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AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

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Page 1: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

AutoPilot Year 1 Results

Principal Investigators:

Jerry Stach

E.K. Park

University of Missouri - Kansas City

Page 2: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Questions Posed By Sponsor

• 1. Decentralized Scaleable Trader (a) How do you maintain global information about a set of

available services without a central point of failure? (b) What are the Query and Update costs of a decentralized

Trader?

University of Missouri - Kansas City

Page 3: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Questions Posed By Sponsor

• 2. Agent Health Monitor

Investigate ways to monitor large numbers of mobile/distributed agents with minimal effect on overall systems performance.

University of Missouri - Kansas City

Page 4: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Questions Posed By Sponsor

• 3. Mobile Agent Patterns As work on the first two categories progresses,

document any design patterns that emerge.

University of Missouri - Kansas City

Page 5: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Engineering Problems - Current System

• Only about 103 agents can run concurrently in the current network. The answers to the Sponsor’s questions must scale at least one order of magnitude:

• 104 concurrent mobile agents

• 104 service nodes with 10 – 20 service instances per node

• the number of Trader Places approaching 103

• 2*103 entries per directory.

University of Missouri - Kansas City

Page 6: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Underlying Causes

• The current limitation of 103 Agents implies Band Limiting• Band Limiting can Occur in the Service Place CPU if mean

processing time is high relative to agent arrivals• Band Limiting can occur in the Network as a function of

message intensity– focused overloads (agent Trader Place)– agent collaboration or management

University of Missouri - Kansas City

Page 7: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Band Limiting in the Service Place CPU

• Possible Strategies– Accelerate CPUs – Increase number of CPUs• Acceleration may not place the power in the right

locations• Increasing the number of Service Places increases

band width demand of the network

University of Missouri - Kansas City

Page 8: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Implications

• If arbitrary increases in the number of Service Places are to be avoided, concurrency is implied to maximize CPU utilization– agents should have capability to function as

autonomous distributed processes– mobility must include agent reasoning about local

congestion and distance

University of Missouri - Kansas City

Page 9: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Band Limiting in the Network

• Strategy– Minimize number of messages in the Agent Colony

• messages associated with accessing the Trader Place regarding Service offerings [Sponsor Question 1]

• messages associated with managing the colony of agents [Sponsor Question 2]

University of Missouri - Kansas City

Page 10: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Implication

• Minimizing the number of messages to the Trader Places implies some intermediate process in the Architecture that can parse the Trader Place vector for multiple agents at a Service Place. The agent must then be able to interpret the vector relative to its own preferences. The mobility decision is implied at the agent (lowest Architecture level) not at the Trader (highest Architecture level)

University of Missouri - Kansas City

Page 11: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Implication

• Minimizing the number of messages associated with population management implies being able to anticipate the location of a given agent to eliminate exhaustive search or suspension of the population. This implies higher levels of the Architecture must understand the mobility decision in order to locate agents in the network

University of Missouri - Kansas City

Page 12: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Problem Synthesis

• A multi - level Architecture is implied

Agent

Service Planner

Trader

University of Missouri - Kansas City

Page 13: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Problem Synthesis

• Migration is fundamental to the answers to Questions 1, 2– agents are situated and must move to a subsequent

Service Place based upon• service attributes of time, cost, quality (perception)

• a context of total moves that satisfies their service sequences (local optimization)

University of Missouri - Kansas City

Page 14: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Problem Synthesis

• Agent migrations should not adversely effect the network system– moves should be sensitive to network dynamics such

as local congestion and path length (global optimization)

– in the large, individual agent migration decisions should cause load leveling at Service Places and traffic distribution without a central authority (emergent behavior)

University of Missouri - Kansas City

Page 15: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Proposed AutoPilot Architecture

Agent

Service Planner

Trader

Topologist

Service Place

Trader Place

AutoPilot Network Router

University of Missouri - Kansas City

Page 16: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Research Implications:Distributed Artificial Intelligence

- there is an Artificial Intelligence Sub Problem

- there is a Distributed Processing sub problem

- there is a Network sub problem

University of Missouri - Kansas City

Page 17: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Artificial Intelligence Sub Problem

• Given a set of agent preferences for time to service, cost of service and quality of service, select the most desirable location from a set of possible locations that conform to the agent’s preferences

