Vijay Kumar & Nitin Prabhu SCE, Computer Networking University of Missouri-Kansas City

1. HDC - Hot Data Caching in Mobile Database Systems Vijay Kumar & Nitin Prabhu SCE, Computer Networking University of Missouri-Kansas City Kansas City, MO 64110, USA kumarv@umkc.edu Panos K. Chrysanthis Computer Science University of Pittsburgh Pittsburgh, PA 15260, USA panos@cs.pitt.edu Presenter: Mohamed A. Sharaf Computer Science University of Pittsburgh

2. HDC - Hot Data Caching in Mobile Database Systems • Topics covered • Introduction to Mobile Database System • Data caching in mobile environment • Transaction processing • Performance • Conclusions

3. Mobile Database System – MDS This architecture is based on GSM (Global System for Mobile Communication) • Base Station Controller (BSC) • It manages the activities of a set of BSs • Database Server (DBS) • All mobile transactions are processed by one or more DBS • A MU reaches a set of DBSs through its BS • Base Station (BS) • A BS serves a cell. • It manages communication in its cell. • It communicates with BSC through wired line. • Mobile Unit (MU) • A mobile user interacts with MDS through this unit • It communicates with the Base Station (BS) through a wireless channel • Examples: PDA, laptop, or a cell phone

4. HDC - Hot Data Caching in Mobile Database Systems • Problem Statement: Maximize the throughput (Minimize the response time) of Read-only transactions submitted at a mobile unit • Contribution: An integrated data-transaction shipping scheme that uses hot data caching to maximize correct local execution of Read-only transactions • Approach: An overlay caching of hot data that: • Maximizes the number of transactions satisfied by the cached snapshot of the database • Minimizes rollbacks due to inconsistent data views

5. HDC - Hot Data Caching in Mobile Database Systems • Topics covered • Introduction to Mobile Database System • Data caching in mobile environment • Transaction processing • Performance • Conclusions

6. Data identification for caching A user initiates transactions through a MU. Then, the MU performs the following steps: • Identifies the transaction’s data requirement • Preprocessing method • Profile-driven method • Builds a data structure, refer to as “cache matrix”, to store these data • Combines the new needed data with the data already cached

7. Data identification phase – An example “1” means: The transaction needs the data item Data Items • Low sum value identifies the least popular data item • Remove the least popular item • Remove transactions that access the least popular item Transactions

8. Data identification phase – An example The elimination continues until the number of data items is less than or equal to the cache size Example, if cache size = 4, then cache matrix is: Hot Data = Data items for caching D1, D10, D15, D30  current cache contents (empty initially) = D1, D10, D15, D30 Warm Data = Data items required by T1, T3, T4, T5, T7, and T10

9. HDC - Hot Data Caching in Mobile Database Systems • Topics covered • Introduction to Mobile Database System • Data caching in mobile environment • Transaction processing • Performance • Conclusions

10. Transaction execution • A transactions will fall into one of the following classes: • Active Transaction (AT): • Uses the hot data and it is immediately scheduled for execution • Waiting Transaction (WT): • Uses the warm data and it is transferred to the wait queue • New Transaction: • Will join the AT or WT according to its data requirements • How to schedule the execution of the transaction mix ?

11. Transaction Scheduling • Schedule the new transaction immediately for execution if all of its required data is available in cache (i.e., hot date) • Add the new transaction to the wait queue if few of its desired data is available in cache (i.e., warm data) • Send the new transaction to the DBS if none of its desired data is available in cache (i.e., cold data) • After a predefined interval, create a new cache matrix and refresh the cache • Some of the waiting transactions will become active • Some of the waiting transactions will continue waiting • To avoid starvation, a waiting transaction is sent to the server after a predefined number of cache refreshes

12. HDC - Hot Data Caching in Mobile Database Systems • Topics covered • Introduction to Mobile Database System • Data caching in mobile environment • Transaction processing • Performance • Conclusions

13. Simulation Model • Assumptions: • An upper limit on the number of transactions that can be executed concurrently, called MPL • A new cache matrix is created if the number of waiting transactions is equal to MPL • A waiting transaction is shipped to the server after two cache refreshes

14. Performance measurement Average transaction wait time with cache size

15. Performance measurement % of transactions executed in each phase

16. Performance comparison Percentage Transaction executed at MU in HDC and LFU

17. Performance comparison Transaction wait time in HDC and LFU

18. Performance comparison Throughput with MPL in HDC and LFU

19. Conclusions • The work proposed the following: • The Hot Data Caching (HDC) policy which is especially suitable for the mobile transaction processing • A transaction scheduling mechanism that integrates data and transaction shipping to reduce the average transaction wait time Preliminary results show that using our proposed HDC leads to increasing the number of transactions that are locally executed at the mobile host. This feature allows HDC to significantly reduces transactions’ wait time compared to existing caching policies.