The Cloud computing paradigm emerged by establishing innovative resources provisioning and consumption models. Together with the improvement of resource management techniques, these models have contributed to an increase in the number of application developers that are strong supporters of partially or completely migrating their application to a highly scalable and pay-per-use infrastructure. However, due to the continuous growth of Cloud providers and Cloud offerings, Cloud application developers nowadays must face additional application design challenges related to the efficient selection of such offerings to optimally distribute the application in a Cloud infrastructure. Focusing on the performance aspects of the application, additional challenges arise, as application workloads fluctuate over time, and therefore produce a variation of the infrastructure resources demands. In this research work we aim to define and realize the underpinning concepts towards supporting the optimal (re-)distribution of an application in the Cloud in order to handle fluctuating over time workloads.
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Design_Support_Cloud_Application_Redistribution
1. Research
University of Stuttgart
Universitätsstr. 38
70569 Stuttgart
Germany
Phone +49-711-685 88337
Fax +49-711-685 88472
Santiago Gómez Sáez and Frank Leymann
Institute of Architecture of Application Systems
{gomez-saez,leymann}@iaas.uni-stuttgart.de
Design Support for
Performance-aware Cloud
Application (Re-)Distribution
ESOCC PhD 2014
Take into account using the linear program solving (for optimization)
What about if there is no solution in the space of alternative topologies?
Make an animation here with the perspective and the background
TOSCA: cloud application portability among Cloud infrastructures
MADCAT: methodological approach targeting the creation of structured native applications covering all phases of its life cycle and including iterative refinement and documentation of decisions made during the application life cycle.
Palladio component model: model driven performance prediction aimed at identify the performance bottlenecks of software architectures.
CloudMiG: cloud migration support towards comparing and planning the migration of an application to the Cloud. Simulation techinques for monitored workloads. It requires the modeling of cloud environments with the help of cloud profiles.
MOCCA: cloud application topology optimization through the introduction of variability points and optimization techniques based on non functional requirements.
Elasticity
Studies demonstrate that achieving an optimal application throughput is complex and involves more than simply increasing the number of VMs, and it requires an analysis on the application profile, as there may be concrete resources scaling configurations that negatively impact on the applications performance. They deployed two variants of an application with two different profiles. Application scaling required understanding the application profile as well as dependencies among the application components.
Different auto-scaling techniques and algorithms are presented in several works. However, to take advantage of the flexibility that auto-scaling techniques offer, it is necessary to adjust it to the incoming workload behavior, and therefore the application profile, enabling dynamicity for the thresholds.
Reactive: AWS autoscaling through the specification of thresholds. Proactive: time series analysis
Performance expectation: Quasar resource management system: based on the specification on performance constraints and letting Quasar determine the most appropriate resource configuration in order to satisfy such constraints. It uses classification techniques to determine the impact of the amount of resources for the workload performance.
Such approaches either focus on providing the means to specify a concrete application distribution, support during the initial phases of the application design, focus on selecting the most efficient Cloud provider or best resource configuration. In this work we go a step further by providing the means to application developers to (re-)distribute their application wrt available Cloud offerings to cope with fluctuating workloads.
Providing therefore the Cloud application developers with such design support
to optimally distribute and re-distribute the application to cope with uctuating
workloads and performance demands raises several challenges. Such decision support
must cover the complete application life-cycle, dene the underpinning concepts, and
provide the required mechanisms towards targeting the analysis and evaluation of
the evolutionary aspects of the application performance, e.g. its workload uctuation.