D$^2$ABS: A Framework for Dynamic Dependence Abstraction of Distributed Programs
This addresses the need for fundamental techniques like dependence analysis in distributed software systems, enabling checking, diagnosing, and evolving such systems, though it appears incremental as it builds on existing concepts with new instantiations.
The paper tackles the problem of dependence analysis for distributed programs, which is challenging due to decoupled components, by presenting D^2ABS, a framework for dynamic dependence abstraction. The result shows that D^2ABS is significantly more effective than existing options, offering varied cost-effectiveness tradeoffs in evaluations on distributed systems of various architectures and scales.
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.