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Autoscaling through Self-Adaptation Approach in Cloud Infrastructure. A Hybrid Elasticity Management Framework Based Upon MAPE (Monitoring-Analysis-Planning-Execution) Loop, to Ensure Desired Service Level Objectives (SLOs)
Butt, Sarfraz S.
Butt, Sarfraz S.
Publication Date
2019
End of Embargo
Supervisor
Rights
The University of Bradford theses are licenced under a Creative Commons Licence.
Peer-Reviewed
Open Access status
Accepted for publication
Institution
University of Bradford
Department
Faculty of Engineering and Informatics
Awarded
2019
Embargo end date
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Additional title
Abstract
The project aims to propose MAPE based hybrid elasticity management
framework on the basis of valuable insights accrued during systematic analysis
of relevant literature. Each stage of MAPE process acts independently as a
black box in proposed framework, while dealing with neighbouring stages. Thus,
being modular in nature; underlying algorithms in any of the stage can be
replaced with more suitable ones, without affecting any other stage.
The hybrid framework enables proactive and reactive autoscaling approaches
to be implemented simultaneously within same system. Proactive approach is
incorporated as a core decision making logic on the basis of forecast data, while
reactive approach being based upon actual data would act as a damage control
measure; activated only in case of any problem with proactive approach. Thus,
benefits of both the worlds; pre-emption as well as reliability can be achieved
through proposed framework. It uses time series analysis (moving average
method / exponential smoothing) and threshold based static rules (with multiple monitoring intervals and dual threshold settings) during analysis and planning
phases of MAPE loop, respectively. Mathematical illustration of the framework
incorporates multiple parameters namely VM initiation delay / release criterion,
network latency, system oscillations, threshold values, smart kill etc. The
research concludes that recommended parameter settings primarily depend
upon certain autoscaling objective and are often conflicting in nature. Thus, no
single autoscaling system with similar values can possibly meet all objectives simultaneously, irrespective of reliability of an underlying framework. The
project successfully implements complete cloud infrastructure and autoscaling
environment over experimental platforms i-e OpenStack and CloudSim Plus.
In nutshell, the research provides solid understanding of autoscaling
phenomenon, devises MAPE based hybrid elasticity management framework
and explores its implementation potential over OpenStack and CloudSim Plus.
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Citation
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Type
Thesis
Qualification name
MPhil