Efficient, Energy Conserving Transaction Processing in Wireless Data Broadcast

1. Efficient, Energy Conserving Transaction Processing in Wireless Data Broadcast SangKeun Lee, Chong-Sun Hwang, and Masaru Kitsuregawa IEEE Transactions on Knowledge and Data Engineering, Sept. 2006 Presented by Jing (David) Dai Oct 31, 2006

2. Outline • Introduction • Preliminaries • Efficient Transaction Processing • Handling Access Failures • Analysis • Performance Evaluation

3. Introduction • Wireless services • Benefited from 3rd generation wireless infrastructure and rapid growth of technology • Limited by bandwidth, disconnection, and power • Wireless broadcasting • Data dissemination in mobile environment • Using palmtops to check airline, stock, weather, and traffic • Microsoft Smart Personal Objects Technology (SPOT)

4. Introduction • Air indexing • Broadcast not only data, but also index • Client listen to the index to predict the arrival of desired data • Client sleep and wake up to receive the data • Measures • Access-time (efficiency) • Time elapsed from issuing a query to receiving data • Tuning-time (power consumption) • Time elapsed by client staying active to retrieve data

5. Introduction • Contributions of this work • Access protocol for multiple items to support wireless transactions • Predeclaration-based transaction processing with selective tuning ability • Mechanism to tolerate access failures during selective tuning • Analytical cost model to derive access-time and tuning-time • Cost model and performance evaluation to assess the proposed method

6. Preliminaries • Basic scenario • One server, single broadcasting channel • Periodically and uniformly broadcast data • Multiple clients, can only read • Updates are reflected between successive broadcasts • Server also broadcasts index to allow clients selectively tune to receive • Client have enough local storage

7. Preliminaries • Data structure • Bucket: smallest unit of a broadcast, one bucket = one data item • Bcast: sequence of buckets, contains all data items • Index: multi-level tree, the lowest level nodes contain pointers to actual data • Pointer: specifies offset from current index bucket to the bucket it points to

8. Preliminaries • Data organization • Access_opt • Tune_opt • (1,m) indexing

9. Efficient Transaction Processing • Predeclaration [2003] • Optimize access_opt organization by pre-declaring the data items that will possibly be requested • Example: if (d2<3) then read(d0) else read(d1)

10. Efficient Transaction Processing • P with selective tuning ability • Work with tune_opt and (1,m) indexing • Three steps • Initial probe • Locate the next nearest index • Index search • Search the index to locate the desired data • Data retrieval • Tune into the channel at the particular point to download the data

11. Efficient Transaction Processing • Accessing multiple items

12. Efficient Transaction Processing • P with selective tuning • Read the index at the beginning of the next broadcast cycle • Prepare predeclared set Pre_RS(T) and constructs the sequence of pointers to all data items in Pre_RS(T) • Acquire data items in Pre-RS(T) • Deliver data to the transaction • Theorem • P generated serializable execution of read-only transactions if the server broadcasts only serializable data values in each broadcast cycle

13. Handling Access Failures • Search may fail because • Disconnection • Power insufficiency • Communication noise • Handoffs & location registration • Three existing solutions for single item retrieval failure • Reprobe • Reaccess • Adaptive access method

14. Handling Access Failures • Handling access failure in (1,m) indexing

15. Handling Access Failures • Principle for serializability • All data items requested by a transaction should be retrieved within the same broadcast cycle • Recovery process • Record the required index buckets • If no data miss, tune to these positions in next nearest index and continue • If data miss, tune to these positions in beginning of next broadcast and continue

16. Method P Access_Opt Access Time Turning Time = Access Time Tune_Opt Access Time Tuning Time (1, m) Indexing Access Time Tuning Time = Tune_Opt. Tuning Time Analysis

17. Analysis • Method InV • Access_Opt • Access Time = Tuning Time • Tuning_Opt • Access Time • Tuning Tim • (1,m) Indexing • Access Time • Tuning Time

18. Analysis • Method MV • Access_Opt • Access Time = Tuning Time • Tuning_Opt • Access Time • Tuning Tim • (1,m) Indexing • Access Time • Tuning Time

19. Experiments • Performance Evaluation • Accuracy of the analytical model in Tune_opt.

20. Experiments • Performance Comparison • Expected access and tuning time as a function of a transaction size • Access time of method P is constant; • Method P is an order of • magnitude better than the others.

21. Experiments • Performance comparison for varying update rate • Method P, both access and tuning times are not affected by the update rate significantly. • Method P is superior to MV and InV in terms of access time for every range of update rates.

22. Experiments • Access and tuning times of method P on various parameter settings

23. Conclusion and Critiques • Conclusion: • An access protocol is proposed to improve the processing time of read only transactions and energy consumption in mobile environment. • Theoretical analysis of access and tuning times of proposed method and other existing methods are given. • Critiques • Assumptions: only one broadcast channel • The probe phase does not explain clearly how the offset value is calculated and obtained. • Real data simulation was not conducted.