000061322 001__ 61322
000061322 005__ 20190709135451.0
000061322 0247_ $$2doi$$a10.1016/j.jss.2017.05.022
000061322 0248_ $$2sideral$$a98661
000061322 037__ $$aART-2017-98661
000061322 041__ $$aeng
000061322 100__ $$aPerez-Palacin, Diego
000061322 245__ $$aAccurate Modeling and Efficient QoS Analysis of Scalable Adaptive Systems under Bursty Workload
000061322 260__ $$c2017
000061322 5060_ $$aAccess copy available to the general public$$fUnrestricted
000061322 5203_ $$aFulfillment of QoS requirements for systems deployed in the Internet is becoming a must. A widespread characteristic of this kind of systems is that they are usually subject to highly variable and bursty workloads. The allocation of resources to fulfill QoS requirements during the peak workloads could entail a waste of computing resources. A solution is to provide the system with self-adaptive techniques that can allocate resources only when and where they are required. We pursue the QoS evaluation of workload-aware self-adaptive systems based on stochastic models. In particular, this work proposes an accurate modeling of the workload variability and burstiness phenomena based on previous approaches that use Markov Modulated Poisson Processes. We extend these approaches in order to accurately model the variations of the workload strongly influence the QoS of the self-adaptive system. Unfortunately, this stochastic modeling may lead to a non tractable QoS analysis. Consequently, this work also develops an efficient procedure for carrying out the QoS analysis.
000061322 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/TIN2014-58457-R$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 644869-DICE$$9info:eu-repo/grantAgreement/EC/H2020/644869/EU/Developing Data-Intensive Cloud Applications with Iterative Quality Enhancements/DICE$$9info:eu-repo/grantAgreement/ES/DGA/T94
000061322 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000061322 590__ $$a2.278$$b2017
000061322 591__ $$aCOMPUTER SCIENCE, THEORY & METHODS$$b21 / 103 = 0.204$$c2017$$dQ1$$eT1
000061322 591__ $$aCOMPUTER SCIENCE, SOFTWARE ENGINEERING$$b19 / 104 = 0.183$$c2017$$dQ1$$eT1
000061322 592__ $$a0.472$$b2017
000061322 593__ $$aHardware and Architecture$$c2017$$dQ1
000061322 593__ $$aSoftware$$c2017$$dQ2
000061322 593__ $$aInformation Systems$$c2017$$dQ2
000061322 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/submittedVersion
000061322 700__ $$aMirandola, Raffaela
000061322 700__ $$0(orcid)0000-0002-8917-6584$$aMerseguer, José$$uUniversidad de Zaragoza
000061322 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000061322 773__ $$g130 (2017), 24-41$$pJ. syst. softw.$$tJOURNAL OF SYSTEMS AND SOFTWARE$$x0164-1212
000061322 8564_ $$s1261966$$uhttps://zaguan.unizar.es/record/61322/files/texto_completo.pdf$$yPreprint
000061322 8564_ $$s69115$$uhttps://zaguan.unizar.es/record/61322/files/texto_completo.jpg?subformat=icon$$xicon$$yPreprint
000061322 909CO $$ooai:zaguan.unizar.es:61322$$particulos$$pdriver
000061322 951__ $$a2019-07-09-11:40:56
000061322 980__ $$aARTICLE