Real-time data center's telemetry reduction and reconstruction using markov chain models

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%. - 2007-2012 IEEE.Scopu

Real-time data center's telemetry reduction and reconstruction using Markov chain models

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%.This work was supported in part by the European Research Council (ERC) under the EU Horizon 2020 programme under Grant GA 639595, in part by the Spanish Ministry of Economy, Industry and Competitiveness under Grant TIN2015-65316-P and Grant IJCI2016-27485, in part by the Generalitat de Catalunya under Grant 2014-SGR-1051, in part by the University of the Punjab, Pakistan, and in part by the Qatar National Research Fund (a member of Qatar Foundation) under NPRP Grant # NPRP9-224-1-049.Peer Reviewe