Search-based Optimization of Cyber-Physical System Software Deployment

1
Search-based Optimization of Cyber-Physical
System SoftwareDeployment Configuration
Dr. Christopher Jules White jules_at_dre.vanderbilt.
edu www.dre.vanderbilt.edu/jules/ Research
Assistant Professor Vanderbilt University
Nashville, Tennessee
Presented at the University of Alabama,
Birmingham, February 2nd, 2009
Sponsors NSF, Air Force Research Lab, Lockheed
Martin Advanced Technology Lab, Raytheon,
Siemens Corporate Research
2
Cyber-Physical Systems Quality of Service

• A Cyber-Physical System (CPS) involves close
  coordination between the system's computational
  physical elements 
• CPS applications areessential in mission-
  safety-critical domains, such as aerospace,
  automotive, chemical processes, civil
  infrastructure, energy, entertainment,
  healthcare, manufacturing, transportation
• CPS applications must adhere to complex sets of
  Quality of Service (QoS) constraints, such as
  real-time scheduling, to ensure safety
  efficiency

3
QoS Constraints Make Manual CPS Design Hard
Safety Constraints
Fault-tolerance Constraints
Resource Constraints
Real-time Scheduling Constraints
Cost Constraints

• A key problem in developing CPS systems is that
  the large number of stringent QoS constraints
  the need for optimization make it hard for
  developers to manually find valid/optimized
  design solutions
• We need intelligent modeling automation
  techniques to help developers find solutions to
  these hard CPS design problems

4
Open Deployment Configuration Problems in CPS
Automated Software Configuration
Software Configuration Constraints
Resource Constraints
Cost Constraints
Problem 1
Problem 2
Problem 3
Problem 1 How to automate the configuration of
software components to meet resource constraints
optimize a linear function at scale? (Past Work)
5
Open Deployment Configuration Problems in CPS
Fault-tolerance Constraints
Real-time Scheduling Constraints
Resource Constraints
Cost Constraints
Problem 1
Problem 2
Problem 3
Problem 2 How can the software deployment to
processors in CPS be automated optimized at
scale? (Ongoing / Emerging Work)
6
Open Deployment Configuration Problems in CPS
Real-time Scheduling Constraints
Resource Constraints
Cost Constraints
Problem 1
Problem 2
Problem 3
Problem 3 How can the software deployment
configuration be optimized in tandem to meet
real-time scheduling, resource, fault-tolerance
constraints? (Future Work)
7
Summary of My Research Area
Search-based Automation
Software Deployment Configuration at Scale
Modeling
My work focuses on using modeling search-based
automation to decrease the complexity of CPS
software deployment configuration My work
spans the gamut of theory, tools,
experimentation at scale
8
Open Deployment Configuration Problems in CPS
Automated Software Configuration
Software Configuration Constraints
Resource Constraints
Cost Constraints
Problem 1
Problem 2
Problem 3
Problem 1 How to automate the configuration of
software components to meet resource constraints
optimize a linear function at scale? (Past Work)
9
Problem 1 Goal Roadmap

• Goal Help designers automate the completion of
  partial software configurations for large-scale
  systems
• Context CPS Software Product Lines

Goal
Challenges
Solution
Evaluation
CPS Configuration
10
Problem 1 Automated CPS Software Configuration
CPS Conf
CPS SPLs
Feature Models
Automated Conf.
Poor Deployment Configuration
Good Deployment Configuration

• Key Challenges
• CPS applications can have 100s to 1,000s of
  components constraints making finding ANY valid
  configuration hard
• Optimization is critical, e.g., differences of a
  few can translate into millions of in
  additional costs or drastic differences in QoS
• The configuration can have a substantial impact
  on QoS

11
Software Product-lines
CPS Conf
CPS SPLs
Feature Models
Automated Conf.
Conf. 3
Conf. 2
Software Product Line
Configuration 1

• Software Product Lines (SPLs) create software
  that can be reconfigured for new requirement sets
• The configuration rules for the software are
  capture in a model
• Development is done via configuration rather than
  coding or ad hoc reuse

