Real-time resource prediction engine for cloud management

QC 20171213
Predicting resource requirements for cloud services is critical for dimensioning, anomaly detection and service assurance. We demonstrate a system for real-time estimation of the needed amount of infrastructure resources, such as CPU and memory, for a given service. Statistical learning methods on server statistics and load parameters of the service are used for learning a resource prediction model. The model can be used as a guideline for service deployment and for real-time identification of resource bottlenecks.

TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering hsv//eng
Cloud managements
Infrastructure resources
Real-time estimation
Real-time identification
Resource prediction
Resource requirements
Service deployment
Statistical learning methods
Forecasting

Johnsson, A.Ericsson Research, Sweden (author)
Ahmed, J.Ericsson Research, Sweden (author)
Moradi, F.Ericsson Research, Sweden (author)
Pasquini, R.RISE,SICS,Federal University of Uberlandia, Brazil (author)
Stadler, RolfRISE,KTH,ACCESS Linnaeus Centre,SICS,KTH Royal Institute of Technology, Sweden(Swepub:ri)rolf.stadler@ri.se (author)
Ericsson Research, SwedenSICS (creator_code:org_t)

In:Proceedings of the IM 2017 - 2017 IFIP/IEEE International Symposium on Integrated Network and Service Management: Institute of Electrical and Electronics Engineers Inc., s. 877-8789783901882890

By the author/editor: Flinta, C.; Johnsson, A.; Ahmed, J.; Moradi, F.; Pasquini, R.; Stadler, Rolf

About the subject

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