1 a scalable execution control method for context- dependent services wataru uchida, hiroyuki kasai,...
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A Scalable Execution Control Method for Context- dependent Services
Wataru Uchida, Hiroyuki Kasai, Shoji KurakakeNetwork Laboratories,
NTT DoCoMo, Inc.Jun. 28, 2006
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Outline Background and motivation Proposal of service execution control
method Simulation results Conclusions and future works
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Background Cellular networks are expected to provide
context-dependent services assist user's real world activities continuously monitor context and are executed
when the context satisfies pre-defined condition.
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Context-dependent servicesPush-based restaurant recommendation
automatically recommends nearby restaurants which have vacant tables.
Frenchrestaurant"la mère"
menu
context:・ location of user・ availability of tables
Child surveillance
user
user terminal's display
child's terminalwith GPS
mother'sterminal
I arrivedat the school!
automatically notify mother of her child's arrival to school/station/private school.
Other examples: 24hours healthcare service, Friend-finder service,...
context: location of child
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Problem and objective Need tremendous number of operations for
execution controls We have to
continuously acquire and collect many kinds of context determine execution for a large number of services.
Example Restaurant-recommendation: continuously locate user,
measure number of vacant tables, collect them and determine to recommend or not
Execution control operations with low frequency doesn't always work well (risk of missing execution timing).
Objective: reduce cost of execution control while preserving the service quality
Objective: reduce cost of execution control while preserving the service quality
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Service execution control
Context acquisition terminals(ex. cell phones with GPS device, non-contact type IC cards,...)
・・・
Server A
User
executionexecution
③ Determination of service execution
③ Determination of service execution
NetworkNetwork
Server BServer CExecutio
nconditio
n
Execution
condition
Execution
condition
① Context acquisition
① Context acquisition
② Context collection
② Context collection
: execution control operations
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Determination of execution Calculate expected utility (EU) for
execution(a1) and non-execution(a2), and chose one with higher EU Utility: effect of execution/non-execution for the
user EU for a1 and a2:
: state of contextyi
: utility of actionU aj
(yi)
EU of non-execution ( )
EU of execution ( )
execution time tEU t
a1
EU ta2
Expected utility (EU)
E U ta1
=X
i
P t(yi)U a1(yi)
E U ta2
=X
i
P t(yi)U a2(yi)
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principle of our method
Reduction of execution control operations Probability of satisfying execution
condition (=risk of missing chance of execution) varies with time.
Reduce execution control operations when probability of satisfying execution condition
is low
Reduce execution control operations when probability of satisfying execution condition
is low
thigh frequency (large risk)
low frequency (small risk of missing chance)
EU EU of non-execution ( )EU ta2
EU of execution ( )EU ta1
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Interchange probability estimation Predict context values Utility in future can be estimated using predicted
values Compare estimated with
EU
tnow
estimated EU ta2
estimated EU ta1
EU of non-execution ( )EU ta2
EU of execution ( )EU ta1
EU ta1
EU ta2
Low probability
High probability
:probability distribution of EU
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Collecting context with large effect
Interchange probability depends on the values of each context. Each context's effect on the probability is not
equal. Context with large effect is collected more
frequently. Each context's effect can be calculated
using conditional probabilities.
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Utility estimation use Bayesian network
can handle probability distributions of context.
Distance（ D）
Option（ O）
customernumber （ C）
Acceptsthe user（ B）
Utility (U)
Action (A) a1: recommenda2: don't recommend
Utility table
O B A U
YES
YESa1 200
a2 -150
NOa1 -50
a2 100
NO
YESa1 -50
a2 100
NOa1 -200
a2 150E U a1
=X
i
PyiU a1(yi)
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System architecture
Server-side controller
Server A Server B Server C
Context 1 acquisition
terminal
Invoke executionRegister execution condition
Context 2 acquisition
terminal
Collect context with high effect
frequently when probability of
satisfying execution condition is high.
Collect context with high effect
frequently when probability of
satisfying execution condition is high.
．．．
．．．
Each terminal send values when the
value enters "alert region" (estimation is incorrect and execution
time approaches )
Each terminal send values when the
value enters "alert region" (estimation is incorrect and execution
time approaches )
(Future work)
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Simulation setup Metrics: number of collections, service quality
(explained in following slides) Assumed service: restaurant recommendation
Compared with: method which Periodically performs Execution Control operations (PEC)
3km
3km
User
Max speed: 100m/min
Restaurant
Context: distance from restaurant, availability of tables
Random-walk
Num. vacant tables increased or decreased at every minute
・・・
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Service quality (1/2) We measured execution ratio:
(num. of timings service is executed) / (num. of timings execution condition is satisfied)Service quality is high when the ratio is
high. t
:Timings services are executed
:Timings execution condition is satisfied
Execution ratio = 4 / 8 = 50%
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Service quality (2/2) Also measured deviation from ideal
decisions: Sum of times when the decision is different
from that of the ideal case (execution control with the highest frequency)
Service quality is high when the value is small.
tTimings in the ideal case
t
Timings the method detected
Time when the decision is different
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0
20000
40000
60000
80000
100000
120000
140000
160000
180000
200000
20% 30% 40% 50% 60% 70% 80% 90% 100%Execution ratio
ProposedPEC
Result 1/2: Execution ratio
Cost:
hig
h
Num
ber
of c
olle
ctio
ns
Service quality: high
Reduce 90% of the total costReduce 90% of the total cost
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Result 2/2:deviation from ideal decisions
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
200000
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000Deviation from ideal decisions
ProposedPEC
Cost:
hig
h
Num
ber
of c
olle
ctio
ns
Service quality: high
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Conclusions Scalable execution control method for context-
dependent servicesMethodology: gather context when the service
execution condition is about to be satisfiedSimulation results: execution control operations
are reduced while preserving service quality
[Future works] Development of execution control using alert region Service quality loss-less (e.g. execution guaranteed) method
