Description

RustFS needs to implement intelligent adaptive buffer sizing optimization that automatically adjusts buffer sizes based on file size and workload type to achieve optimal balance between performance, memory usage, and security.

Background

Current file transfers use fixed-size buffers, which cannot adapt to different scenarios. Different workloads like AI/ML, big data, and secure storage have varying requirements for buffer sizes.

Functional Requirements

1. Core Data Structures

#[derive(Debug, Clone, PartialEq)]
pub enum WorkloadProfile {
    /// General purpose - default configuration
    GeneralPurpose,
    /// AI/ML training: optimized for large sequential reads
    AiTraining,
    /// Data analytics: mixed read-write patterns  
    DataAnalytics,
    /// Web workloads: small file intensive
    WebWorkload,
    /// Industrial IoT: real-time streaming
    IndustrialIoT,
    /// Secure storage: security first, memory constrained
    SecureStorage,
    /// Custom configuration
    Custom(BufferConfig),
}

#[derive(Debug, Clone)]
pub struct BufferConfig {
    pub min_size: usize,
    pub max_size: usize,
    pub default_unknown: usize,
    pub thresholds: Vec<(i64, usize)>,
}

#[derive(Debug, Clone)]
pub struct RustFSBufferConfig {
    pub workload: WorkloadProfile,
    pub base_config: BufferConfig,
}

2. Adaptive Algorithm

• Automatically select buffer size based on file size
• Support unknown file size scenarios (streaming)
• Memory pressure aware adjustment
• Performance monitoring and dynamic optimization

3. Scenario-Specific Optimizations

• GeneralPurpose: Balanced performance and memory usage
• AiTraining: Large file optimization, maximize throughput
• SecureStorage: Security first, limited memory usage
• IndustrialIoT: Low latency, real-time priority
• WebWorkload: High concurrency, small file optimization

4. Special Support

• Adaptation for special OS environments (e.g., Kylin, NeoKylin, Unity OS, etc.)
• Special handling for encryption devices
• Compliance guarantee for secure scenarios
• Optimization for embedded and edge computing environments

Acceptance Criteria

• Implement WorkloadProfile enum and configuration structures
• Implement file size based buffer calculation functions
• Provide optimized configurations for each workload
• Add performance monitoring and dynamic adjustment mechanism
• Special environment adaptation
• Unit tests covering all scenarios
• Performance benchmark tests
• Documentation updates

Technical Constraints

• Follow Apache 2.0 license
• Maintain S3 protocol compatibility
• Controlled memory usage, avoid OOM
• Thread safety

Related Files

• src/storage/buffer_pool.rs
• src/config/workload_profiles.rs
• src/performance/metrics.rs

Priority

High - Core performance optimization feature