httperf—a tool for measuring web server performance
ACM SIGMETRICS Performance Evaluation Review
Measuring CPU overhead for I/O processing in the Xen virtual machine monitor
ATEC '05 Proceedings of the annual conference on USENIX Annual Technical Conference
The Definitive Guide to the Xen Hypervisor (Prentice Hall Open Source Software Development Series)
The Definitive Guide to the Xen Hypervisor (Prentice Hall Open Source Software Development Series)
The Eucalyptus Open-Source Cloud-Computing System
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
A Runtime Model Based Monitoring Approach for Cloud
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
OpenNebula: A Cloud Management Tool
IEEE Internet Computing
Response time for cloud computing providers
Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
Black-box and gray-box strategies for virtual machine migration
NSDI'07 Proceedings of the 4th USENIX conference on Networked systems design & implementation
Towards autonomic detection of SLA violations in Cloud infrastructures
Future Generation Computer Systems
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Monitoring of infrastructural resources in clouds plays a crucial role in providing application guarantees like performance, availability, and security. Monitoring is crucial from two perspectives - the cloud-user and the service provider. The cloud user's interest is in doing an analysis to arrive at appropriate Service-level agreement (SLA) demands and thecloud provider's interest is to assess if the demand can be met. To support this, a monitoring framework is necessary particularly since cloud hosts are subject to varying load conditions. To illustrate the importance of such a framework, we choose the example of performance being the Quality of Service (QoS) requirement and show how inappropriate provisioning of resources may lead to unexpected performance bottlenecks. We evaluate existing monitoring frameworks to bring out the motivation for buildingmuch more powerful monitoring frameworks. We then propose a distributed monitoring framework, which enables ï卢ne grained monitoring for applications and demonstrate with a prototype system implementation for typical use cases.