1. Configuring the Cloud
Automated Planning as a Semantic
Technology
2010 Semantic Technology
Conference
Blazej Bulka, PhD Stuart Charlton
Clark & Parsia, LLC Elastra
blazej@clarkparsia.com stuartc@elastra.com
www.clarkparsia.com www.elastra.com
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2. Who we are?

Clark & Parsia is a semantic 
Elastra is a cloud computing
software startup software startup

Offices in DC and Cambridge, 
Offices in San Francisco, CA
MA 
Co-Funded by Amazon.com, Bay

Software products for end-user Partners, and Hummer Winblad
and OEM use 
Provides software and services for

Provides software development helping organizations migrate and
and integration services manage their applications in private
and public clouds

Specializing in Semantic Web,
web services, and advanced AI 
Elastra Cloud Server available for
technologies for federal and Amazon Web Services and VMWare
enterprise customers

3. Outline

Introduction to automated planning

Examples of planning systems

Hierarchical planning

Case Study: Planning in Cloud Computing

Semantic technologies in planning:
HotPlanner from Clark & Parsia

Conclusion
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4. Introduction to automated planning
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5. What is automated planning?

Automated process to determine which actions
need to be taken to achieve a desired goal

Current state of the world

Description of actions
Planning Required actions

Goals and constraints system (plan)

Multiple applications of produced plans
− Software execution
− Human execution
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− Assistance in solving a problem

6. Simple example
Installing new software package

Current state of the world: Description of installed software
packages and their dependencies

Available actions:
− Retrieve list of packages available for install from a server
− Retrieve package dependencies
− Download software package
− Install software package

Goal: Install software package X

Plan – sequence of actions specifying which packages to
download and install, and in which order
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7. Domain-dependent planning

Solves one type of problem well

Specialized algorithm and data representation
(the designer encoded the structure of the
problem and solution in the code)

Small modifications to problem (e.g., new
kinds of dependencies between software
packages) = modifications to the planning
system

Larger modifications to problem = rewriting 7
significant portions of the planning system

8. Domain-independent planning

Solves problems in multiple, entirely different
domains (e.g., software management, truck
routing, space mission control)

Domain (actions, state of the world, goals) have
to be specified in a way understandable to the
planning system

Generic planning algorithm

Modifications to planning problem (small or
large) – modifications to the domain
specification = No change of planning system's 8
code

9. What makes a plan good?

Depends on the application

Typical quality metrics
− Plan length (number of actions)
− Makespan (time to execute the plan)
− Plan cost (every action has a cost associated
with it)
− Multi-objective metrics
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10. Examples of planning systems
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11. Space Exploration:
Deep Space Network
Images: nmp.nasa.gov/ds1/

Deep Space 1 (DS1)
− First ever close pictures of a comet
(Borelly in 1999)
− Part of Deep Space Network
− Proof-of-concept for later systems

Benefits
− Spacecraft autonomy
− Commands = goals
− Plan verification
− Execution monitoring
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12. Earth Observing-1 Mission (EO-1)
Images: eo1.gsfc.nasa.gov

Proof-of-concept for autonomous
sensor web (incl. satellites, buoys
etc.)

Autonomous analysis of data

Feature detection (e.g., fire) and
decide to investigate on its own

Collaboration among multiple
satellites

Plan activities: transmissions, power
usage, set-up, camera usage

Dramatic increase in amount of
scientifically usable data (1000 x ?)
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13. Mars Exploration Rovers and
MAPGEN
Images: www.nasa.gov

Autonomous execution of plans
prepared on Earth

Very limited planning on Mars

MAPGEN – computer planning system
assisting human planners

Can produce explanations of
dependencies

Successor of MAPGEN is EUROPA
(Extendable Uniform Remote
Operations Planning Architecture)
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14. Xerox RMP

Planning system for Rack Mounted Printing prototype
− System with multiple parallel printing engines
(for performance and fault tolerance)
− Sheet path planning (and re-planning in case of
failure)
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Images: Do, Ruml, and Zhou (ICAPS 2008 Proceedings)

15. Planning in games

Strategy games and first-person shooters
− Killzone 2 (Game of the Year 2009 in
Shooter category)

500 plans per second
− F.E.A.R. and Condemned: Criminal Origins
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16. 16

17. 17

18. Siemens Tecnomatix 9
CAM-oriented planning tasks
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19. Hierarchical planning
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20. Hierarchical planning