12
Feature Model Configuration Grammar
CPS Conf
CPS SPLs
Feature Models
Automated Conf.
Automobile Variability in a Feature Model

• Feature Models have become a widely accepted
  modeling standard to specify SPL configuration
  rules
• Software variabilities increments of
  functionality are modeled as features
• Feature models are a tree-like arrangement of
  features constraints between features
• Configurations of SPL variants are described as
  feature selections

13
Goal Helping Designers via Configuration
Automation
CPS Conf
CPS SPLs
Feature Models
Automated Conf.
Goal

• User manually selects desired features
• User specifies key constraints that must be
  adhered to, such as resource limits
• Intelligent automation determines how to optimize
  the selection of the remaining features adhere
  to constraints

Automation selects the remaining 980 features
The user selects the 20 important features
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Problem 1 Challenge Roadmap

• Challenges
• Configuration scale
• Multiple models
• Mixed resource configuration constraints
• Limitations of Existing Solutions

Goal
Challenges
Solution
Evaluation
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Challenge 1.1 Scalable Automated Configuration
Automobile feature model has 1,000s of features
memory cost constraints
Automobile Software 5K Features

• Selecting a valid set of features that satisfies
  a resource constraint is NP-Hard
• It is estimated that current cell phones have 1
  million lines of code
• Automobiles are estimated to have 100s of
  millions of lines of codes
• Defense system software repositories are also
  growing dramatically

Large-scale problems most need automation to
reduce complexity!
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Challenge 1.2 Multi-model Automated Configuration
..
Brake Controller
GPS Navigation

• In production CPS environments, the problem is
  even harder
• We have multiple independent applications sharing
  the same resources, such as memory
• Each application is represented by an independent
  feature model
• The feature selections across all feature models
  must satisfy the resource constraints

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Challenge 1.3 Mixed Resource/Configuration

• Can we just turn this into a knapsack problem?
• or bin-packing?
• Feature selection with resource constraints is
  NP-hard

Knapsack
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Existing Work CSP-based Automated Configuration
CSP Solver
Existing Approaches

• Feature models mapped to Constraint Satisfaction
  Problems (CSPs)
• Partial configurations represented as a partial
  labeling of the CSP variables
• A CSP solver is used to autonom-ously complete
  partial configuration

1. Jules White et al., Constraint-based Model
   Weaving, Springer Transactions on Aspect-Oriented
   Programming Special Issue on Aspects
   Model-Driven Engineering, 39pgs., (to appear)
2. Jules White et al., Automated Diagnosis of
   Product-line Configuration Errors in Feature
   Models, Software Product Lines Conference (SPLC),
   10pgs., September, 2008, Limmerick, Ireland (30
   Acceptance Rate), Received the Best Paper
   Award

Automated Configuration Choices
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CSP Approaches Dont Scale to Emerging CPS
CSP approaches can top out around 150 features
for tough configuration problems with resource
constraints We want techniques to handle
1,000s of features!
CSP
There are 100,000,000,000,000 possible
configurations for this example
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Problem 1 Solution Roadmap

• Solution Approach Transform feature
  configuration problems with resource constraints
  into multi-dimensional multiplechoice knapsack
  problems
• Benefits Ability to apply scalable
  polynomial-time heuristic algorithms aggregate
  sets for multiple models

Goal
Challenges
Solution
Evaluation
Set Population
FCF Alg.
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Solution Approach Filtered Cartesian Flattening
Set Population
FCF Alg.

• Our solution approach is called Filtered
  Cartesian Flattening (FCF)
• We reduce the feature model to the less
  constrained Multidimensional Multiple-choice
  Knapsack Problem (MMKP)
• We use an MMKP heuristic algorithm to solve
• We translate the MMKP solution into a feature
  model configuration

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Overview of MMKP
Set Population
FCF Alg.

• Divides items into sets requires choosing
  exactly one item from each set
• Challenge 1 Solution ? There are excellent
  polynomial-time MMKP algs
• Challenge 2 Solution ? MMKP mixes resource
  configuration constraints

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Simplify by Converting FM to MMKP Sets/Items
Set Population
FCF Alg.

