dsfx/docs/adr/0013-performance-optimizations-and-resource-management.md

# ADR-0013: Performance Optimizations and Resource Management

## Status

Proposed

## Context

Our system is designed to handle extensive file operations, including encryption, decryption, file partitioning, replication, and versioning across a distributed network of nodes. As such, ensuring that the system performs efficiently and manages resources optimally is critical. This ADR addresses methods to enhance throughput, minimize latency, and manage computational and storage resources effectively.

Performance and resource management are especially important given:

- **Functional requirements (FR-2, FR-5):** which demand efficient partitioning, deduplication, and replication of file chunks across nodes.
- **Non-functional requirements (NFR-2, NFR-3, NFR-6):** which stipulate that operations should meet performance benchmarks even when testing under simulated adverse network and disk conditions.

The system should be capable of processing file operations with minimal delay while scaling to manage growing data volumes and an increasing number of nodes. This ADR also needs to consider the impact of encryption and audit logging (FR-1, FR-6) on overall resource utilization.

## Decision

We will incorporate several key strategies to optimize performance and manage resources effectively:

- **Efficient Algorithms and Optimized Code Paths:**
  Critical operations such as file chunking, encryption/decryption, and hashing (using SHA-256) will be implemented with optimized algorithms to minimize computational overhead. The design will include profiling and benchmarking during development to identify and address performance bottlenecks.

  - Use hardware acceleration (where available) for cryptographic functions.
  - Optimize file I/O through buffer management and asynchronous processing where appropriate.

- **Caching and In-Memory Data Structures:**
  Frequently accessed data (e.g., recently used file chunks, manifest metadata) will be cached in memory to reduce repeated disk I/O overhead.

  - Implement lightweight in-memory caches to store hash values and intermediate processing results.
  - Ensure the cache size is configurable to balance memory usage and performance.

- **Asynchronous and Parallel Processing:**
  Operations that are independent, such as processing individual file chunks or replicating file data across nodes, will be executed asynchronously or in parallel.

  - Leverage concurrent processing for encryption, hashing, and network data transfers.
  - Use non-blocking I/O patterns to improve responsiveness, especially under simulated high-load conditions.

- **Resource Utilization Monitoring:**
  The system will include internal monitoring to track CPU, memory, disk I/O, and network usage.

  - Build diagnostic tools that integrate with our deterministic simulation framework to allow testing under varied load conditions, as stipulated in NFR-2 and NFR-6.
  - Use collected data to dynamically adjust cache sizes or processing concurrency levels.

- **Optimized Replication and Audit Logging:**
  To minimize the performance impact of data replication and audit logging:
  - Use batched replication operations where possible to reduce network overhead.
  - Implement efficient append-only logging with periodic snapshotting to limit the overhead of continuously processing long audit trails (refer to ADR-0006).

## Consequences

- **Advantages:**

  - **Improved Throughput & Responsiveness:** Optimized algorithms, caching, and asynchronous processing will reduce latency, ensuring that file operations meet the speed and performance benchmarks specified in NFR-2.
  - **Scalability:** Effective resource management will support the system's ability to scale horizontally as the number of nodes and data volume increases, aligning with our NFR-3 requirements.
  - **Predictable Performance:** Integrating performance monitoring with deterministic testing provides a means to consistently validate that resource utilization remains within acceptable bounds under varied simulated conditions (NFR-6).

- **Trade-offs:**
  - **Implementation Complexity:** Introducing caching, asynchronous processing, and parallelism can significantly increase system complexity. However, modular design techniques and careful documentation will mitigate long-term maintenance challenges.
  - **Resource Overhead:** Proactive resource monitoring may add some overhead; nonetheless, this cost is justified by the benefits of proactive performance optimization.
  - **Testing and Tuning:** Optimizing performance requires ongoing profiling and tuning, which may extend initial development time but ensures that the system maintains high performance as it scales.

## References to Requirements

- **Functional Requirements:**

  - FR-2: Efficient file partitioning and chunk management require that file operations are processed quickly and reliably.
  - FR-5: Replication and redundancy management must occur efficiently to ensure that data remains available even under adverse conditions.

- **Non-Functional Requirements:**
  - NFR-2: Performance & Responsiveness – The system must complete operations within acceptable time frames under simulated network and disk latency.
  - NFR-3: Scalability & Capacity – The system should handle a growing number of files, versions, and nodes while maintaining high performance.
  - NFR-6: Deployability & Maintainability – Deterministic simulation testing of resource utilization ensures consistent performance and ease of maintenance.

## Conclusion

The proposed performance optimizations and resource management strategy is designed to ensure that the system remains responsive and scalable even under heavy load or simulated network and disk adversities. By employing efficient algorithms, caching, asynchronous processing, and proactive resource monitoring, this strategy fulfills our functional and non-functional requirements while supporting a robust, high-performance file storage system.