2011 IEEE 4th International Conference on Cloud Computing 2011
DOI: 10.1109/cloud.2011.75
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Profiling Applications for Virtual Machine Placement in Clouds

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Cited by 61 publications
(29 citation statements)
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“…Several realistic workload datasets from different datacenters are applied to evaluate the system performance. Do et al [13] propose a profiling system for the decision making of job scheduling and resource allocation. Their approach adopts the canonical correlation analysis method to identify the relationship between application performance and resource usage.…”
Section: Related Workmentioning
confidence: 99%
“…Several realistic workload datasets from different datacenters are applied to evaluate the system performance. Do et al [13] propose a profiling system for the decision making of job scheduling and resource allocation. Their approach adopts the canonical correlation analysis method to identify the relationship between application performance and resource usage.…”
Section: Related Workmentioning
confidence: 99%
“…The authors of [16] have investigated the relationship between resource demands and application performance metrics. They have presented an application profiling technique using a Canonical Correlation Analysis (CCA) method.…”
Section: Related Workmentioning
confidence: 99%
“…We also combine SLA-based control of distributed systems with action scheduling based on previously determined relations between application load and the number of active services, however we consider additional algorithms for processing the monitoring information such as a genetic algorithm and result from applying Little's Law. Anh et al [50] analyse the performance of cloud applications, which share resources with other running applications in the same physical host, by looking at the correlations between application and system performance. We use statistical correlation [47] between the time series corresponding to the performance monitoring metrics of distributed services in order to determine the set of predictors of critical SLA metrics, which can then be used for controlling services scaling.…”
Section: Related Work On Prediction Algorithmsmentioning
confidence: 99%