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Identifier 000388449
Title Cross-layer monitoring and adaptation of multi-cloud service-based applications
Alternative Title Παρακολούθηση και προσαρμογή εφαρμογών βασισμένων σε ηλεκτρονικές υπηρεσίες πάνω σε πολλαπλά υπολογιστικά νέφη
Author Ζεγκίνης, Χρυσόστομος
Thesis advisor Πλεξουσάκης, Δημήτρης
Abstract Service-Oriented Architecture (SOA) emerged in the late 90s, introducing Web services as a new means for delivering software over a network. Nowadays, it provides many opportunities for businesses to automate their processes, by providing services to either end-user applications or to other services distributed in a network, via published and discoverable interfaces. The success of many Web service related projects has shown that existing technologies enables implementing a true SOA. However, the evolution of Web services indicates that they are moving beyond the simple exchange of information to the concept of combining existing and new applications in order to provide more complex Service-based Applications (SBAs). Consequently, businesses will be able to create customizable composite SBAs, also integrating back-end and older technology systems found in local or remote applications. With the advent of the new century, a number of additional pieces of the computing “puzzle” fell into place to complement SOA and reflect the new trends introduced by the Internet of things (IoT), which refers to the interconnection of uniquely identifiable embedded computing devices with the existing Internet infrastructure. Cloud computing has emerged as a new paradigm for delivering “asa-service” offerings to the end users: (i) Software-as-a-Service (SaaS) is the software delivery model adopted in Cloud computing, where Web services are made available to users on demand via the Internet from a Cloud provider, (ii) Platform-asa-Service (PaaS) provides the platform to the application owners to deploy their applications on, and (iii) Infrastructure-as-a-Service (IaaS) is a resource provision-ing model allowing Cloud providers to outsource equipment, i.e. Virtual Machines (VMs), required by SaaS and PaaS. The dynamic nature of Web services and the vulnerable execution environment in which they perform requires that their functional and non-functional (Quality of Service (QoS)) characteristics should be monitored. The monitoring process is essential in order to gain a clear view of how they perform within their operational environment, take management decisions and perform adaptation actions to modify and adjust their behavior, according to the new posed requirements. This dissertation addresses monitoring and adaptation of SBAs deployed on multiple Clouds, introducing an Event based Cross-layer Monitoring and Adaptation Framework, named ECMAF. As SOA and Cloud architecture comprises a number of functional layers, including various application components, spanning from abstract business processes to concrete infrastructure resources, monitoring and adaptation should take into account the layers’ dependencies in order to efficiently correlate the monitoring events and promptly determine the most suitable adaptation actions that should be triggered. The proposed approach takes into consideration all the Cloud and SOA layers comprising a multi-Cloud SBA. To investigate the applicability of ECMAF and demonstrate its benefits, we have implemented and deployed a traffic management application on a multi- Cloud setup. This application has been suitably designed to optimally perform on multiple Clouds offering different storage and computational power. The Clouds used for the SBA’s deployment exhibit a number of dependencies among the involved components across all the SOA and Cloud layers, captured in a component meta-model, which has been especially designed to model a snapshot of the current SBA deployment status and the dependencies of the active components. These dependencies are exploited by the ECMAF framework in order to extract valid event patterns causing specific Service-level Objectives (SLO) violations, which are further processed to form more complex ones, mapping to suitable adaptation strategies. Moreover, these dependencies are also enriched at run-time to reflect new behaviors of the SBA, dictated by a context or an individual component’s status modification. Additional meta-models for validating the monitoring events, as well as the adaptation strategies supported by the ECMAF’s incorporated adaptation engine, are introduced. Experimental evaluation is performed using synthetically generated datasets including various event patterns, investigating the ECMAF’s performance and scalability, in terms of execution time and throughput, as well as optimality and accuracy with regards to the produced adaptation strategy. The results show excellent agreement with theoretical the main work assumptions. In particular, the performance of the pattern discovery process mainly relies on the metric’s definition, which can be adjusted to reflect the optimal time interval dictated by the pattern discovery process, so as to produce the most effective proactive adaptation results. As far as the performance results are concerned, they show that the ECMAF’s execution time depends mostly on the considered monitored metrics, as well as on the deployment’s scope (single or multi-Cloud) and SBA’s size (i.e. the number of individual services). The accuracy and the performance of the adaptation process (mainly the scaling actions) are mainly based on the image size, the location and the number of the provisioned VMs, but mostly depends on the expertise of the adaptation strategy designer. Finally, the overall ECMAF’s evaluation results reveal efficient handling of the detected monitoring events, thus enabling the successful addressing of the individual SLO violations (i.e. reactive adaptation), as well as of the discovered critical event patterns causing aggregate metric’s violations (i.e. proactive adaptation).
Language English
Subject Cloud computing
Web services modeling
Μοντελοποίηση
Issue date 2014-11-04
Collection   School/Department--School of Sciences and Engineering--Department of Computer Science--Doctoral theses
  Type of Work--Doctoral theses
Permanent Link https://elocus.lib.uoc.gr//dlib/4/1/8/metadata-dlib-1415363684-835749-20401.tkl Bookmark and Share
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