FP7-ICT-2011-8-318484www.modaclouds.eu

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Palladio Optimization Suite: QoS optimization forcomponent-based Cloud applications

Michele Ciavotta, Danilo Ardagna Politecnico di Milano,

Dipartimento di Elettronica, Informazione e Bioingegneria

Anne Koziolek Karlsruhe Institute of

Technology, Institute forProgram Structures and Data

Organization

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FP7-ICT-2011-8-318484

Introduction• Modern software applications have evolved in terms of size and scope• Design an application in the best possible way is crucial• It is often a manual process = arduous and time-consuming• Explore the space of design alternatives and (cloud) services• Assess the QoS of several Software Architectures (SAs)

• Specific models and tools have been created to predict the QoS • Analytical and Simulation based solvers• Not user friendly

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FP7-ICT-2011-8-318484

Palladio Optimization Suite

• A collection of complementary plugins (at the moment 2)• Running atop Palladio Bench (graphical interface and M2M transf.)• Automatic exploration of the space of possible architectures • Advanced exploration paradigms• Evolutionary Algorithm• Local Search

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FP7-ICT-2011-8-318484

• Multi-objective optimization of component-based applications• A Pareto front of Nondominated solutions

• Evolutionary algorithm to explore the architectural space

• An initial population from a candidate solution defined by the user in Palladio Component Model (PCM) format

• The individuals are then modified along degrees of freedom.• Server Farm Configuration• Component Selection• Component Allocation

PerOpteryx SPACE4Clouds

• Optimization of Cloud architectures• Hybrid approach: Mathematical model +

Local search based engine• Cost Optimization under QoS and

Architectural constraints• LINE Solver• Percentiles are supported• Random environments

• Optimization over a 24-hour time horizon.• Variable workload• Elasticity

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Objectives• Minimize

• Application Cost (one hour)• Response time (2 components)

• Maximize• Throughput (2 components)

Possible decisions:• Allocation of 9 software components• Different VM types per component groups

Workflow - Phase 1: PerOpteryx

Solver• SimuCom (Simulation)

Solution:• Hourly cost: 1.29 $• Avg. Resp. Time: 0.30 s• Avg. Throughput: 9.9 req./s• Number of tiers: 5

Workflow – Phase 1

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FP7-ICT-2011-8-318484

Workflow – Phase 2Workflow – Phase 2: Space4Cloud

Possible decisions: • Type of VM for each application

tier• Number of VM for each our of

the day

Objective• Minimization of the daily cost

under variable workloadConstraints• Average response time < 0.6 s• 95-th percentile < 1 s

Solvers• MILP solver (Cplex, CBC, etc)• LINE solver (for LQN models)

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FP7-ICT-2011-8-318484

Conclusions

• Suite for multi-attribute QoS optimization of component based cloud applications• Combination of an evolutionary optimization with a local-search-based approach• Time-varying workload and the distinctive traits of the cloud are considered

Future work• More work on the integration of the two tools• Validation of the results on industrial settings• Extension to data-intensive applications

FP7-ICT-2011-8-318484www.modaclouds.eu

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Thank You!