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The volume of data to be collected and processed for effective real-time monitoring of large-scale computing systems and networks poses significant Big Data challenges, and a scalable solution requires a systematic approach to dimensionality reduction during the data collection, transmission, and analysis phases. Compressive sampling can reduce the dimensionality of the data collected at the source...
Performance monitoring of datacenters provides vital information for dynamic resource provisioning, anomaly detection, capacity planning, and metering decisions. Online monitoring, however, incurs a variety of costs: the very act of monitoring a system interferes with its performance, consuming network bandwidth and disk space. With the goal of reducing these costs, we develop and validate a strategy...
Online performance monitoring of computer systems incurs a variety of costs: the very act of monitoring a system interferes with its performance and if the information is transmitted to a monitoring station for analysis and logging, this consumes network bandwidth and disk space. Compressive sampling-based schemes can help reduce these costs on the local machine by acquiring data directly from the...
Performance monitoring of data centers provides vital information for dynamic resource provisioning, fault diagnosis, and capacity planning decisions. However, the very act of monitoring a system interferes with its performance, and if the information is transmitted to a monitoring station for analysis and logging, this consumes network bandwidth and disk space. This paper proposes a low-cost monitoring...
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