Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Rewriting-Based Techniques for Runtime Verification
Automated Software Engineering
QoS Enhancement for PDES Grid Based on Time Series Prediction
GCC '07 Proceedings of the Sixth International Conference on Grid and Cooperative Computing
QoS Proxy Architecture for Real Time RPC with Traffic Prediction
DS-RT '07 Proceedings of the 11th IEEE International Symposium on Distributed Simulation and Real-Time Applications
Exploring event correlation for failure prediction in coalitions of clusters
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Event-Driven Quality of Service Prediction
ICSOC '08 Proceedings of the 6th International Conference on Service-Oriented Computing
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
Storage Performance Optimization Based on ARIMA
DBTA '09 Proceedings of the 2009 First International Workshop on Database Technology and Applications
An Adaptive Web Services Selection Method Based on the QoS Prediction Mechanism
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Runtime Monitoring of Web Service Conversations
IEEE Transactions on Services Computing
An empirical comparison of methods to support QoS-aware service selection
Proceedings of the 2nd International Workshop on Principles of Engineering Service-Oriented Systems
Distributed QoS Evaluation for Real-World Web Services
ICWS '10 Proceedings of the 2010 IEEE International Conference on Web Services
Automating QoS Based Service Selection
ICWS '10 Proceedings of the 2010 IEEE International Conference on Web Services
Quantifying event correlations for proactive failure management in networked computing systems
Journal of Parallel and Distributed Computing
An effective sequential statistical test for probabilistic monitoring
Information and Software Technology
Business process performance prediction on a tracked simulation model
Proceedings of the 3rd International Workshop on Principles of Engineering Service-Oriented Systems
Dynamic QoS Management and Optimization in Service-Based Systems
IEEE Transactions on Software Engineering
Towards accurate failure prediction for the proactive adaptation of service-oriented systems
Proceedings of the 8th workshop on Assurances for self-adaptive systems
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
Statistical detection of QoS violations based on CUSUM control charts
ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
An Approach to Forecasting QoS Attributes of Web Services Based on ARIMA and GARCH Models
ICWS '12 Proceedings of the 2012 IEEE 19th International Conference on Web Services
Self-adaptive workload classification and forecasting for proactive resource provisioning
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
Regression-based utilization prediction algorithms: an empirical investigation
CASCON '13 Proceedings of the 2013 Conference of the Center for Advanced Studies on Collaborative Research
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Predicting future values of Quality of Service (QoS) attributes can assist in the control of software intensive systems by preventing QoS violations before they happen. Currently, many approaches prefer Autoregressive Integrated Moving Average (ARIMA) models for this task, and assume the QoS attributes' behavior can be linearly modeled. However, the analysis of real QoS datasets shows that they are characterized by a highly dynamic and mostly nonlinear behavior to the extent that existing ARIMA models cannot guarantee accurate QoS forecasting, which can introduce crucial problems such as proactively triggering unrequired adaptations and thus leading to follow-up failures and increased costs. To address this limitation, we propose an automated forecasting approach that integrates linear and nonlinear time series models and automatically, without human intervention, selects and constructs the best suitable forecasting model to fit the QoS attributes' dynamic behavior. Using real-world QoS datasets of 800 web services we evaluate the applicability, accuracy, and performance aspects of the proposed approach, and results show that the approach outperforms the popular existing ARIMA models and improves the forecasting accuracy by on average 35.4%.