Real-time resource prediction engine for cloud management

Real-time resource prediction engine for cloud management

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 s...

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Bibliographic Details
Published in:2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM) pp. 877 - 878
Main Authors: Flinta, Christofer, Johnsson, Andreas, Ahmed, Jawwad, Moradi, Farnaz, Pasquini, Rafael, Stadler, Rolf
Format: Conference Proceeding
Language:English
Published: IFIP 01-05-2017
Subjects:
Analytical models
Load modeling
Measurement
Memory management
Predictive models
Real-time systems
Servers
Online Access:Get full text
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Description
Summary: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.
DOI:10.23919/INM.2017.7987392