How to save money with computer monitoring
ACM '72 Proceedings of the ACM annual conference - Volume 2
Predicting the Performance of Globus Monitoring and Discovery Service (MDS-2) Queries
GRID '03 Proceedings of the 4th International Workshop on Grid Computing
GridICE: a monitoring service for Grid systems
Future Generation Computer Systems - Special issue: High-speed networks and services for data-intensive grids: The DataTAG project
Assessing the Performance Impact of Service Monitoring
ASWEC '10 Proceedings of the 2010 21st Australian Software Engineering Conference
Platform-as-a-Service Architecture for Real-Time Quality of Service Management in Clouds
ICIW '10 Proceedings of the 2010 Fifth International Conference on Internet and Web Applications and Services
A Service-Oriented Framework for GNU Octave-Based Performance Prediction
SCC '10 Proceedings of the 2010 IEEE International Conference on Services Computing
ISCC '11 Proceedings of the 2011 IEEE Symposium on Computers and Communications
SP 800-145. The NIST Definition of Cloud Computing
SP 800-145. The NIST Definition of Cloud Computing
Monitoring service choreographies from multiple sources
SERENE'12 Proceedings of the 4th international conference on Software Engineering for Resilient Systems
A generative approach for the adaptive monitoring of SLA in service choreographies
ICWE'13 Proceedings of the 13th international conference on Web Engineering
A service framework for energy-aware monitoring and VM management in Clouds
Future Generation Computer Systems
The QoS-based MCDM system for SaaS ERP applications with Social Network
The Journal of Supercomputing
Future Generation Computer Systems
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While Cloud computing offers the potential to dramatically reduce the cost of software services through the commoditization of IT assets and on-demand usage patterns, one has to consider that Future Internet applications raise the need for environments that can facilitate real-time and interactivity and thus pose specific requirements to the underlying infrastructure. The latter, should be able to efficiently adapt resource provisioning to the dynamic Quality of Service (QoS) demands of such applications. To this direction, in this paper we present a monitoring system that facilitates on-the-fly self-configuration in terms of both the monitoring time intervals and the monitoring parameters. The proposed approach forms a multi-layered monitoring framework for measuring QoS at both application and infrastructure levels targeting trigger events for runtime adaptability of resource provisioning estimation and decision making. Besides, we demonstrate the operation of the implemented mechanism and evaluate its effectiveness using a real-world application scenario, namely Film Post Production.