Show simple item record

AuthorBaig S.-U.-R.
AuthorIqbal W.
AuthorBerral J.L.
AuthorErradi A.
AuthorCarrera D.
Available date2020-04-01T06:54:49Z
Publication Date2019
Publication NameIEEE Systems Journal
ISSN19328184
URIhttp://dx.doi.org/10.1109/JSYST.2019.2918430
URIhttp://hdl.handle.net/10576/13645
AbstractLarge-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.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectData center monitoring
Subjectdata reconstruction
Subjectdata reduction
SubjectMarkov chain models (MMs)
Subjectpolynomial regression (PR)
Subjectreal time
Subjecttelemetry
TitleReal-time data center's telemetry reduction and reconstruction using markov chain models
TypeArticle
Pagination4039-4050
Issue Number4
Volume Number13


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record