Show simple item record

dc.contributor.authorAlonso, Juncal
dc.contributor.authorOrue-Echevarria, Leire
dc.contributor.authorOsaba, Eneko
dc.contributor.authorLópez Lobo, Jesús
dc.contributor.authorMartinez, Iñigo
dc.contributor.authorDiaz de Arcaya, Josu
dc.contributor.authorEtxaniz, Iñaki
dc.date.accessioned2021-09-22T07:35:34Z
dc.date.available2021-09-22T07:35:34Z
dc.date.issued2021-07-30
dc.identifier.citationAlonso, Juncal, Leire Orue-Echevarria, Eneko Osaba, Jesús López Lobo, Iñigo Martinez, Josu Diaz de Arcaya, and Iñaki Etxaniz. “Optimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuum.” Information 12, no. 8 (July 30, 2021): 308. doi:10.3390/info12080308.en
dc.identifier.urihttp://hdl.handle.net/11556/1204
dc.description.abstractThe current IT market is more and more dominated by the “cloud continuum”. In the “traditional” cloud, computing resources are typically homogeneous in order to facilitate economies of scale. In contrast, in edge computing, computational resources are widely diverse, commonly with scarce capacities and must be managed very efficiently due to battery constraints or other limitations. A combination of resources and services at the edge (edge computing), in the core (cloud computing), and along the data path (fog computing) is needed through a trusted cloud continuum. This requires novel solutions for the creation, optimization, management, and automatic operation of such infrastructure through new approaches such as infrastructure as code (IaC). In this paper, we analyze how artificial intelligence (AI)-based techniques and tools can enhance the operation of complex applications to support the broad and multi-stage heterogeneity of the infrastructural layer in the “computing continuum” through the enhancement of IaC optimization, IaC self-learning, and IaC self-healing. To this extent, the presented work proposes a set of tools, methods, and techniques for applications’ operators to seamlessly select, combine, configure, and adapt computation resources all along the data path and support the complete service lifecycle covering: (1) optimized distributed application deployment over heterogeneous computing resources; (2) monitoring of execution platforms in real time including continuous control and trust of the infrastructural services; (3) application deployment and adaptation while optimizing the execution; and (4) application self-recovery to avoid compromising situations that may lead to an unexpected failure.en
dc.description.sponsorshipThis research was funded by the European project PIACERE (Horizon 2020 research and innovation Program, under grant agreement no 101000162).en
dc.language.isoengen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleOptimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuumen
dc.typearticleen
dc.identifier.doi10.3390/info12080308en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/101000162/EU/Programming trustworthy Infrastructure As Code in a sEcuRE framework/PIACEREen
dc.rights.accessRightsopenAccessen
dc.subject.keywordsOptimizationen
dc.subject.keywordsSelf-learningen
dc.subject.keywordsConcept driften
dc.subject.keywordsAnomaly detectionen
dc.subject.keywordsCloud continuumen
dc.subject.keywordsSelf-healingen
dc.identifier.essn2078-2489en
dc.issue.number8en
dc.journal.titleInformationen
dc.page.initial308en
dc.volume.number12en


Files in this item

Thumbnail

    Show simple item record

    Attribution 4.0 InternationalExcept where otherwise noted, this item's license is described as Attribution 4.0 International