• We simplify the configuration problem by
  converting to an MMKP
• To convert to MMKP, we create a series of MMKP
  sets, such that
• The items in the sets represent partial feature
  model configurations
• The partial configurations are divided into sets
  such that choosing one configuration from each
  set always results in a complete valid
  configuration

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Flattening/Filtering (Compilation)
Set Population
FCF Alg.
Set (Euclidean,PCA,Face..), (MahCosine,PCA,Face
..), (IdaSoft,LDA,Face..), .

• We create the items sets by enumerating the
  possible configurations of subtrees
• A state explosion occurs as we flatten the
  subtrees by enumerating the configurations
• We bound the number of items per set
• A heuristic (filter) is used to select items when
  the bound is exceeded
• We calculated value/resource consumption
  took the top 2,000
  partial configurations

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Simplify Multi-Model Configuration with MMKP
Set Population
FCF Alg.
..
Brake Controller
Infotainment System
GPS Navigation

• Challenge 3 Solution ? Multiple feature models
  can be merged into the same MMKP problem by
  collecting their MMKP sets

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Problem 1 Evaluation

• Evaluation Empirically demonstrate that FCF can
  configure feature models far beyond the scale of
  standard CSP techniques

Goal
Challenges
Solution
Evaluation
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FCF Scalability Validation
CSP technique
FCF can scale up to problems CSP cant handle
is fast (10s ms)
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FCF Scales to Models with 1,000 Features
Models with 1,000 features configured in 1s
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FCF Scales to Models with 10,000 Features
Models with 10,000 features configured in 1-12s
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FCF Provides Configuration Assistant
Results

• A developer can use FCF to rapidly analyze
  different configuration alternatives
• What configuration minimizes memory while adding
  infotainment capabilities?
• Is it possible to find a configuration that
  includes ABS Brake Controllers for less than X?
• Software configurations are generated in seconds
  rather than hours, days, etc.

The FCF automation acts as a design assistant for
the developer
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FCF Provides Configuration Assistant

• Publications
• Jules White et al, Using Filtered Cartesian
  Flattening Microrebooting to Build Enterprise
  Applications with Self-adaptive Healing, Software
  Engineering for Self-Adaptive Systems, 2009 (to
  appear)
• Jules White et al, Filtered Cartesian Flattening
  An Approximation Technique for Optimally
  Selecting Features while Adhering to Resource
  Constraints, First International Workshop on
  Analyses of Software Product Lines at the 12th
  International Software Product Line Conference,
  Limerick, Ireland, September 12, 2008
• Jules White et al, Selecting Highly Optimal
  Architectural Feature Sets with Filtered
  Cartesian Flattening, Journal of Software
  Systems Special Issue on Design Decisions
  Design Rationale in Software Architecture
  (Submitted)

The FCF automation acts as a design assistant for
the developer
32
Open Deployment Configuration Problems in CPS
Fault-tolerance Constraints
Real-time Scheduling Constraints
Resource Constraints
Cost Constraints
Problem 1
Problem 2
Problem 3
Problem 2 How can the software deployment to
processors in CPS be automated optimized at
scale? (Ongoing / Emerging Work)
33
Problem 2 Goal

• Goal Help designers automate the deployment of
  software to hardware processors to minimize
  hardware footprint network bandwidth
  consumption
• Impact Reduced cost, power consumption, weight,
  maintenance

Goal
Challenges
Solution
Evaluation
34
Goal Optimized Software Deployment
How do we minimize hardware infrastructure needed
to support software configuration?