This is a multi-attribute programming problem

University of Missouri - Kansas City

Page 18: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Distributed Processing Sub Problem

• Given a set of Service Places and the service set of each Service Place, find an optimal assignment of Services to the Service Places subject to the Service Place environments

This is a multi-processor task assignment problem

University of Missouri - Kansas City

Page 19: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Network Sub Problem

• Given a sequence of Services specified by the agent’s work flow signature and a set of feasible Service Places, construct a optimal itinerary that minimizes total trip time

This is a graph theory problem (trip planning)

University of Missouri - Kansas City

Page 20: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Strategy

• Solve academic problems in a manner that produces engineering solutions as well as new knowledge

• Select solution techniques that integrate the three classes of sub problems

University of Missouri - Kansas City

Page 21: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Research Approach: Composition not Decomposition

• 1. Obtain a solution for the assignment of Services to Service Places

• 2. Obtain a solution to for agent's attribute based perception of Service Places

• 3. Integrate the results from (1) and (2) forming a mobility heuristic

• 4. Validate the heuristics by simulating situated multi-agents in a network of Service Places.

• 5. Formulate Trader Place Inquiry/Update Costs

University of Missouri - Kansas City

Page 22: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Overview of Assignment of Services to Service Places

• Each agent carries a work flow signature for the possible processing sequences of its task graph

AB

C

D

E

Work Flow Signature = A;B;(C+D);E

University of Missouri - Kansas City

Page 23: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Assignment of Services continued

• Build an interior graph of the signature. Weight interior edges with the payload size from taskI to taskJ

AB

C

D

E

1.0 2.83.6

.8

1.1

Input is initial agent payload and a scaling matrix

University of Missouri - Kansas City

Page 24: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Assignment of Services continued

• Connect each interior Service node to every Service Place supporting that service subject to the agent’s preference criteria

• Weight the edges from the Services to the Service Places by agent preference for the Service Place

University of Missouri - Kansas City

Page 25: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Assignment of Services continued

_serviceQuality_of Move. Possible

qAgent.w Software

cAgent.w Software

qAgent.w

rviceCost_of_se Move. Possible

qAgent.w Software

cAgent.w Software

cAgent.w

,

∗+

+∗

+

=

Software

Software

qix

University of Missouri - Kansas City

Page 26: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Assignment of Services continued

)(12

11

,,1

,, qiq

qrriqi x

nn

xn

w ? −

−−−

=

Weighting of edges from Services to Service Places

University of Missouri - Kansas City

Page 27: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Assignment of Services continued

A

B D

CE

C a,b

C b,d

C b,c

C d,e

C c,e

SP1SP2

SP3

Wa,sp1

Wa,sp3

Wb,sp1 Wd,sp2

We,sp3Wc,sp3

University of Missouri - Kansas City

Page 28: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Assignment of Services continued

A

B D

CE

C a,b

C b,d

C b,c

C d,e

C c,e

SP1

SP2

SP3

Wa,sp1

Wa,sp3

Wb,sp1

Wd,sp2

We,sp3

Wc,sp3

Not SP1

Find a minimum cut to the network - Services A,B,C are assigned to SP1

University of Missouri - Kansas City

Page 29: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

D

E

C d,eSP2

SP3

Wd,sp2

We,sp3

Re-compute weights, find a new minimum cut, D is assigned to SP2, E is assigned to SP3

Assignment of Services continued

Final Service Assignments

SP1:= A,B,C

SP2 := D

SP3 := E

University of Missouri - Kansas City

Page 30: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Improving Performance

• We do not want to consider 10K Service Places for each agent– Observations

• several locations may be equivalent by agent perception of time to service, cost of service and quality of service

• if we could pick the Service Places to consider in the right order, we should assign all services in a relatively few iterations

University of Missouri - Kansas City

Page 31: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Improving Performance continued

• Leads to the multi attribute programming problem

• An agent perceives each Service Place by its attributes (time, cost,quality)

• If the agent could rank the Service Places by these attributes, we could generate equivalence classes of Service Places

University of Missouri - Kansas City

Page 32: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Improving Performance continued

Sp3SP5SP4 SP2SP1

SP5SP3SP2SP1SP4

SP3SP2SP1SP4SP5

Equivalence By Time Equivalence By Cost Equivalence By Quality

SP3 is an non-dominated Service Place in intersectionof the first equivalence class for each attribute.Have the graph algorithm consider SP3 first.