HTN (Hierarchical Task Network) planning

Hierarchy of actions/goals

Hint on the problem structure from the domain expert

Prevents reinventing the wheel by the planning system

Planner can first solve high-level goals and then refine
lower-level ones

Significant improvement in performance

Designed templates of desired solutions (e.g., plans
that adhere to internal policies)
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21. Example HTN Plan
Goal: Have package X
Install package X
Download Satisfy
Download Install
package list dependencies
X X
for X
Have package Have package
Y Z
Install Already
package Y installed
Satisfy
Download Install
dependencies
Y Y
for Y
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22. Case Study: Configuring the Cloud
With Elastra Enterprise Cloud Server
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23. Elastra Enterprise Cloud Server
Monitoring & Capacity Application-Level Automation OS-Level Automation
Event Virtualization
Correlation Managers
Automated Application Discover
Systems Manage
Publish Deployment & Scale Out Integrate
Subscribe Planning & Orchestration Configuration
Agents
Monitoring
Systems
Integrate
Public Cloud APIs
3 Party IT Systems
rd
PDP Federated Identity & Access Control PEP
• Configure, Deploy, and Scale Multi-Tier Applications
» Commercial and/or open source software
• Manage Applications Across a Hybrid Cloud
» VMWare, Amazon Web Services, others
• Portable & Consistent Cloud Application Management

24. The Elastic Modeling Languages:
OWL Ontologies for Cloud Computing
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25. Meeting the Challenges of IT Automation:
How can these two servers communicate?
Possible areas of problems:
• Security
» Bad credentials
• Server Configuration
» Wrong IP or Port
» Bad setup to listen or call
• Network Configuration
» Wrong duplex
» Bad DNS or DHCP
• Firewall Configuration
» Ports or protocols not open

26. How do we usually automate a configuration?
• Scripts & Runbooks
• In Situation X, do this
• In Situation Y, do this
• In Situation Y, but exception Z, do this
• ….
• Problems: Context, Variability, Timing & Transitions
• Scripts require you to explicitly write out each control
transition, assume you know in advance the
one right way to do things
• That’s fine in the small; in the large, it gets complicated.
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27. Elastra Next Generation Automation Engine:
Elastra Plan Composer
Strategy Examples:
HTN Strategies Configure Full System
Scale-Out a System
Application Recover a Database
& Deployment
Data HTN Tactics
Search for Next Tactics :
(Background Set Firewall Rules
HTN Atomic Restore Filesystem
Individuals) Actions Install & Start Service
Provision Network
Elastic Jena + SPARQL
Modeling
Languages Search for Atomic Bindings:
Clark & Parsia HotPlanner Configuration Agent Callout
(OWL v2
Ontology) Cloud API Callout
Clark & Parsia Pellet Virtualization Manager Callout
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28. Elastra’s Success Results with HotPlanner
• Reduced, More Maintainable Codebase
» Custom SPARQL and Java Code to interpret RDF, vs.
HotPlanner’s Domain-Specific Language for HTN
» Up to a 3x reduction in code length for various modules
• Performance
» Ability to generate a plan in under a minute for a 20 server, 20
component, multi-tier system design
» Typical planning time: 3 to 5 seconds, up to 15 for complex
» Near-equivalent to classic manual SPARQL-based analyzer
• Visibility and Modifiability of Plan
» Plans are just OWL-S processes, easy to parse, render to GUI,
and/or modify before execution
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29. Semantic Technologies in Planning
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30. Semantic technologies in planning

Semantic technologies have not been used
together with planning frequently so far
− Promising opportunity

Modern semantic technologies (RDF and
OWL) are relatively new compared to planning

Planning finally became usable in real-world
settings
− The two areas can contribute to each other
− Many places where one area's weakness is
the other's strength
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31. Expressing planning domain

Planners depend on accurate formal description of planning
domain
− Language to describe the current state of the world
− Goals
− Actions

Parameters (e.g., install package X)

Preconditions (when an action may be executed)

Effects (what changes in the world when the action is executed)

Hierarchy

Ideal solution
− Expressive (e.g., describing quantities)
− Keep computational complexity under control
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32. Classical planning formalisms are
inadequate

Traditional formalisms
− STRIPS, PDDL (Planning Domain Definition
Language)
− Limited expressivity
Expressing complex domains difficult
− Support for hierarchical planning is sparse
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33. Representing planning knowledge in
OWL

Describing the current state of the world in
RDF and OWL
− Expressive language
− Designed with computational complexity in
mind (e.g., various profiles EL, RL, QL)
− Can be used to infer information about the
current state of the world that is not explicitly
specified
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34. Describing actions and hierarchies
based on OWL-S

OWL-S is designed to specify descriptions of
Web services

Can be used to specify any action in
Hierarchical Planning by using owls:Process
− Composition of actions (hierarchy)
− Preconditions
− Effects
− Parameters of actions
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35. Challenges

OWL ontologies are designed to represent
static data

Planning includes reasoning about changes
− Describing effects of action (some axioms
become true, other become false)
− Identifying invariants that are preserved
− Matching effects of one actions with
preconditions of others
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36. HotPlanner

Clark & Parsia's response to these challenges

Used in production systems

Uses Pellet OWL reasoner for state
knowledge base management, reasoning and
query answering
– Can plan and optimize for multiple objectives
– Extension to OWL-S for actions
– Scheduling and planning for parallel task
execution

For more information go to http://clarkparsia.com/planner36

37. Conclusions

Planning is a proven technology for task
automation, decision support, autonomous
systems

Semantic technologies and planning have a
lot to offer to each other

Automated planning is quickly growing in use
for Cloud Computing automation
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38. Thank you for your attention!
Questions?
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