Multi-Level Error Detection (MLED) Framework

The Multi-Level Error Detection (MLED) framework, denoted MLED(n, P), is a novel, recursive architecture designed to significantly reduce the probability of undetected errors (UEP) during large-scale file transfers by leveraging in-network resources. This is especially critical for scientific and data-intensive applications, where ensuring data integrity is essential for reliable and reproducible analysis. The framework is designed to be modular and decoupled, allowing for easy integration with existing file transfer protocols. The current implementation focuses on error detection, but the architecture is flexible enough to support other functionalities in the future.

Key Features:

Recursive Layered Design: MLED is composed of n ≥ 3 levels, with each level consisting of multiple layers. Every layer j at level i represented by L(i,j) is governed by a policy P(i,j) ∈ P, which defines operational behavior over its scope.
Policy-Driven Operation: Policies can be designed to manage communication functionalities such as error detection, routing, addressing, congestion control, and flow control. While the current implementation supports only error detection, the modular and decoupled architecture allows for conceptual support and future integration of other policy types.
Hierarchical Scope Management: Layers at level i operate at an equal or smaller scope than those at level i+1, enabling incremental and early in-network error interception across levels.
Compatibility and Flexibility: MLED is highly configurable and it can replicate existing file transfer frameworks while improving error detection through additional checks at intermediate layers.

Benefits:

Reduced Undetected Errors: Errors are intercepted across multiple layers before final delivery, reducing the chance of corrupt file transmission.
Efficient Recovery: Early detection enables localized retransmissions, minimizing the need for full file retransfers.
Validation & Experimentation: The MLED framework has been mathematically formulated and experimentally validated on the FABRIC testbed, demonstrating measurable reductions in undetected errors and retransmission overhead.

Validation & Experimentation:

The MLED framework has been mathematically formulated and experimentally validated on the FABRIC testbed, demonstrating measurable reductions in undetected errors and retransmission overhead.

You can access the experimental setup, instructions, and analysis tools here:

FABRIC Artifact & Setup: https://artifacts.fabric-testbed.net/artifacts/eecb2b0b-5a42-4e5f-ace0-a01d59b46dff

MLED Architecture

The Multi-Level Error Detection framework, MLED(n, P), is defined by n≥3 levels and a set of policies P. A configuration with n<3 reflects either the traditional network stack or lacks the recursive structure that distinguishes MLED. Each level i is made up of j layers, and each layer Lij is governed by a corresponding configurable policy Pij∈P which dictates the operations of the layer over its scope. The policies define parameters and configurations for error detection, routing, addressing, congestion control, flow control, and other communication functions. With this approach, MLED decouples these functions and provides greater flexibility and modularity. The recursive structure dictates that layers at level i have a smaller or equal scope than the layers at level i+1. The values of n, j, and P are determined by network conditions and the user specified admissible UEP γ

MLED Architecture

The MLED framework is designed to be modular and decoupled, allowing for the integration of additional policies in the future. The current implementation focuses on error detection, but the architecture can be extended to include other communication functionalities as needed. This flexibility allows MLED to adapt to various network conditions and requirements, making it a versatile solution for enhancing data integrity in large-scale file transfers.

The MLED framework is a promising approach to improving data integrity in large-scale file transfers. By leveraging in-network resources and a recursive layered design, MLED can significantly reduce the probability of undetected errors, making it an essential tool for scientific and data-intensive applications.

For more information on the MLED framework and its implementation, please refer to the following resources:

This work is supported in part by NSF grants CNS-2215671 and CNS-2215672.

If you have any questions or would like to discuss the MLED framework further, please feel free to contact me.