Optimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuum

Alonso, JuncalOrue-Echevarria, LeireOsaba, EnekoLópez Lobo, JesúsMartinez, IñigoDiaz de Arcaya, JosuEtxaniz, Iñaki2021-07-30Alonso , J , Orue-Echevarria , L , Osaba , E , López Lobo , J , Martinez , I , Diaz de Arcaya , J & Etxaniz , I 2021 , ' Optimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuum ' , Information , vol. 12 , no. 8 , 308 , pp. 308 . https://doi.org/10.3390/info120803082078-2489researchoutputwizard: 11556/1204Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.The 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.1767937enginfo:eu-repo/semantics/openAccessOptimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuumjournal article10.3390/info12080308OptimizationSelf-learningConcept driftAnomaly detectionCloud continuumSelf-healingOptimizationSelf-learningConcept driftAnomaly detectionCloud continuumSelf-healingSelf-healing optimizationInformation SystemsProject IDinfo:eu-repo/grantAgreement/EC/H2020/101000162/EU/Programming trustworthy Infrastructure As Code in a sEcuRE framework/PIACEREinfo:eu-repo/grantAgreement/EC/H2020/101000162/EU/Programming trustworthy Infrastructure As Code in a sEcuRE framework/PIACEREFunding InfoThis research was funded by the European project PIACERE (Horizon 2020 research and innovation Program, under grant agreement no 101000162).This research was funded by the European project PIACERE (Horizon 2020 research and innovation Program, under grant agreement no 101000162).http://www.scopus.com/inward/record.url?scp=85112609138&partnerID=8YFLogxK