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© Vijay Kumar, Nitin Prabhu, Panos K. Chrysanthis, USA AICCSA – 2005, Cairo, Egypt Vijay Kumar & Nitin Prabhu SCE, Computer Networking University of Missouri-Kansas.

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Presentation on theme: "© Vijay Kumar, Nitin Prabhu, Panos K. Chrysanthis, USA AICCSA – 2005, Cairo, Egypt Vijay Kumar & Nitin Prabhu SCE, Computer Networking University of Missouri-Kansas."— Presentation transcript:

1 © Vijay Kumar, Nitin Prabhu, Panos K. Chrysanthis, USA AICCSA – 2005, Cairo, Egypt Vijay Kumar & Nitin Prabhu SCE, Computer Networking University of Missouri-Kansas City Kansas City, MO 64110, USA Panos K. Chrysanthis Computer Science University of Pittsburgh Pittsburgh, PA 15260, USA HDC - Hot Data Caching in Mobile Database Systems Presenter: Mohamed A. Sharaf Computer Science University of Pittsburgh

2 © Vijay Kumar, Nitin Prabhu, Panos K. Chrysanthis, USA AICCSA – 2005, Cairo, Egypt Topics covered Introduction to Mobile Database System Data caching in mobile environment Transaction processing Performance Conclusions HDC - Hot Data Caching in Mobile Database Systems

3 © Vijay Kumar, Nitin Prabhu, Panos K. Chrysanthis, USA AICCSA – 2005, Cairo, Egypt Mobile Database System – MDS This architecture is based on GSM (Global System for Mobile Communication) 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 Base Station (BS) A BS serves a cell. It manages communication in its cell. It communicates with BSC through wired line. 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

4 © Vijay Kumar, Nitin Prabhu, Panos K. Chrysanthis, USA AICCSA – 2005, Cairo, Egypt Problem Statement: Problem Statement: Maximize the throughput (Minimize the response time) of Read-only transactions submitted at a mobile unit Contribution: Contribution: An integrated data-transaction shipping scheme that uses hot data caching to maximize correct local execution of Read- only transactions Approach: Approach: An overlay caching of hot data that: 1.Maximizes the number of transactions satisfied by the cached snapshot of the database 2.Minimizes rollbacks due to inconsistent data views HDC - Hot Data Caching in Mobile Database Systems

5 © Vijay Kumar, Nitin Prabhu, Panos K. Chrysanthis, USA AICCSA – 2005, Cairo, Egypt Topics covered Introduction to Mobile Database System Data caching in mobile environment Transaction processing Performance Conclusions HDC - Hot Data Caching in Mobile Database Systems

6 © Vijay Kumar, Nitin Prabhu, Panos K. Chrysanthis, USA AICCSA – 2005, Cairo, Egypt A user initiates transactions through a MU. Then, the MU performs the following steps: Data identification for caching 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 © Vijay Kumar, Nitin Prabhu, Panos K. Chrysanthis, USA AICCSA – 2005, Cairo, Egypt D1D8D10D11D15D20D22D30 T11111 T21 T3111 T411 T511 T6111 T71 T811 T911 T1011 Sum Data identification phase – An example Data Items Transactions “1” means: The transaction needs the data item 1.Low sum value identifies the least popular data item 2.Remove the least popular item 3.Remove transactions that access the least popular item D1D8D10D11D15D20D22D30 T11111 T21 T3111 T411 T511 T6111 T71 T811 T911 T1011 Sum

8 © Vijay Kumar, Nitin Prabhu, Panos K. Chrysanthis, USA AICCSA – 2005, Cairo, Egypt D1D10D15D30 T21 T6111 T811 T911 Sum2331 D1, D10, D15, D30  current cache contents (empty initially) = D1, D10, D15, D30 Hot Data = Data items for caching Warm Data = Data items required by T1, T3, T4, T5, T7, and T10 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:

9 © Vijay Kumar, Nitin Prabhu, Panos K. Chrysanthis, USA AICCSA – 2005, Cairo, Egypt Topics covered Introduction to Mobile Database System Data caching in mobile environment Transaction processing Performance Conclusions HDC - Hot Data Caching in Mobile Database Systems

10 © Vijay Kumar, Nitin Prabhu, Panos K. Chrysanthis, USA AICCSA – 2005, Cairo, Egypt Transaction execution A transactions will fall into one of the following classes: 1.Active Transaction (AT): Uses the hot data and it is immediately scheduled for execution 2.Waiting Transaction (WT): Uses the warm data and it is transferred to the wait queue 3.New Transaction: Will join the AT or WT according to its data requirements How to schedule the execution of the transaction mix ?

11 © Vijay Kumar, Nitin Prabhu, Panos K. Chrysanthis, USA AICCSA – 2005, Cairo, Egypt 1.Schedule the new transaction immediately for execution if all of its required data is available in cache (i.e., hot date) 2.Add the new transaction to the wait queue if few of its desired data is available in cache (i.e., warm data) 3.Send the new transaction to the DBS if none of its desired data is available in cache (i.e., cold data) 4.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 5.To avoid starvation, a waiting transaction is sent to the server after a predefined number of cache refreshes Transaction Scheduling

12 © Vijay Kumar, Nitin Prabhu, Panos K. Chrysanthis, USA AICCSA – 2005, Cairo, Egypt Topics covered Introduction to Mobile Database System Data caching in mobile environment Transaction processing Performance Conclusions HDC - Hot Data Caching in Mobile Database Systems

13 © Vijay Kumar, Nitin Prabhu, Panos K. Chrysanthis, USA AICCSA – 2005, Cairo, Egypt 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 © Vijay Kumar, Nitin Prabhu, Panos K. Chrysanthis, USA AICCSA – 2005, Cairo, Egypt Average transaction wait time with cache size Performance measurement

15 © Vijay Kumar, Nitin Prabhu, Panos K. Chrysanthis, USA AICCSA – 2005, Cairo, Egypt % of transactions executed in each phase CacheInitial cache1 refresh2 refreshTo server 10012%21%7%60% 15023%48%7%22% 20030%70%0% 25039%61%0% Performance measurement

16 © Vijay Kumar, Nitin Prabhu, Panos K. Chrysanthis, USA AICCSA – 2005, Cairo, Egypt Performance comparison Percentage Transaction executed at MU in HDC and LFU

17 © Vijay Kumar, Nitin Prabhu, Panos K. Chrysanthis, USA AICCSA – 2005, Cairo, Egypt Transaction wait time in HDC and LFU Performance comparison

18 © Vijay Kumar, Nitin Prabhu, Panos K. Chrysanthis, USA AICCSA – 2005, Cairo, Egypt Throughput with MPL in HDC and LFU Performance comparison

19 © Vijay Kumar, Nitin Prabhu, Panos K. Chrysanthis, USA AICCSA – 2005, Cairo, Egypt Conclusions The work proposed the following: 1.The Hot Data Caching (HDC) policy which is especially suitable for the mobile transaction processing 2.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.


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