A survey of autonomic computing—degrees, models, and applications
ACM Computing Surveys (CSUR)
Enabling semantic communications for virtual machines via iConnect
VTDC '07 Proceedings of the 2nd international workshop on Virtualization technology in distributed computing
Adaptive Monitoring with Dynamic Differential Tracing-Based Diagnosis
DSOM '08 Proceedings of the 19th IFIP/IEEE international workshop on Distributed Systems: Operations and Management: Managing Large-Scale Service Deployment
Isolation points: Creating performance-robust enterprise systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Model-Driven Assessment of QoS-Aware Self-Adaptation
Software Engineering for Self-Adaptive Systems
Non-functional data collection for adaptive business processes and decision making
Proceedings of the 4th International Workshop on Middleware for Service Oriented Computing
A systematic and practical approach to generating policies from service level objectives
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
Do you know your IQ?: a research agenda for information quality in systems
ACM SIGMETRICS Performance Evaluation Review
Leveraging many simple statistical models to adaptively monitor software systems
International Journal of High Performance Computing and Networking
Value creation in IT service platforms through two-sided network effects
GECON'12 Proceedings of the 9th international conference on Economics of Grids, Clouds, Systems, and Services
Service Abstractions With Fault Virtualization for Distributed Network Infrastructures
DS-RT '13 Proceedings of the 2013 IEEE/ACM 17th International Symposium on Distributed Simulation and Real Time Applications
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The scale, reliability and cost requirements of enterprise data centers require automation of center management. Examples include provisioning, scheduling, capacity planning, logging and auditing. A key component of such automation functions is online monitoring. In contrast to monitoring systems designed for human users, a particular concern for online enterprise monitoring is Quality of Service (QoS). Since breaking service level agreements (SLAs) has direct financial and legal implications, enterprise monitoring must be conducted so as to maintain SLAs. This includes the ability to differentiate the QoS of monitoring itself for different classes of users or more generally, for software components subject to different SLAs. Thus, without embedding notions of QoS into the monitoring systems used in next generation data centers, it will not be possible to accomplish the desired automation of their operation. This paper both demonstrates the importance of QoS in monitoring and it presents a QoS-capable monitoring system, termed QMON. QMON supports utility-aware monitoring while also able to differentiate between different classes of monitoring, corresponding to classes of SLAs. The implementation of QMON offers high levels of predictability for service delivery (i.e., predictable performance) and it is dynamically configurable to deal with changes in enterprise needs or variations in services and applications. We demonstrate the importance of QoS in monitoring and the QoS capabilities of QMON in a series of case studies and experiments, using a multi-tier web service benchmark.