A novel recursive architecture designed to significantly reduce the probability of undetected errors (UEP) during large-scale file transfers by leveraging in-network resources.
The Multi-Level Error Detection (MLED) framework, denoted as MLED(n, P), represents a groundbreaking recursive architecture engineered to dramatically minimize undetected error probability (UEP) in large-scale file transmission systems through strategic utilization of in-network computational resources. This innovation proves particularly vital for scientific computing and data-intensive applications where maintaining absolute data integrity is fundamental for reliable and reproducible computational analysis.
Architectural Innovation: MLED implements a sophisticated hierarchical design comprising n ≥ 3 levels, where each level contains multiple operational layers. Every layer L(i,j) at level i operates under a specific policy P(i,j) ∈ P that governs its functional behavior within its designated scope. This modular and decoupled architecture enables seamless integration with existing file transfer protocols while providing conceptual flexibility for future enhancements.
Research Validation: The MLED framework has undergone rigorous mathematical formulation and comprehensive experimental validation using the FABRIC research testbed infrastructure. Results demonstrate significant reductions in undetected error rates and retransmission overhead, validating its effectiveness in real-world network environments.
Implements n ≥ 3 levels with multi-layer composition where each layer L(i,j) operates under policy-driven governance P(i,j) ∈ P. This design ensures incremental error interception across progressively refined scopes.
Supports comprehensive communication functionalities through configurable policies for error detection, routing, addressing, congestion control, and flow management. Current implementation emphasizes error detection with architectural support for future policy expansions.
Achieves substantial reduction in undetected error probability through multi-layer interception before final delivery. Early detection mechanisms enable efficient localized retransmissions, minimizing full file retransfer requirements.
Highly configurable architecture capable of replicating existing file transfer frameworks while providing enhanced error detection capabilities. Seamless integration with current network infrastructures without requiring major protocol modifications.
The MLED framework is mathematically defined as MLED(n, P), where n represents the number of hierarchical levels (n≥3) and P denotes the comprehensive set of operational policies. Configurations with n<3 lack the recursive structure that fundamentally distinguishes MLED from traditional network architectures.
Each level i comprises j layers, with layer Lij governed by corresponding policy Pij∈P. The recursive structure mandates that layers at level i maintain smaller or equal scope compared to layers at level i+1. Framework parameters n, j, and P are dynamically determined based on network conditions and user-specified admissible UEP threshold γ.
This modular and decoupled approach enables the integration of additional communication policies while maintaining the core recursive architecture, making MLED adaptable to diverse network environments and application requirements.
The MLED framework delivers substantial improvements in data transmission reliability through its innovative multi-layer error detection approach. Comprehensive testing and validation have demonstrated the framework's effectiveness across diverse network conditions and application scenarios.
Errors are systematically intercepted across multiple hierarchical layers before final delivery, significantly reducing the probability of corrupt file transmission reaching the destination application.
Early detection capabilities enable targeted, localized retransmissions rather than complete file retransfers, minimizing network overhead and improving overall transfer efficiency.
Rigorous experimental validation conducted on the FABRIC research infrastructure demonstrates measurable reductions in undetected error rates and retransmission overhead under real-world conditions.
Complete experimental setup documentation, analysis tools, and reproducible artifacts are available through the FABRIC testbed, enabling researchers to validate and extend the framework.
Research Support: This work is supported in part by NSF grants CNS-2215671 and CNS-2215672, demonstrating the framework's significance for advancing network communication reliability in scientific and data-intensive computing environments.
C++ core with Python tooling, configurable 3-7+ hierarchical levels supporting error detection, routing, flow control, and congestion management policies.
Cross-platform support for Linux, Windows, and HPC environments with TCP/IP, UDP, and custom protocol integration.
Supported by NSF grants CNS-2215671 and CNS-2215672, with comprehensive validation on FABRIC testbed infrastructure.