• Goal
• Find a deployment that minimizes the number of
  required processors
• In planes, each 1lb of processing infrastructure
  adds 4lbs of supporting infrastructure as well as
  power, cost, maintenance complexity to the
  system
• Find a deployment of the software to hardware
  that minimizes network load

35
Problem 2 Challenge Roadmap

• Challenges
• Multiple constraint types
• Need for network bandwidth optimization
• Limitations of Existing Solutions

Goal
Challenges
Solution
Evaluation
36
Challenge 2.1 Mixed Constraint Bin-packing

• Most resource constrained deployment problems are
  modeled as bin-packing problems
• Bin-packing algorithms do not simultaneously
  handle real-time scheduling, co-location, bin
  restriction, resource constraints
• Existing Approaches
• Bin-packing for multiprocessor scheduling
  (real-time scheduling resource constraints)
• Bin-packing for memory constraints (resource
  constraints)
• CSP deployment models (exponential algorithms)

Bin-packing models CPUs as bins but cannot handle
all constraint types
Multiple Constraints
Network Opt.
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Challenge 2.2 Network Bandwidth Optimization
Multiple Constraints
Network Opt.
Bin-packing does not optimize for network
bandwidth usage

• Bin-packing algorithms do not perform network
  bandwidth optimization
• Existing Approaches
• Bin-packing with partitioning (subdivides
  components for network optimization different
  problem)
• Mapping Task-Graphs using Integer Programming
  (exponential algorithms)
• CSP deployment models (exponential algorithms)

Network
38
Problem 2 Solution Roadmap

• Solution Approach Combine hybrid heuristic
  bin-packing with particle swarm optimization
• Benefits Reduced weight, cost, power
  consumption, fuel consumption, etc. for CPS

Goal
Challenges
Solution
Evaluation
PSO Seeding
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2.1 Approach Black Box Bin-packing
New Bin-packing
An item fits into a bin if it is schedulable
using response time analysis or Liu Layland,
does not exceed resource limits, does not
violate constraints in the constraint network

• We developed a first-fit hybrid bin-packing
  constraint propagation algorithm
• Solution Approach
• fits is a pluggable black box function
• Items are sorted in increasing order according to
  the number of bins they will fit into
• Items are placed using a first-fit policy
• Remaining items are resorted after each item
  placement
• Publications
• Brian Dougherty, Jules White, Jaiganesh
  Balasubramanian, Chris Thompson, and Douglas C.
  Schmidt, Deployment Automation with BLITZ, 31st
  International Conference on Software Engineering,
  May 16-24, 2009 Vancouver, Canada

40
Bin-packer Constraint Extensibility
New Bin-packing
Fits can be modified to use domain-specific
constraints like it fits if the processor is 21
inches from the perimeter of the car

• Solution Approach
• fits is a pluggable black box function
• The bin-packing heuristic is based on
• A first-fit packing policy
• Sorting items based on the number of available
  bins they could fit into
• fits can be modified to incorporate new
  constraint types without affecting the core
  heuristics

41
2.2 Approach ? Swarm Intelligence
New Bin-packing

• Existing techniques werent designed to handle
  network bandwidth minimization a combination of
  real-time scheduling, memory, co-location
  constraints
• Solution Approach
• Leverage evolutionary algorithms to explore the
  design space for deployment plans that minimize
  bandwidth
• Competing deployment plans are evolved over a
  series of generations to find an optimized
  solution
• Best deployment plan(s) is presented to the
  developer

Particle Swarm Optimization (PSO)
Genetic Optimization
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Overview of Particle Swarm Optimization (PSO)
New Bin-packing
Particles Personal Best
Current Global Best
Possible Flight Path
Each particle is pulled towards a combination
of its personal best solution the global best
solution Velocity Calculation Formula

Particle
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Challenges Applying PSO
New Bin-packing

• Challenges Applying PSO
• How do you represent deployment plans as particle
  positions?
• How do you randomly generate valid initial
  deployment plans?
• How do you modify the particle flight plans to
  adhere to deployment constraints use heuristic
  information?
• PSO degenerates to random search for many
  problems!!!

44
Particle Position Approach Deployment Vectors
New Bin-packing
Node 1
Node 2
Deployment topologies are represented as
vectors Example 1, 2, 1
45
Permuting Deployment Plans
New Bin-packing
Design at Step i 1
Design at Step i
Node 1
Node 2
Node 1
Node 2
2, 1, 2
Velocity 1, -1, 0
1, 2, 2
46
Poor Results with Random Particle Generation
New Bin-packing
Relatively Sparse Valid Solution Population
X
Invalid Solutions Dominate Space

• Most of the solution space represents
  invalid solutions
• The evolutionary swarm intelligence algorithms
  degenerate into random searches with poor results!

