1 A Scalable Execution Control Method for Context- dependent Services Wataru Uchida, Hiroyuki Kasai, Shoji Kurakake Network Laboratories, NTT DoCoMo, Inc. Jun. 28, 2006

2 Outline Background and motivation Proposal of service execution control method Simulation results Conclusions and future works

3 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.

4 Context-dependent services Push-based restaurant recommendation automatically recommends nearby restaurants which have vacant tables. French restaurant "la mère" menu context: ・ location of user ・ availability of tables Child surveillance user user terminal's display child's terminal with GPS mother's terminal I arrived at 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

5 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

6 Service execution control Context acquisition terminals (ex. cell phones with GPS device, non-contact type IC cards,...) ・・ ・ Server A User execution ③ Determination of service execution Network Server BServer C Execution condition ① Context acquisition ② Context collection : execution control operations

7 Determination of execution Calculate expected utility (EU) for execution( a 1 ) and non-execution( a 2 ), and chose one with higher EU  Utility: effect of execution/non-execution for the user  EU for a 1 and a 2 : : state of context : utility of action EU of non-execution ( ) EU of execution ( ) execution time t Expected utility (EU)

8 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 t high frequency (large risk) low frequency (small risk of missing chance) EU EU of non-execution ( ) EU of execution ( )

9 Interchange probability estimation Predict context values Utility in future can be estimated using predicted values Compare estimated with EU t now estimated EU of non-execution ( ) EU of execution ( ) Low probability High probability :probability distribution of EU

10 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.

11 Utility estimation use Bayesian network  can handle probability distributions of context. Distance （ D ） Option （ O ） customer number （ C ） Accepts the user （ B ） Utility (U) Action (A) a 1 : recommend a 2 : don't recommend Utility table OBAU YES a1a1 200 a2a NO a1a1 -50 a2a2 100 NO YES a1a1 -50 a2a2 100 NO a1a a2a2 150

12 System architecture Server-side controller Server AServer BServer C Context 1 acquisition terminal Invoke execution Register execution condition Context 2 acquisition terminal 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 ) (Future work)

13 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 User Max speed: 100m/min Restaurant Context: distance from restaurant, availability of tables Random-walk Num. vacant tables increased or decreased at every minute ・・ ・

14 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%

15 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. t Timings in the ideal case t Timings the method detected Time when the decision is different

16 Result 1/2: Execution ratio Cost: high Number of collections Service quality: high Reduce 90% of the total cost

17 Result 2/2: deviation from ideal decisions Cost: high Number of collections Service quality: high

18 Conclusions Scalable execution control method for context- dependent services  Methodology: gather context when the service execution condition is about to be satisfied  Simulation 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

19 Thank you!