:26
000064430 980__ $$aARTICLE