Presentation slides for the 9th EAI International Conference on Performance Evaluation Methodologies and Tools (VALUETOOS 2015) December 14–16, 2015 | Berlin, Germany.
The crux of the talk is the presentation of Palladio Optimization Suite.
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Palladio Optimization Suite: QoS optimization for component-based Cloud applications
1. FP7-ICT-2011-8-318484www.modaclouds.eu
www.modaclouds.eu
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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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
4. 4
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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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FP7-ICT-2011-8-318484
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 2
Workflow – 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 workload
Constraints
• 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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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
Modern applications have greatly changed, in size and scope. In many cases they are web-based with several of services and microservices deployed in different region with many databases and cache systems.
Design them in the best possible way is crucial because an error made in the early stages of design can cost a lot amount of money later.
Software design is somehow a kind of art. There are pattern to follows but they cannot guarantee important QoS characteristics.
Everything has to be done manually, many design alternatives and services must be considered and the quality of Service has to be predicted.
Yes, but how? It is true that there are models and tools to assess the quality of a software platform (QN, Petri nets) that can be solved both analytically or by simulation.
This approach is not user friendly and goes beyond the normal skills of a software architect.
The palladio Optimization suite is a collection of complementary plugins that have in common the idea of optimize the architecture of an application at desing time.
At the moment there are 2 plugins in the collection, namely PerOpteryx and Space4Clouds.
Both run atop Palladio bench, exploiting its metamodels, the grafical interface and the M2M transformation to queuing models.
Both plugins deal with the exploration of the space of possible architectures for a software architecture, but while PO is related to in-house application, S4C refers only to Cloud application with IaaS and PaaS services
Both implements Advanced exploration paradigms in particular Genetic algorithms for peropteryx and Local search based appaorch is implemented withing S4C
PO is tool of Multi-objective optimization of component-based applications
It works with population of candidate solutions and returns a Pareto front of Nondominated solutions is returned
Implements the NSGA-II 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
Design-time QoS assessment and optimization of Cloud applications
Hybrid approach: Mathematical model + Local search based engine
Cost Optimization under QoS and Architectural constraints
LINE Solver
LQNS solver
Percentiles are supported
Random environments
Optimization over a 24-hour time horizon.
Variable workload
Elasticity