47
Random Deployment Plan Approach Semi-random
Bin-packing
New Bin-packing
By starting most of the particles on valid
solution points, we can help focus the search to
valid points
48
Random Deployment Plan Generation
A Particle Represents A Deployment Topology

• We used our hybrid first-fit heuristic
  bin-packing algorithm with randomization to
  generate the initial particle positions
• Bin-packer is forced to deploy a random subset of
  the components first
• Bin-packer is forced to use a random ordering for
  those components
• Remaining components are deployed normally with
  the bin-packer

New Bin-packing
Particle Seeding
Semi-Randomized Bin-Packing
Particle Swarm
49
Constrained Flight Path Approach Packing Order
Positions
New Bin-packing
In the process of flying towards a more optimal
solution, a particle may fly out of the valid
solution space
50
Approach Add Guardrail to Solution Space
New Bin-packing
Our next approach was to try add a guardrail
to do a better job of restricting the search to
valid solutions
51
Guardrail Packing Order Vectors
New Bin-packing
Changed vectors to represent bin-packing
orderings Example pack C3, C1, then C2
3, 1, 2
Node 1
Node 2
Bin-Packer
Deployment Topology
52
Problem 1 Solution Roadmap

• Evaluation Mission avionics system data from
  Lockheed Martin comprising 41 processors, 41
  software components, 150 real-time tasks,
  14,000 component interactions

Goal
Challenges
Solution
Evaluation
53
Validation Results Mission Avionics
Solution Approach PSO
Research Adapter
Aeronautics Deployment Problem
Solution Approach Genetic Opt.

• Avionics Experiment Setup
• Mission avionics system data from Lockheed Martin
  comprising 41 processors, 41 software components,
  150 real-time tasks, 14,000 component
  interactions
• Attempted to minimize total processors used
• Attempted to minimize total network bandwidth
  consumed

54
Results on Avionics Example
6 processor reduction 13.9 X 107 Bytes 24
reduction in network bandwidth Solution found in
an average of 9s
55
Best Results with State as Packing Order
4 X increase in the number of optimal solutions
produced
Solutions Solved with Given Optimality
Results comparing solutions from 100 invocations
of the algorithms using deployment vectors vs.
packing order vectors
56
Evolutionary Deployment Automation
Results

• Developers can rapidly generate highly optimized
  deployment configurations
• The intelligent techniques improved the manually
  created design of a large-scale avionics system
• Improved deployment design reduces
• Hardware cost
• Power consumption
• Fuel consumption
• Network load

Evolutionary algorithms can improve manually
produced deployments are generate new ones
57
Deployment Ongoing Future Work
80 Core Processors
80 Embedded Processors
The hard deployment problems now, may become the
standard in a few years
5 years
Now

• To make chips more power efficient, multicore
  designs are being used
• In 2007, Intel produced a demonstration processor
  with 80 cores
• CELL processor have between 5 9 cores
• If we are writing applications for 80 core
  processors in 5 years, we are going to be facing
  similar deployment problems to what we have in
  CPS
• How do we adapt these techniques for multicore?

58
Ongoing Future Work
Real-time Scheduling Constraints
Resource Constraints
Cost Constraints
Problem 1
Problem 2
Problem 3
Problem 3 How can software deployments
configurations be optimized in tandem to meet
real-time scheduling, resource, fault-tolerance
constraints?
59
Configuration Ongoing Future Work
Unexpected good combination of deployment
configuration

• Currently, we separate the deployment
  configuration phases
• Extend techniques to optimize configuration
  deployment simultaneously similar to
  hardware/software co-design. e.g.
  configuration/deployment co-design
• Optimization is done via scoring of solutions.
  Extend techniques to allow developers to define
  scoring functions in models
• Extend techniques to include cache effects in
  optimization analysis

