Observatorio de I+D+i UPM

Memorias de investigación
Conferences:
Constraint-Based Runtime Prediction of SLA Violations in Service Orchestrations
Year:2012
Research Areas
  • Engineering
Information
Abstract
Service-Oriented Computing is an approach to creating applications where di erent loosely-coupled software services are composed in order to accomplish a goal that is more complex than what the constituent services can do. The composition is performed in a platform-independent manner (component ser- vices are executed in heterogeneous, usually non-local environments and accessed through a standardized interface) and is usually a long-running process which spans across organizations and administrative boundaries. In turn, these combi- nations are usually exposed as services themselves. Service combinations are usually divided into orchestrations and choreogra- phies. In the former case there is a single agent which controls the individual services and routes the data between them. In the latter, data movements and control are not centralized. In this talk we will focus on service orchestrations. A critical point for the usability of service compositions is the Quality-of- Service (QoS) they o er. Execution time, availability, or monetary cost are some usual metrics. The acceptable values for QoS attributes in a business relation are usually de?ned in Service Level Agreements (SLAs), along with the penalties in case they are violated. We present and evaluate a method whereby, using techniques from constraint logic programming, we derive, at a given point of execution of a service compo- sition, a set of constraints that predict SLA conformance and violation scenarios over a certain time horizon. This is done on the basis of the structure of the composition and known or empirically measured properties of the component services. SLA failure and conformance constraints are expressed symbolically and may be used by other components for, e.g., development of data-mining models, optimized service matching, or triggering preventive adaptation or heal- ing. Additional precision can be obtained within the same analysis framework by inspecting the state of the composition at the point of prediction
International
Si
Entity
Entity Nationality
Sin nacionalidad
Place
Bupapest
Participants
  • Autor: Dragan Ivanovic . (UPM)
  • Autor: Manuel Carro Liñares (UPM)
  • Autor: Manuel de Hermenegildo Salinas (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Computación lógica, Lenguajes, Implementación y Paralelismo (CLIP)
  • Departamento: Inteligencia Artificial
S2i 2019 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)