University of Missouri - Kansas City

Page 33: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Multi-Attribute Programming Problem

• We cannot use a linear weighting scheme to rank nodes because time, cost and quality do not normalize

• an agent’s constant perception of its environment is time

• the Topologist can provide the Service Planner the current geodasic to a Service Place (router interface)

University of Missouri - Kansas City

Page 34: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Multi-Attribute Programming continued

• Humans distort time by attributes – long car ride for a bargain is viewed as acceptable to

some limit of time– a one hour poor presentation is long– a two hour great movie is short• Why not let the agent distort time by the attributes of

Service Places?– Need an objective function

University of Missouri - Kansas City

Page 35: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Functions of Cost and Quality on Time

))(1()(

))(1()(

qc

qtq

qc

ctc

ww

wwqtf

www

wctf

+−=

+−=

University of Missouri - Kansas City

Page 36: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Quantifying agent perception

[ ]n_distortiomax

tftfserviceinitiatetotime

perception

qc

+↔

=

)()(___

In AutoPilot we limit max_distortion to twice the diameter of the network so an agent perceives the timeto initiate service from nearly zero to twice the networkdiameter depending on its perception of the Service Place.

University of Missouri - Kansas City

Page 37: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Demonstrations of Research Results

• Description of Base Cases presented

• Results viewed by visual front end

• single agent simulations– link speeds are negligible

• heuristic search for service - equal preferences for time, cost, quality

• migration by preference for time

• migration by preference for cost

University of Missouri - Kansas City

Page 38: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Demonstrations of Research Results continued

• Multi-agent simulation– colony of 100 agents– all services offered on all nodes– arrival rates to network are high relative to processing

time– transmission times are negligible– hope to see second order network effects as emergent

behavior

University of Missouri - Kansas City

Page 39: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

London

New York

Sydney

Los Angeles1

2

3

Page 40: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Network Second Order Effects as a result of multi-agent interaction

Emergent Colony Behavior• Under network loading individual agent decisions aggregate to Service Place load-leveling in the absence of any central network or Trader authority.

Expected Behavior Desired Behavior

Legend CPU UtilizationService Place Queue LengthService Place Agent Age

Page 41: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Accomplishments• 1. Obtain a solution for the assignment of Services to Service

Places Complete

• 2. Obtain a solution to for agent's attribute based perception of Service Places Complete

• 3. Integrate the results from (1) and (2) forming a mobility heuristic Complete

• 4. Validate the heuristics by simulating situated multi-agents in a network of Service Places.

– Partially Complete, base cases only, not fully debugged

• 5. Formulate Trader Place Inquiry/Update Costs– Equations presented in year end report

Page 42: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Proposed Research ActivitiesYear 2

• Focus on remaining Sponsor questions:

– 1(a). Decentralized Scaleable Trader

How do you maintain global information about a set of available services without a central point of failure?

–2. Agent Health Monitor Investigate ways to monitor large numbers of mobile/distributed

agents with minimal effect on overall systems performance subject to the Trader cost formula from Year 1 results.

University of Missouri - Kansas City

Page 43: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Proposed Research Activities Year 2 continued

• generalize the multi-attribute function for n attributes• fully debug the simulator and extend to accommodate a

definition of the Sponsor’s network (links/ number nodes)

• extend the simulator to include Trader Place update policies

(interval, random..)

University of Missouri - Kansas City

Page 44: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Proposed Research Activities Year 2 continued

• study the relationship between emergent behavior and the Trader Place update policy

• improve visualization post processor

• in first half year produce two journal papers on agent mobility– multi attribute programming solution– general formulation of agent mobility

University of Missouri - Kansas City

Page 45: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Proposed Research Activities Year 2 continued

• study the applicability of Artificial Life principles to agent mobility in large colonies.

University of Missouri - Kansas City

Page 46: AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park University of Missouri - Kansas City

Current Status

• Summary of progress against proposal– On-track in pursuing Sponsor questions with respect

to research activities– Behind in debugging the simulator - not ready for

delivery yet

University of Missouri - Kansas City