<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>Pezzè, Mauro</dc:contributor>
  <dc:creator>Xin, Rui</dc:creator>
  <dc:date>2020-01-24</dc:date>
  <dc:description xmlns:ns0="xml" ns0:lang="en">Complex multi-tier systems are composed of many distributed machines, feature multi-layer architecture and offer  different types of services. Shared complex multi-tier systems, such as cloud systems, reduce costs and improves  resource utilization efficiency, with a considerable amount of complexity and dynamics that challenge the reliability  of the system. The new challenges of complex multi-tier systems motivate a new holistic self-healing approach,  which must be accurate, lightweight and proactive, to ensure reliable cloud applications. Self-healing techniques  work at runtime, thus they offer automatic and flexible ways to increase reliability by detecting errors, diagnosing  errors, and either fixing the errors or mitigating their effects. Self-Healing Systems leverage the time between the  activation of a fault and the failure by taking actions to avoid failures. Self-Healing systems shall predict failures,  localize the faults and fix or mask them before the failure occurrence. In my Ph.D, I focused on predicting failures  and localizing faults. In this thesis I present an approach, DyFAULT, that predicts failures by detecting anomalous  systems states early enough to diagnose the causing errors and fix them before the failure occurrence, and  localizes faults by leveraging the collected data to pinpoint the location of error and possibly the type of the fault.  The contribution of my Ph.D work includes: (i) an approach to accurately predict failures and localize faults that  requires training with fault seeding. (ii) an approach to predict failures and localize faults that requires training with  data from normal execution only. (iii) a prototype implementation of the two approaches (iv) a set of experimental  results that evaluate the proposed approaches of DyFAULT.</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>https://susi.usi.ch/global/documents/319374</dc:identifier>
  <dc:identifier>https://n2t.net/ark:/12658/srd1319374</dc:identifier>
  <dc:identifier>https://susi.usi.ch/documents/319374/files/2020INFO017.pdf</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:rero-006-119194</dc:relation>
  <dc:relation>info:eu-repo/semantics/altIdentifier/ark/12658/srd1319374</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>License undefined</dc:rights>
  <dc:subject xmlns:ns1="xml" ns1:lang="en">Self-healing systems</dc:subject>
  <dc:subject xmlns:ns2="xml" ns2:lang="en">Software reliability</dc:subject>
  <dc:subject xmlns:ns3="xml" ns3:lang="en">Failure Prediction</dc:subject>
  <dc:subject xmlns:ns4="xml" ns4:lang="en">Fault localization</dc:subject>
  <dc:subject xmlns:ns5="xml" ns5:lang="en">Machine learning</dc:subject>
  <dc:subject xmlns:ns6="xml" ns6:lang="en">Data analytics</dc:subject>
  <dc:subject xmlns:ns7="xml" ns7:lang="en">Cloud systems</dc:subject>
  <dc:subject>info:eu-repo/classification/udc/004</dc:subject>
  <dc:title xmlns:ns8="xml" ns8:lang="en">Predicting failures in complex multi-tier systems</dc:title>
  <dc:type>http://purl.org/coar/resource_type/c_db06</dc:type>
</oai_dc:dc>