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 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. 

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

Nyckelord

Cloud managements

Infrastructure resources

Real-time estimation

Real-time identification

Resource prediction

Resource requirements

Service deployment

Statistical learning methods

Forecasting

Publikations- och innehållstyp

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