60
Summary of Publications, Tutorials, Talks

• Journal Publications
• Jules White, James Hill, Jeff Gray, Sumant Tambe,
  Douglas C. Schmidt, Anirrudha Gokhale, Improving
  Domain-specific Language Reuse through Software
  Product-line Configuration Techniques, IEEE
  Software Special Issue on Domain-Specific
  Languages Modeling (to appear)
• J. White, Jeff Gray, D. Schmidt, Constraint-based
  Model Weaving, IEEE Transactions on
  Aspect-Oriented Programming
• J. White, H. Strowd, D. Schmidt, Creating
  Self-healing Service Compositions with Feature
  Models Microrebooting, International Journal of
  Business Process Integration Management (to
  appear)
• J. White, D. Schmidt, Automating Deployment
  Planning with an Aspect Weaver, IET Software
  Special Issue on Domain-specific Modeling
  Languages for Aspect-Oriented Programming
• J. White, D. Schmidt, A. Nechypurenko, E.
  Wuchner, Model Intelligence an Approach to
  Modeling Guidance, UPGRADE Journal (to appear)
• J. White, D. Schmidt, E. Wuchner, A.
  Nechypurenko, Automatically Composing Reusable
  Software Components for Mobile Devices, Journal
  of the Brazilian Computer Society Special Issue
  on Software Reuse, SciELO Brasil, Volume 14,
  Number 1, pgs. 25-44, March, 2008
• J. White, D. Schmidt, A. Gokahle, Simplifying
  Autonomic Enterprise Java Bean Applications via
  Model-driven Engineering Simulation, Journal of
  Software Systems Modeling, Springer, Volume 7,
  Number 1, pgs. 3-23, May, 2007

First Author
Second Author
61
Summary of Publications, Tutorials, Talks
Conference Publications

1. Jules White, Douglas C. Schmidt, David Benavides,
   Pablo Trinidad, Antonio Ruiz-Cortez, Automated
   Diagnosis of Product-line Configuration Errors in
   Feature Models, Software Product Lines Conference
   (SPLC), 10pgs., September, 2008, Limmerick,
   Ireland (30 Acceptance Rate), Received the
   Best Paper Award
2. Brian Dougherty, Jules White, Jaiganesh
   Balasubramanian, Chris Thompson, and Douglas C.
   Schmidt, Deployment Automation with BLITZ, 31st
   International Conference on Software Engineering,
   May 16-24, 2009 Vancouver, Canada
3. Brian Dougherty, Jules White, Chris Thompson,
   Douglas C. Schmidt, Automating Hardware
   Software Evolution Analysis, 16th Annual IEEE
   International Conference Workshop on the
   Engineering of Computer Based Systems (ECBS),
   April 13-16, 2009 San Francisco, CA USA.
4. J. White D. Schmidt, Model-Driven Product-Line
   Architectures for Mobile Devices, Proceedings of
   the 17th Conference of the International
   Federation of Automatic Control, Seoul, Korea,
   July 6-11, 2008.
5. J. White D. Schmidt, Automated Configuration of
   Component-based Distributed Real-time Embedded
   Systems from Feature Models, Proceedings of the
   17th Conference of the International Federation
   of Automatic Control, Seoul, Korea, July 6-11,
   2008.
6. J. White, K. Czarnecki, D. Schmidt, G. Lenz, C.
   Wienands, E. Wuchner, Ludger Fiege, Automated
   Model-based Configuration of Enterprise Java
   Applications, EDOC 2007, October, 2007,
   Annapolis, Maryland

