University of Colorado at Denver 2006 Math Clinic Dynamic Scheduling of Prioritized Tasks

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University of Colorado at Denver2006 Math
ClinicDynamic Scheduling of Prioritized Tasks

• Spring 2006

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Agenda

• Raytheon initiatives and interests
• Satellite Scheduling
• Dynamic Tasking
• Math Clinic problem statement review and
  expectations
• QA

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Raytheon Initiatives and Interests
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More Than 30 Years of Space Experience
Mission Management
Command and Control
Ground Terminals
Data Processing
Network Management
Complete ground capabilities for space systems
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Scheduling Research Interests

• Scheduling Topic
• Near Real-time Dynamic Re-Tasking Systems
• Dynamic scheduling of prioritized tasks
• Potential Scheduling Approaches
• Meta Heuristics
• Parallel/Distributed Scheduling
• Continuous Optimization
• Scheduling via Data Clustering
• Scheduling via Predictive Analysis
• Automated Optimizer Tuning (feedback loops)
• Clinic should take a fresh approach, and leverage
  the 2005 Math Clinic work where possible.

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ExampleMission Scheduling of a Notional
Commercial Remote Sensing Satellite System
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Satellites
Remote Sensing
Scientific Discovery
Intelligence
Communications
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Introduction

• Satellite mission scheduling is the process of
  assigning resources to tasks, over time.
• Resource sensor, receiver, thruster, gyro,
  antenna, processor
• Task collection, downlink, orbit adjustment,
  data processing function
• Payload scheduling attempts to optimally sequence
  a collection of related activities on a payload
  resource (e.g. sensor), to maximize the efficient
  use of this expensive, oversubscribed resource

Tasks to be Scheduled
The Schedule
Resource
Scheduler/Optimizer
TN
T0
Time
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Modeling

• Basics
• Tasks
• Activities to be performed (image collection,
  orbit adjust)
• Occupy discrete time on a schedule (start time
  duration)
• Resources
• Objects that perform activities (sensors,
  thrusters)
• Constraints
• Restrict when, where and how tasks are scheduled
• Tasking (input parameters preferred times,
  minimum quality)
• Visibility (access to target pointing/attitude
  time)
• Availability (resource availability or capacity)
• Temporal (time order relationships between
  tasks)
• Physical (power restrictions, thermal exposure)

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Tasks

• Task
• Id
• Collection Interval
• Earliest latest collection times
• Priority (numeric)
• Target
• Cartesian coordinate system
• Duration (sec)
• Strategy

• Collection Strategies
• Simple
• A single collection for a single target
• Monitor
• Periodic (revisit) collections over a defined
  interval of time for a single target
• Initial task model
• Iterate over the course of the semester, if
  necessary

• Complexity
• 1000?3000 potential collections per area of
  interest
• A mix of standing tasks and on-demand requests
• 1?N on-demand requests, from M users, that arrive
  according to some probabilistic distribution
  during the course of an executing schedule

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Visibility Constraint to Target

• Shape size of orbit determine when and how
  often a point on the earth is visible from the
  satellite
• Satellite agility also plays a role in target
  access times
• Role (smaller)
• Role Pitch
• Role, Pitch Yaw (larger)
• Collection activity can only be scheduled during
  a Window of Opportunity

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Slewing

• Slewing is the act of changing a satellites
  attitude (pointing), typically performed when
  transitioning from the collection of one target
  to another.
• Generally slewing introduces idle time for the
  sensor
• Consider a shortest path problem
• Visiting targets that are close together ?
  shorter slews ? less idle time
• When scheduling, slewing is only one
  consideration. One must balance a shortest
  (bore-sight) path with other criteria, such as
  task priority, collection strategy and collection
  quality.

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Quality Constraints

• There can be a relationship between geometries
  and collection quality, which in turn affects
  collection duration.
• Angle between satellite and target
• Angle between sun and target

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Dynamic Tasking
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Traditional TCPED Cycle
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New TCPED Cycle with Dynamic Tasking
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Dynamic Tasking Description

• Interpretation is a new decision function
  executed in near real-time
• Can be performed anywhere (processing facility,
  in the field, etc)
• Can be performed by human or machine
• Results in a decision versus analysis and
  reporting products (exploitation)
• Causes near real-time redirection of assets
  (dynamic re-tasking)
• Implications to Payload Scheduling
• Larger input data size due to many more sources
• Non-deterministic arrival of tasks (on-demand
  tasking)
• Much shorter response times (rapid rescheduling)

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Example of Near Real-time Dynamic Tasking Systems
Notional Surveillance Attack on enemy tanks
(timeline does not reflect required, designed or
actual capabilities)
Dynamic Re-tasking
Satellite Finds Tanks 000030
Satellite Tracks Tanks 000045
Data Passed to commander 000200
Commander Tasks Aircraft 000300
Initial Satellite Tasking 000000
Satellite Re-Tracks Tanks 000100
Enemy Tanks Destroyed 001300
Kill Chain 13 minutes from first observation to
bombs on target
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Example of Near Real-time Dynamic Tasking Systems
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Problem Description Review and Expectations
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Problem Description

• Schedule tasks for a single asset (could use
  commercial remote sensing satellite as an example
  test case)
• New task requests can arrive at any time
  (non-deterministic)
• Most task requests are due in near real-time
• All tasks have a fixed duration
• Assume that there is access for all tasks over
  the scheduling interval (1 hour access interval)
• Assume that there is a transition cost between
  any two tasks
• The time to transition from one task to another
  is a function of the distance between the task
• Assume only one task is performed at a time

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Problem Description Details

• Tasking
• Id (alpha-numeric)
• Activity Interval
• Earliest Start from 5 sec - 30 min out, from task
  arrival
• Latest End from 10 sec - 1 hour out, from task
  arrival
• Earliest Start ? Preferred Start ? Preferred End
  ? Latest End
• To schedule outside the Preferred interval is a
  soft constraint violation with a penalty that
  increases linearly the farther you are from the
  Preferred interval. Scheduling outside the
  Earliest/Latest interval is a hard constraint
  that cannot be violated.
• Priority (0 - 100, where 100 is the lowest
  priority 0 is the highest)
• location (non-uniformly distributed - clusters)
• Duration (fixed at 5 seconds for all tasks)
• Strategy (simple or monitor)
• Activity Strategies
• A mix of more Simple and fewer Monitor
• Scheduling Goals
• Activities will not miss their due dates
• Higher priority tasks take precedence over lower
  priority tasks
• A densely packed schedule
• Build an existing schedule and then reschedule
  based on dynamic re-task requests

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Problem Description Details

• Scheduling Objectives (samples)
• Maximize the sum of the priorities AND
• Minimize transition costs AND
• Maximize -utilization of schedule
• Priorities are not cumulative i.e. one priority
  10 task is more important than ten priority 100
  tasks.
• Characterizations of scheduling performance
  (samples)
• Average runtime vs the number of tasks scheduled
• Average wait time from task input to task
  execution
• -satisfaction of tasks (i.e. scheduled vs
  considered)
• Clinic should identify appropriate
  characterizations. Focus on performance from a
  high volume, dynamic user input perspective.

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Expectations Deliverables

• Literature search (annotated bibliography)
• Chosen algorithm
• Justification
• Description
• Performance/Characterization results
• Prototype (Matlab, Java, C/C)
• Point of Contact
• Mick Stahlberg
• mjstahlberg_at_raytheon.com
• 720.858.4204

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Questions Answers