CPS Scheduling Policy Design with Utility and Stochastic

Execution*Chris Gill

Associate ProfessorDepartment of Computer Science and Engineering

Washington University, St. Louis, MO, USAcdgill@cse.wustl.edu

Georgia Tech CPS Summer SchoolAtlanta, GA, June 23-25, 2010

*Research supported in part by NSF grants CNS-0716764 (Cybertrust) and CCF-0448562 (CAREER) and driven by numerous

contributions from post-doctoral student Robert Glaubius; doctoral student Terry Tidwell; undergraduate students Braden Sidoti, David Pilla, Justin Meden, Carter Bass, Eli Lasker, Micah

Wylde, and Cameron Cross; and Prof. William D. Smart

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Washington University in St. Louis

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Dept. of Computer Science and Engineering

24 faculty members and 70 Ph.D. students working in: real-time and embedded systems, robotics, graphics,

computer vision, HCI, AI, bioinformatics, networking, high-performance architectures, chip multi-processors, mobile computing, sensor networks, optimization

PhD students are fully funded, and we emphasize individual mentorship and interdisciplinary work

Recent graduates are on faculty at U. Mass, UT-Austin, Rochester, RIT, CMU, Michigan St., and UNC-Charlotte

Graduate study application deadline for Fall 2011 is January 15: http://www.cse.wustl.edu

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Why Pursue CPS Research?

Systems are increasingly being designed to interact with the physical world

This trend offers compelling new research challenges that motivate our work

Consider for example the domain of mobile robotics

my name is

LewisMedia and Machines Laboratory

Washington University in St. Louis

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Why is This Work CPS Research?

As in many other systems, resources must be shared among competing tasks

Fail-safe modes may reduce consequences of resource-induced timing failures, but precise scheduling matters

The physical properties of some resources motivate new models and techniques

my name is

LewisMedia and Machines Laboratory

Washington University in St. Louis

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Which Problem Features are Interesting?

Sharing e.g., a camera between navigation and image capture tasks

(1) in general doesn’t allow efficient preemption

(2) involves stochastically distributed durations

Also important in general:(3) scalability (many tasks sharing such a resource);(4) task utility/availability

LewisMedia and Machines Laboratory

Washington University in St. Louis

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System Model Assumptions We model time as being discrete

» E.g., based on some multiple of the Linux jiffy» States and scheduling decisions align with those

quanta

Separate tasks require a shared resource» Access is mutually exclusive (a task binds the

resource)» Binding durations are independent and non-

preemptive» Tasks’ duration distributions are known (or learned

[1])» Each task is always available to run (relaxed in part

III)

Goal: precise resource allocation among tasks [5]» E.g., 2:1 utilization share targets for tasks A vs. B» Need a deterministic scheduling policy (decides

which task gets the resource when) that best fits that goal

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Part I

Utilization State Spaces and

Markov Decision Processes

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Towards Optimal Policies

A Markov decision process (MDP) is a 4-tuple (X,A,C,T) that matches our system model well:X: a finite set of states (e.g., utilizations of 8 vs. 17

quanta)A: the set of actions (giving resource to a particular task)C: cost function for taking an action in a stateT: transition function (probability of moving from one

state to another state based on the action chosen)

Solving the MDP gives a policy that maps each state to an action to minimize long term expected costs

However, to do that we need a finite set of states

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Share Aware Scheduling

A system state: cumulative resource usage of each task

Dispatching a task moves the system stochastically through the state space according to that task’s duration

(8,17)

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Share Aware Scheduling

Utilization target induces a ray {u:0} through the state space

Encode each state’s “goodness” (relative to the share) as a cost

Require that costs grow with distance from utilization ray

u

u=(1/3,2/3)

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Transition Structure

Transitions are state-independent

I.e., relative distribution over successor states is the same in each state

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Cost Structure

States along same line parallel to the utilization ray have equal cost

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Equivalence Classes

Transition and cost structure thus induce equivalence classes

Equivalent states have the same optimal long-term cost and policy!