• J. White, D. Schmidt, E. Wuchner, A.
  Nechypurenko, Optimizing Automating
  Product-Line Variant Selection for Mobile
  Devices, 11th Annual Software Product Line
  Conference (SPLC), Sept. 10-14, 2007, Kyoto,
  Japan
• A. Nechypurenko, E. Wuchner, J. White, D.
  Schmidt, Application of Aspect-based Modeling
  Weaving for Complexity Reduction in the
  Development of Automotive Distributed Realtime
  Embedded Systems, Proceedings of the Sixth
  International Conference on Aspect-Oriented
  Software Development, Vancouver, British
  Columbia, March 12-16, 2007.
• J. White D. Schmidt, Reducing Enterprise
  Product Line Architecture Deployment Costs via
  Model-Driven Deployment Configuration Testing,
  13th Annual IEEE International Conference
  Workshop on the Engineering of Computer Based
  Systems (ECBS '06), March 27th-30th, 2006,
  Potsdam, Germany.
• J. White, D. Schmidt, A. Gokahle, Simplifying
  Autonomic Enterprise Java Bean Applications via
  Model-driven Development a Case Study,
  Proceedings of MODELS 2005, ACM/IEEE 8th
  International Conference on Model Driven
  Engineering Languages Systems, Half Moon
  Resort, Montego Bay, Jamaica, October 5-7, 2005.
  (Selected as a best paper)
• J. White, D. Schmidt, A. Gokahle, The J3
  Process for Building Autonomic Enterprise Java
  Bean Systems, Proceedings of the International
  Conference on Autonomic Computing (ICAC 2005),
  Seattle, WA, June 2005 (short paper).
• Jules White, Boris Kolpackov, Balachandran
  Natarajan, and Douglas C. Schmidt, Reducing Code
  Complexity With Vocabulary-Specific XML Language
  Bindings,Proceedings of the 43nd ACM Southeastern
  Conference, 7pgs., Atlanta, GA, March 2005.

First Author
Second Author
62
Summary of Publications, Tutorials, Talks

• Conference Best Paper Award
• Jules White, Douglas C. Schmidt, David
  Benavides, Pablo Trinidad, Antonio
  Ruiz-Cortez, Automated Diagnosis of Product-line
  Configuration Errors in Feature Models, Software
  Product Lines Conference (SPLC), 10pgs.,
  September, 2008, Limmerick, Ireland
• Book Chapters
• J. White, A. Nechypurenko, E. Wuchner, D.
  Schmidt, Reducing the Complexity of Designing
  Optimizing Large-scale Systems by Integrating
  Constraint Solvers with Graphical Modeling Tools,
  Designing Software-Intensive Systems Methods
  Principles, edited by Dr. Pierre F. Tiako,
  Langston University, Oklahoma, USA, (to appear)
• Jules White, Brian Doughtery, Harrison Strowd,
  Douglas C. Schmidt, Using Filtered Cartesian
  Flattening Microrebooting to Build Enterprise
  Applications with Self-adaptive Healing, Software
  Engineering for Self-Adaptive Systems, edited by
  Betty H. C. Cheng, Rogerio de Lemos, Holger
  Giese, Paola Inverardi, Jeff Magee (to appear)

• Workshop Publications
• Jules White, Douglas C. Schmidt, Filtered
  Cartesian Flattening An Approximation Technique
  for Optimally Selecting Features while Adhering
  to Resource Constraints, First International
  Workshop on Analyses of Software Product Lines at
  the 12th International Software Product Line
  Conference, Limerick, Ireland, September 12, 2008
• James Hill, Jules White, Sean Eade, Douglas C.
  Schmidt, Towards a Solution for Synchronizing
  Disparate Models of Ultra-Large-Scale
  Systems, Proceedings of the 2nd International
  Workshop on Ultra-Large-Scale Software-Intensive
  Systems at the 30th IEEE/ACM International
  Conference on Software Engineering, May 10-11,
  2008, Leipzig, Germany.
• J. White, D. Schmidt, Sean Mulligan, The Generic
  Eclipse Modeling System, Model-Driven Development
  Tool Implementer's Forum, TOOLS '07, June, 2007,
  Zurich Switzerland
• A. Nechypurenko, J. White, E. Wuchner, D.
  Schmidt, Applying Model Intelligence Frameworks
  for Deployment Problem in Real-time Embedded
  Systems, Proceedings of MARTES Modeling
  Analysis of Real-Time Embedded Systems to be
  held on October 2, 2006 in Genova, Italy in
  conjunction with the 9th International Conference
  on Model Driven Engineering Languages Systems,
  MoDELS/UML 2006.
• J. White, A. Nechypurenko, E. Wuchner, D.
  Schmidt, Intelligence Frameworks for Assisting
  Modelers in Combinatorically Challenging Domains,
  Proceedings of the Workshop on Generative
  Programming Component Engineering for QoS
  Provisioning in Distributed Systems, October 23,
  2006, Portland, Oregon.
• J. White D. Schmidt, Simplifying the
  Development of Product-line Customization Tools
  via Model Driven Development, MODELS 2005
  workshop on MDD for Software Product-lines Fact
  or Fiction?, October 2, 2005, Jamaica.

