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Remote Procedure Call (RPC) is a protocol that allows one program to request a service or action to be executed by another program located on a different computer in a network. The program initiating the call sends a request to the remote host machine, which processes the request and sends back the result. In the context of our exercise, the server receives an RPC request to increment a field value in a disk block. Understanding RPC is essential as it is the foundation of how the request is communicated and executed across distributed systems.

Key benefits of RPC include:

• Transparency: Acts like a local procedure call despite being remote.
• Efficiency: Suitable for distributed systems connectivity.
• Modularity: Enables separation of services into discrete units.

With RPC, we focus on creating a seamless and efficient method for interaction between different system components, which is crucial for our exercise on executing requests exactly once.

Undo logging is a technique used to ensure data consistency and recoverability in case of a system crash. Before modifying any data, the current state of that data is written to a log. If a crash happens during the operation, this log can be used to 'undo' the changes, restoring the data to its previous state.

In our exercise, undo logging steps include:

• Writing the current value of the block to the undo log before making changes.
• Proceeding with the requested updates knowing we can revert if needed.

In the event of a crash, the undo log is crucial for ensuring that incomplete operations do not leave the data in an inconsistent state. By rolling back to the previous state, undo logging helps maintain stability and reliability in the system.

Exactly-once semantics guarantees that a particular operation, such as an RPC request, is executed precisely one time. This is challenging in distributed systems, especially when considering server crashes and retries.

To achieve exactly-once semantics, our exercise uses a combination of undo logging and a designated log to track completed operations. The main points include:

• Logging the initial state of data before changes begin.
• Logging the completion of the operation.

In case of a server crash, these logs are referenced to decide whether an operation needs to be reverted or re-executed. This mechanism ensures that all operations are executed once and only once, promoting data consistency and system reliability.

Server crash recovery is the process of restoring system operations and ensuring data consistency after a failure or crash. In our exercise, maintaining an undo log and a designated completion log helps streamline recovery.

Upon restart, the server checks:

• The completion log to identify finished operations.
• The undo log to revert any incomplete changes.

After ensuring the state is consistent, incomplete requests are re-executed from the start. This approach helps the system handle crashes gracefully, minimizing data loss and ensuring high availability.

Data consistency ensures that a system reflects the accurate and correct state of data at any given time, even in the face of concurrent operations or crashes. It is a critical aspect of distributed systems.

In the context of our exercise, the techniques used ensure consistent data:

• Undo logging to revert to previous states if needed.
• Marking completed operations in a designated log.

By using these concepts, the system ensures that data remains accurate and intact, preventing anomalies and ensuring trust in the operations. Consistent data forms the backbone of reliable distributed systems, essential for maintaining system integrity.