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Periodicity

Periodic structure allows us to represent each equivalence class with a single exemplar [4]

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Wrapping the State Model

Remove all but one exemplar from each equivalence class

Actions and costs remain unchanged

Remap any dangling transitions (to removed states) to the corresponding exemplar

(0,0)

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c(x)=

c(x)=

Truncating the State Model

Inexpensive states are nearer the utilization target

Good policies should keep costs small

Can truncate the state space by bounding sizes of costs considered

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Bounding the State Model

Map any dangling transitions produced by truncation, to a high-cost absorbing state

This guarantees that we will be able to find bounded-cost policies if they exist

Bounded costs also guarantee bounded deviation from the resource share (precision)

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A Scheduling Policy Design Approach

Iteratively increase the bounds and re-solve the resulting MDP

As the bounds increase, the bounded model solution converges towards the optimal wrapped model policy

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Automating Model Discovery

ESPI: Expanding State Policy Iteration [3]

1. Start with a policy that only reaches finitely many states from (0,…,0).

E.g., always run the most underutilized task.

2. Enumerate enough states to evaluate and improve that policy

3. If policy can not be improved, stop4. Otherwise, repeat from (2) with newly improved

policy

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Policy Evaluation Envelope

Enumerate states reachable from the initial state

Explore state space breadth-first under the current policy, starting from the initial state(0,0)

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Policy Improvement Envelope

Consider alternative actions

Close under the current policy using breadth-first expansion

Evaluate and improve the policy within this envelope

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ESPI Termination

As long as the initial policy has finite closure, each ESPI iteration terminates (this is satisfied by starting with the heuristic policy that always runs the most underutilized task)

Policy strictly improves at each iteration

Anecdotally, ESPI terminates on all of the task scheduling MDPs to which we have applied it

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Comparing Design Methods

Policy performance is shown normalized and centered on the ESPI solution data

Larger bounded state models yield the ESPI solution

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Part II

Scalabilityand

Approximation Techniques

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What About Scalability?

MDP representation allows consistent approximation of the optimal scheduling policy

Empirically, bounded model and ESPI solutions appear to be near-optimal

However, approach scales exponentially in number of tasks so while it may be good for (e.g.) sharing an actuator, it won’t apply directly to larger task sets

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What our Policies Say about Scalability

To overcome limitations of MDP based approach, we focus attention on a restricted class of appropriate scheduling policies

Examining the policies produced by the MDP based approach gives insights into choosing (and into parameterizing) appropriate policies [2]

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Two-task MDP Policy

Scheduling policies induce a partition on a 2-D state space with boundary parallel to the share target

Establish a decision offset d to identify the partition boundary

Sufficient in 2-D, but what about in higher dimensions?

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Time Horizons Suggest a Generalization

H0 H1 H2 H3 H4

Ht={x : x1+x2+…+xn=t}

H0

H1

H2

(0,0) (2,0,0)

(0,2,0)

(0,0,2)

u

u

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Three-task MDP Policy

Action partitions meet along a decision ray that is parallel to the utilization ray

Action partitions are roughly cone-shaped

t =10 t =20 t =30

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Parameterizing a Partition

Specify a decision offset at the intersection of partitions

Anchor action vectors at the decision offset to approximate partitions

A conic policy selects the action vector best aligned with the displacement between the query state and the decision offset

a1a2

a3

x

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Conic Policy Parameters

Decision offset dAction vectors a1,a2,…,an

Sufficient to partition each time horizon into n regions

Allows good policy parameters to be found through local search

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Comparing Policies

Policy found by ESPI (for small numbers of tasks)πESPI(x) – chooses action at state x per solved MDP

Simple heuristics (for all numbers of tasks)πunderused(x) – runs the most underutilized task

πgreedy(x) – minimizes immediate cost from state x

Conic approach (for all numbers of tasks)πconic(x) – selects action with best aligned action vector

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Policy Comparison on a 4 Task Problem

