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Managing Real-Time Transactions in Mobile Ad-Hoc Network DatabasesLe GruenwaldThe University of OklahomaSchool of Computer ScienceNorman, Oklahoma, U.S.A.http://email@example.com Funded by National Science Foundation Grants: EIA-9973465andIIS-0312746
Introduction • Goal • To develop and prototype a real-time database transaction management model for Mobile Ad-Hoc Network (MANET). • MANET: • Collection of wireless mobile nodes • No fixed infrastructure • Frequent occurrence of Network Partitions • Server and Client Power restriction • Time-critical applications • Used in Battlefield, Disaster Recovery, etc.
System Architecture • Servers (Large Mobile Host LMH) • Classical workstations with high memory, power and computing capabilities • Contains the complete DBMS • Clients (Small Mobile Host SMH) • Computers with reduced memory, power and computing capabilities • Clients contain the Query Processing Module of the DBMS Server1 Client4 Client5 Client1 Server3 Client2 Server4 Server2 Client3 Client6
Research Issues • Transaction Management • Data Caching • Data Replication • Concurrency Control • Commit Protocol • Recovery
Transaction Management • Incorporated three energy modes: active, doze and sleep. • Designed a Client Transaction Submission Protocol: LEQ (Location-Energy-Queue) • Firm Transactions • Time is the most important factor => sent to the least workloadandnearest server for transaction processing. • Soft Transaction • Energy is the most important factor => sent to the least workloadand highest energy server for transaction processing.
Transaction Management • Designed a Real Time Transaction Scheduling algorithm. s = d - (t + c + Pd * Td) • 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. • 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
GMANET (Group based MANET) Caching Model • Group leader movement vector: GM • Group member movement vector: RM + GM
GMANET Caching Model • Cache Assignment • Selective caching: only data with access frequency higher than some threshold is cached. • Data accessed by UD (Up-to-Date) type transactions are cached at group server leaders LMHg. • Data accessed by OU (Outdated Data) type transactions are cached at clients (LMHs and SMHs). • Cache Consistency • 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. • Cache Replacement • Based on access frequency and transaction type (firm vs. soft)
GMANET Caching Model • All write transactions are sent to LMHgs. • UD type read-only transactions can access cached data on LMHgs • Cache on LMHgs is always fresh by the strong consistency protocol. • OD type read-only transactions can access cached data on clients and LMHgs • They accept stale cached data in return for fast retrieval.
Data Replication Contacted the Norman Fire department and OU Military department for data and transaction model requirements. Data Items Temporal Data Items Read-Only Data Items Persistent Data Items Periodic Update Aperiodic Update Periodic Update Aperiodic Update Transactions Read Transactions Write Transactions Use Previous Value Overwrite Previous Value MRV MRVP OD Insert/Delete MRV – Most Recent Value MRVP – Most Recent Value in a Partition OD – Outdated Data
Replication Strategy • Real Time Aware: data items accessed by firm transactions are replicated before those accessed by soft transactions. • Partition Aware: the decision to replicate is based on: • Current network topology • Remaining power of servers • MRVP transactions are used to address network partitioning. • Power Aware: Servers with higher power hold the data items that are most frequently accessed.
Data Replication Strategy • Access frequencies of data items are computed based on: • Data Types • Transaction Types • Hot data items are replicated before cold data items. • Data accessibility is improved by reducing replica duplication between servers.
Prototype • Hardware • Laptop (Servers) • PDA (Clients) • Global Positioning System (GPS) • Wireless LAN Card • Software • Servers: MySQL, Linux, C, C++ • Clients: DALP, Win CE, Embedded Visual C++ • Routing Protocol
Future Research Directions • Develop a Real-Time Commit Protocol for MANET databases. • Develop a Real-Time Concurrency Control Protocol for MANET databases. • Evaluate the performance of the proposed techniques using the developed prototype for Fire Department and Military applications.