Real-Time Data Center's Telemetry Reduction and Reconstruction Using Markov Chain Models
IEEE Systems Journal(2019)
摘要
Large-scale data centers are composed of thousands of servers organized in interconnected racks to offer services to users. These data centers continuously generate large amounts of telemetry data streams (e.g., hardware utilization metrics) used for multiple purposes, including resource management, workload characterization, resource utilization prediction, capacity planning, and real-time analytics. These telemetry streams require costly bandwidth utilization and storage space, particularly at medium-long term for large data centers. This paper addresses this problem by proposing and evaluating a system to efficiently reduce bandwidth and storage for telemetry data through real-time modeling using Markov chain based methods. Our proposed solution was evaluated using real telemetry datasets and compared with polynomial regression methods for reducing and reconstructing data. Experimental results show that data can be lossy compressed up to
$75\%$
for bandwidth utilization and
$95.33\%$
for storage space, with reconstruction accuracy close to
$92\%$
.
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关键词
Telemetry,Data centers,Data models,Real-time systems,Measurement,Markov processes,Bandwidth
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