FP7-ICT-2011-8-318484 www.modaclouds.eu w w w . m o d a c l o u d s . e u Palladio Optimization Suite: QoS optimization for component-based Cloud applications Michele Ciavotta, Danilo Ardagna Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria Anne Koziolek Karlsruhe Institute of Technology, Institute for Program Structures and Data Organization
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Palladio Optimization Suite: QoS optimization for component-based Cloud applications
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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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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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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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• 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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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
• 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