1. Managing Real-Time Transactions in Mobile Ad-Hoc Network Databases Le Gruenwald The University of Oklahoma School of Computer Science Norman, Oklahoma, U.S.A. http://www.cs.ou.edu/~database ggruenwald@ou.edu Funded by National Science Foundation Grants: EIA-9973465 and IIS-0312746

2. Introduction o Goal To develop and prototype a real-time database transaction management model for Mobile Ad-Hoc Network (MANET). o MANET: Collection of wireless mobile nodes Collection of wireless mobile nodes No fixed infrastructure No fixed infrastructure Frequent occurrence of Network Partitions Frequent occurrence of Network Partitions Server and Client Power restriction Server and Client Power restriction Time-critical applications Time-critical applications o Used in Battlefield, Disaster Recovery, etc.

3. System Architecture o Servers (Large Mobile Host LMH) Classical workstations with high memory, power and computing capabilities Classical workstations with high memory, power and computing capabilities Contains the complete DBMS Contains the complete DBMS o Clients (Small Mobile Host SMH) Computers with reduced memory, power and computing capabilities Computers with reduced memory, power and computing capabilities Clients contain the Query Processing Module of the DBMS Clients contain the Query Processing Module of the DBMS Client1 Server1 Server2 Client2 Client3 Client4 Client5 Client6 Server3 Server4

4. Research Issues o Transaction Management o Data Caching o Data Replication o Concurrency Control o Commit Protocol o Recovery

5. Transaction Management o o Incorporated three energy modes: active, doze and sleep. o o Designed a Client Transaction Submission Protocol: LEQ (Location-Energy-Queue) o o Firm Transactions Time is the most important factor => sent to the least workload and nearest server for transaction processing. o o Soft Transaction Energy is the most important factor => sent to the least workload and highest energy server for transaction processing.

6. Transaction Management o o Designed a Real Time Transaction Scheduling algorithm. s = d - (t + c + Pd * Td) o o Designed a Server Transaction Processing protocol making use of servers’ energy modes (active vs. doze) to reduce the number of firm transaction aborts while conserving energy. o o Designed a Server Transaction Result Delivery protocol making use of clients’ energy modes (active vs. doze) to reduce the number of firm transaction aborts while conserving energy. s – Slack Time d – Deadline t – Transaction Execution Time c – Current Time Pd – Probability of Disconnection Td – Average Disconnection Time

7. Simulation Results (Transaction Management)

8. GMANET (Group based MANET) Caching Model o Group leader movement vector: GM o Group member movement vector: RM + GM

9. GMANET Caching Model o Cache Assignment o Selective caching: only data with access frequency higher than some threshold is cached. o Data accessed by UD (Up-to-Date) type transactions are cached at group server leaders LMHg. o Data accessed by OU (Outdated Data) type transactions are cached at clients (LMHs and SMHs). o Cache Consistency Caches on clients are maintained at the weak consistency level => calculate refresh time estimate for randomly/periodically updated data. Caches on clients are maintained at the weak consistency level => calculate refresh time estimate for randomly/periodically updated data. Caches on group leaders are maintained at the strong consistency level => invalidation method. Caches on group leaders are maintained at the strong consistency level => invalidation method. o o Cache Replacement Based on access frequency and transaction type (firm vs. soft)

10. GMANET Caching Model o All write transactions are sent to LMHgs. o UD type read-only transactions can access cached data on LMHgs Cache on LMHgs is always fresh by the strong consistency protocol. Cache on LMHgs is always fresh by the strong consistency protocol. o OD type read-only transactions can access cached data on clients and LMHgs They accept stale cached data in return for fast retrieval. They accept stale cached data in return for fast retrieval.

11. Simulation Results (Caching)

12. Data Replication Contacted the Norman Fire department and OU Military department for data and transaction model requirements. Data Items Read-Only Data ItemsTemporal Data ItemsPersistent Data Items Periodic Update Aperiodic Update Transactions Read TransactionsWrite Transactions MRVMRVPODInsert/Delete Use Previous Value Overwrite Previous Value MRV – Most Recent Value MRVP – Most Recent Value in a Partition OD – Outdated Data

13. Replication Strategy o o Real Time Aware: data items accessed by firm transactions are replicated before those accessed by soft transactions. o o Partition Aware: the decision to replicate is based on: Current network topology Remaining power of servers MRVP transactions are used to address network partitioning. o o Power Aware: Servers with higher power hold the data items that are most frequently accessed.

14. Data Replication Strategy o o Access frequencies of data items are computed based on: Data Types Transaction Types o o Hot data items are replicated before cold data items. o o Data accessibility is improved by reducing replica duplication between servers.

15. Prototype o Hardware Laptop (Servers) Laptop (Servers) PDA (Clients) PDA (Clients) Global Positioning System (GPS) Global Positioning System (GPS) Wireless LAN Card Wireless LAN Card o Software Servers: MySQL, Linux, C, C++ Servers: MySQL, Linux, C, C++ Clients: DALP, Win CE, Embedded Visual C++ Clients: DALP, Win CE, Embedded Visual C++ Routing Protocol Routing Protocol

17. Future Research Directions o o Develop a Real-Time Commit Protocol for MANET databases. o o Develop a Real-Time Concurrency Control Protocol for MANET databases. o o Evaluate the performance of the proposed techniques using the developed prototype for Fire Department and Military applications.

18. Thanks!