An Architecture for Coordinating Multiple Self-Management Systems
WICSA '04 Proceedings of the Fourth Working IEEE/IFIP Conference on Software Architecture
Rainbow: Architecture-Based Self-Adaptation with Reusable Infrastructure
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
The monitorability of service-level agreements for application-service provision
WOSP '07 Proceedings of the 6th international workshop on Software and performance
SLA-Driven Clustering of QoS-Aware Application Servers
IEEE Transactions on Software Engineering
Building self-adapting services using service-specific knowledge
HPDC '05 Proceedings of the High Performance Distributed Computing, 2005. HPDC-14. Proceedings. 14th IEEE International Symposium
Efficient online monitoring of web-service SLAs
Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
Self-adaptive software: Landscape and research challenges
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Using quantitative analysis to implement autonomic IT systems
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Software Engineering for Self-Adaptive Systems: A Research Roadmap
Software Engineering for Self-Adaptive Systems
MUSIC: Middleware Support for Self-Adaptation in Ubiquitous and Service-Oriented Environments
Software Engineering for Self-Adaptive Systems
StarMX: A framework for developing self-managing Java-based systems
SEAMS '09 Proceedings of the 2009 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems
Monitoring probabilistic properties
Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Runtime Monitoring of Web Service Conversations
IEEE Transactions on Services Computing
Comprehensive QoS monitoring of Web services and event-based SLA violation detection
Proceedings of the 4th International Workshop on Middleware for Service Oriented Computing
Journal of Systems and Software
An empirical comparison of methods to support QoS-aware service selection
Proceedings of the 2nd International Workshop on Principles of Engineering Service-Oriented Systems
Towards Measuring the Degree of Fulfillment of Service Level Agreements
ICIC '10 Proceedings of the 2010 Third International Conference on Information and Computing - Volume 03
An effective sequential statistical test for probabilistic monitoring
Information and Software Technology
Monitoring of Probabilistic Timed Property Sequence Charts
Software—Practice & Experience
Dynamic QoS Management and Optimization in Service-Based Systems
IEEE Transactions on Software Engineering
DySOA: making service systems self-adaptive
ICSOC'05 Proceedings of the Third international conference on Service-Oriented Computing
Using Automated Control Charts for the Runtime Evaluation of QoS Attributes
HASE '11 Proceedings of the 2011 IEEE 13th International Symposium on High-Assurance Systems Engineering
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
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Currently software systems operate in highly dynamic contexts, and consequently they have to adapt their behavior in response to changes in their contexts or/and requirements. Existing approaches trigger adaptations after detecting violations in quality of service (QoS) requirements by just comparing observed QoS values to predefined thresholds without any statistical confidence or certainty. These threshold-based adaptation approaches may perform unnecessary adaptations, which can lead to severe shortcomings such as follow-up failures or increased costs. In this paper we introduce a statistical approach based on CUSUM control charts called AuDeQAV - Automated Detection of QoS Attributes Violations. This approach estimates at runtime a current status of the running system, and monitors its QoS attributes and provides early detection of violations in its requirements with a defined level of confidence. This enables timely intervention preventing undesired consequences from the violation or from inappropriate remediation. We validated our approach using a series of experiments and response time datasets from real-world web services.