Resilience through Automated Adaptive Configuration for Distribution and Replication

TL;DR

This paper introduces an automated framework that enhances system resilience by optimizing the distribution and replication of software components across heterogeneous hardware, using a novel state-space exploration algorithm.

Contribution

The paper presents a new algorithm for resilient configuration and reconfiguration of software systems, incorporating a quotient reduction technique for efficient state-space exploration.

Findings

Successfully applied to an autonomous driving system model

Demonstrated improved resilience through adaptive reconfiguration

Efficient state-space exploration with quotient reduction

Abstract

This paper presents a powerful automated framework for making complex systems resilient under failures, by optimized adaptive distribution and replication of interdependent software components across heterogeneous hardware components with widely varying capabilities. A configuration specifies how software is distributed and replicated: which software components to run on each computer, which software components to replicate, which replication protocols to use, etc. We present an algorithm that, given a system model and resilience requirements, (1) determines initial configurations of the system that are resilient, and (2) generates a reconfiguration policy that determines reconfiguration actions to execute in response to failures and recoveries. This model-finding algorithm is based on state-space exploration and incorporates powerful optimizations, including a quotient reduction based on a novel equivalence relation between states. We present experimental results from successfully applying a prototype implementation of our framework to a model of an autonomous driving system.