A Scalable Execution Control Method for Context- dependent Services

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 Child surveillance Push-based restaurant recommendation context: location of child context:・location of user・availability of tables I arrivedat the school! Frenchrestaurant"la mère"menu child's terminalwith GPS user mother'sterminal user terminal's display automatically notify mother of her child's arrival to school/station/private school. automatically recommends nearby restaurants which have vacant tables. Other examples: 24hours healthcare service, Friend-finder service,...

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

7. 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 context Expected utility (EU) :utility of action EU of non-execution ( ) execution time t EU of execution ( )

8. Reduce execution control operations when probability of satisfying execution condition is low EU EU of non-execution ( ) EU of execution ( ) high frequency (large risk) low frequency (small risk of missing chance) principle of our methodReduction of execution control operations • Probability of satisfying execution condition (=risk of missing chance of execution) varies with time. t

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

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. a1: recommenda2: don't recommend Action (A) Distance（D） Option（O） Utility (U) Acceptsthe user（B） customernumber（C） Utility table

12. Each terminal send values when the value enters "alert region" (estimation is incorrect and execution time approaches ) Collect context with high effect frequently when probability of satisfying execution conditionis high. (Future work) System architecture Server A Server B Server C ．．． Invoke execution Register execution condition Server-side controller Context 1 acquisition terminal Context 2 acquisition terminal ．．．

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) Context: distance from restaurant, availability of tables 3km Random-walk User 3km ・・・ Max speed: 100m/min Restaurant 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 Execution ratio = 4 / 8 = 50% :Timings execution condition is satisfied :Timings services are executed

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

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

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! email: uchidaw@nttdocomo.co.jp