• Submitted Papers
• Jules White, Brian Doughtery, Douglas C.
  Schmidt, ASCENT An Algorithmic Technique for
  Designing Hardware Software in Tandem, IEEE
  Transactions on Software Engineering Special
  Issue on Search-based Software Engineering
• Jules White, Brian Doughtery, Douglas C.
  Schmidt, Selecting Highly Optimal Architectural
  Feature Sets with Filtered Cartesian Flattening,
  Journal of Software Systems Special Issue on
  Design Decisions Design Rationale in Software
  Architecture

63
Other Impact

• Generic Eclipse Modeling System (GEMS)
• The Generic Eclipse Modeling System (GEMS) has
  been incorporated into the Eclipse Foundation
  is distributed by over 45 mirrors in North
  America, Europe, Asia, South America
• Articles on GEMS have been written in both
  English
  German published in key trade
  
  publications, such as Eclipse magazine
  
  OBJEKT Spektrum
• I have also presented tutorials on GEMS at
  
  OOPSLA, ICSE, OOP, MODELS
• Teaching as Research Grant
• Received a Vanderbilt Teaching as
  
  Research grant to
  introduce my research
  
  on modeling topics to undergraduates
• Undergraduate Graduate Involvement
• Mentored 4 undergraduate students on GEMS
  research
  projects that resulted in 4 publications
  
  (including a journal paper a book chapter)
• Graduate research projects using GEMS at UAB,
  Vanderbilt, University of Innsbruck

64
Other Impact

• Eclipse Model OptimizatiON (MOON) Project
• I am currently co-leading a project with Patrick
  Albert, a co-founder of ILOG, to generalize these
  modeling guidance techniques to any Eclipse
  Modeling Framework (EMF) model
• ILOG is the leading producer of rule engine,
  constraint solver, configuration software
• Research work for MOON is going to be in
  collaboration with Jean Bezivin Frederic
  Jouault at the University of Nantes

65
Concluding Remarks

• Intelligent search-based automation techniques
  can help designers find solutions to complex CPS
  design problems.
• Many of these real-world design problems require
  new theoretical tooling advances to work at
  scale with CPS constraint types
• Scale is major challenge in current future
  systems, but automation can help significantly
• Good tooling is critical for transitioning the
  theoretical advances to CPS system designers
  teaching the techniques to students
• The ASCENT Design Studio is a collection of
  design tools that includes my deployment
  configuration research
• Two undergraduates one graduate student are
  also involved in its development
• http//code.google.com/p/ascent-design-studio/
• GEMS is available through the Eclipse Foundation
  at
• http//www.eclipse.org/gmt/gems

66
Creating Sets Tree Slicing/Dicing
Slice
Slice
Set 1
Set 3
Set 2

• We traverse the feature model tree split it
  into subtrees
• Where there is a point of variability (XOR,
  Optional, etc.) that does not have any ancestor
  points of variability
• Each subtree will eventually produce one set for
  the MMKP problem

67
Software/Hardware Design Productivity Gap
Complexity Current CPS Tools Can Handle
Emerging CPS Complexity

• The 1999 International Technology Roadmap for
  Semiconductors estimated that design complexity
  is increasing at 58 per year, while designer
  productivity is trailing at 21
• The difference between these is the design
  productivity gap
• Something similar is happening in software
• e.g., BMW estimates that software development now
  accounts for 20-28 of the cost of a car
• We need scalable design tools techniques to
  close this gap
• We need tools that can do smart analysis
  optimization for designers