D$^2$ABS: A Framework for Dynamic Dependence Abstraction of Distributed Programs

TL;DR

D$^2$ABS is a dynamic dependence abstraction framework for distributed programs that infers method-level dependencies across processes, improving analysis effectiveness over existing methods with flexible precision and efficiency tradeoffs.

Contribution

The paper introduces D$^2$ABS, a novel framework for dynamic dependence analysis in distributed systems, incorporating partial-ordering, causality inference, and multiple instantiations for tailored tradeoffs.

Findings

D$^2$ABS outperforms existing dependence analysis tools in effectiveness.

The framework provides customizable tradeoffs between efficiency and precision.

Empirical evaluations demonstrate scalability across various distributed architectures.

Abstract

As modern software systems are increasingly developed for running in distributed environments, it is crucial to provide fundamental techniques such as dependence analysis for checking, diagnosing, and evolving those systems. However, traditional dependence analysis is either inapplicable or of very limited utility for distributed programs due to the decoupled components of these programs that run in concurrent processes at physically separated machines. Motivated by the need for dependence analysis of distributed software and the diverse cost-effectiveness needs of dependence-based applications, this paper presents D$^2$ABS, a framework of dynamic dependence abstraction for distributed programs. By partial-ordering distributed method-execution events and inferring causality from the ordered events, D$^2$ABS abstracts method-level dependencies both within and across process boundaries. Further, by exploiting message-passing semantics across processes, and incorporating static dependencies and statement coverage within individual components, we present three additional instantiations of D$^2$ABS that trade efficiency for better precision. We present the design of the D$^2$ABS framework and evaluate the four instantiations of D$^2$ABS on distributed systems of various architectures and scales using our implementation for Java. Our empirical results show that D$^2$ABS is significantly more effective than existing options while offering varied levels of cost-effectiveness tradeoffs. As our framework essentially computes whole-system run-time dependencies, it naturally empowers a range of other dependence-based applications.