Task durations: random histograms over [2,32]100 iterations of Monte Carlo conic parameter

searchESPI outperforms, conic eventually approximates

well

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Policy Comparison on a Ten Task Problem

Repeated the same experiment for 10 tasksESPI is omitted (intractable here)Conic outperforms greedy & underutilized

heuristics

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Comparison with Varying #s of Tasks

100 independent problems for each # (avg, 95% conf)

ESPI only tractable through all 2 and 3 task casesConic approximates ESPI, then outperforms

others

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Part III

Expanding our Notions of Utility and Availability

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Time-Utility Functions

Previously, utility was proximity to utilization target; now we let tasks’ utility and job availability* varytime-utility function (TUF) name

period boundarytermination time

termination timeperiod boundary

* Availability variable qi is defined over {0,1}; {0, tmi/pi }; or {0,1} tmi/pi

Time

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Utility × Execution Utility Density

A task’s time-utility function and its execution time distribution (e.g., Di(1) = Di(2) = 50%) give a distribution of utility for scheduling the task

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Actions and State Space StructureState space can be more compact here than in parts I and

II: dimensions are task availability (e.g., over (q1, q2)) vs. time

Can wrap the state space over the hyper-period of all tasks (e.g., D1(1) = D2(1) = 1; tm1 = p1 = 4; tm2 = p2 = 2)

Scheduling actions induce a transition structure over states (e.g., idle action = do nothing; action i = run task i)

action 2action 1idle action

time timetime

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Reachable States, Successors, RewardsStates with the same task availability and the

same relative position within the hyper-period have the same successor state and reward distributions reachable states

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Evaluation

(target sensitive)

(linear drop)

(downward step)Different TUF shapes are useful

to characterize tasks’ utilities (e.g., deadline-driven, work-ahead, jitter-sensitive cases)

We chose three representative shapes, and randomized their key parameters: ui, tmi, cpi

(we also randomized 80/20 task

load parameters: li, thi, wi)

utilitybounds

criticalpoints

terminationtimes

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How Much Better is Optimal Scheduling?

Greedy (Generic Benefit*) vs. Optimal (MDP) Utility Accrual

* P. Li, PhD Dissertation, VA Tech, 2004

2 tasks 3 tasks

5 tasks4 tasks

TUF nuances matter: e.g., work conserving approach degrades target sensitive policy

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Divergence Increases with # of Tasks

Note we can solve 5 task MDPs for periodic task sets (smaller state spaces; scalability is an ongoing issue)

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Conclusions

We have developed new techniques for designing non-preemptive scheduling policies for tasks with stochastic resource usage durations

MDP-based methods are effective for 2 or 3 task utilization share problems (e.g., for an actuator)

Conic policy performance is competitive with ESPI for smaller problems, and for larger problems improves on the underutilized and greedy policies

Ongoing work is focused on identifying and evaluating important categories of time-utility functions and tailoring our approach to address their nuances

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Publications[1] R. Glaubius, T. Tidwell, C. Gill, and W.D. Smart, “Real-Time

Scheduling via Reinforcement Learning”, UAI 2010

[2] R. Glaubius, T. Tidwell, B. Sidoti, D. Pilla, J. Meden, C. Gill, and W.D. Smart, “Scalable Scheduling Policy Design for Open Soft Real-Time Systems”, RTAS 2010 (received Best Student Paper award)

[3] R. Glaubius, T. Tidwell, C. Gill, and W.D. Smart, “Scheduling Policy Design for Autonomic Systems”, International Journal on Autonomous and Adaptive Communications Systems, 2(3):276-296, 2009

[4] R. Glaubius, T. Tidwell, C. Gill, and W.D. Smart, “Scheduling Design and Verification for Open Soft Real-Time Systems”, RTSS 2008

[5] T. Tidwell, R. Glaubius, C. Gill, and W.D. Smart, “Scheduling for Reliable Execution in Autonomic Systems”, ATC 2008

Thanks, and hopeto see you at CPSWeek 2011!

Chris Gill Associate Professor of

Computer Science and Engineering