# On the Robustness of Distributed Computing Networks

## TL;DR

This paper investigates the robustness of distributed computing networks against failures in communication and computation resources, introducing new metrics and algorithms to evaluate and optimize network resilience.

## Contribution

It defines novel cut metrics for network connectivity, analyzes the flow-cut gap, and develops mathematical and approximation algorithms for optimal network interdiction.

## Key findings

- Non-zero flow-cut gap identified in the network
- Mathematical programs for optimal interdiction developed
- Polynomial-time approximation algorithms achieve near-optimal results

## Abstract

Traffic flows in a distributed computing network require both transmission and processing, and can be interdicted by removing either communication or computation resources. We study the robustness of a distributed computing network under the failures of communication links and computation nodes. We define cut metrics that measure the connectivity, and show a non-zero gap between the maximum flow and the minimum cut. Moreover, we study a network flow interdiction problem that minimizes the maximum flow by removing communication and computation resources within a given budget. We develop mathematical programs to compute the optimal interdiction, and polynomial-time approximation algorithms that achieve near-optimal interdiction in simulation.