SHF: EAGER: Towards Self-Adaptive Dynamic Analysis for Distributed Software

SHF：EAGER：面向分布式软件的自适应动态分析

• 批准号: 1936522
• 负责人: Haipeng Cai
• 金额: $ 15万
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1936522&HistoricalAwards=false
• 关键词: SHF; EAGER; Towards; Self; Adaptive

Due to growing demands for computing performance and scalability, distributed software systems are increasingly developed and deployed. Most of the critical software and services being used today, such as financial systems and medical networks, are distributed systems in nature. The quality, including various factors (e.g., reliability and security), of these systems is thus of paramount importance to the modern society and economy. Dynamic program analysis, a methodology that models and reasons about the behavior of programs using their execution information, has been a key enabler for powerful quality assurance tool support. However, conventional dynamic analysis is known to suffer from scalability challenges due to its substantial overheads. It also has been a standing challenge to balance the effectiveness (e.g., precision) and cost (e.g., time) of the analysis, as reflected in many analysis techniques that are efficient but do not provide a practically useful level of precision and those that are usefully precise but at unacceptable cost. To dynamic analysis of distributed software, these challenges are exacerbated because of the typically large code size and greater complexity of those software systems, in addition to unbounded execution information as a result of the fact that distributed systems commonly run as continuous (uninterrupted) services. This project will address these challenges by investigating self-adaptive dynamic analysis, a fundamentally new paradigm of dynamic program analysis, which continuously adapts its cost and effectiveness to the optimal tradeoff within user-specified budget bounds. The state of the art in dynamic analysis will be significantly advanced by this new paradigm and its superior scalability and cost-effectiveness optimality, especially in the challenging context of distributed software. This project will develop the foundational underpinning of self-adaptive dynamic analysis, including (1) the formulation of an integrated dynamic analysis infrastructure featured by hybrid dependence modeling and a built-in cost-benefit model, and (2) the design of self-adaptive and distributed dynamic-analysis algorithms focusing on dependence abstraction as empowered by the infrastructure and guided by the cost-benefit model. Compared to conventional dynamic analysis, which commonly adopts a fixed algorithmic configuration throughout the entire analysis, the studied framework exploits differences in the complexity, and accordingly those in the analysis overheads (for the same level of precision), of different regions of programs and different segments of program executions. These differences will be sensed through various monitoring utilities in the infrastructure and leveraged to adjust the algorithmic configuration (e.g., granularity and selection of the dynamic data used by the analysis). With intelligent uses of assorted program information and analysis configurations, the new framework will provide flexible cost-effectiveness balances to meet diverse budgetary needs. Meanwhile, it will attain high scalability through automatic, distributed control of the distributed analysis. By making smart decisions at runtime, the analysis will achieve and sustain optimal cost-benefit tradeoffs with respect to given constraints (e.g., resources limits) and changing run-time environment conditions during continuous system executions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

由于对计算性能和可扩展性的需求不断增长，分布式软件系统的开发和部署越来越多。今天使用的大多数关键软件和服务，如金融系统和医疗网络，本质上都是分布式系统。质量，包括各种因素（例如，因此，这些系统的可靠性和安全性对于现代社会和经济至关重要。动态程序分析是一种利用程序的执行信息对程序的行为进行建模和推理的方法，它已经成为强大的质量保证工具支持的关键推动者。然而，传统的动态分析是已知的，遭受可扩展性的挑战，由于其大量的开销。它也一直是一个长期的挑战，以平衡的有效性（例如，精度）和成本（例如，时间）的分析，如反映在许多分析技术，是有效的，但不提供实际有用的精度水平和那些有用的精度，但在不可接受的成本。对于分布式软件的动态分析，这些挑战由于这些软件系统的通常大的代码大小和更大的复杂性而加剧，此外，由于分布式系统通常作为连续（不间断）服务运行的事实而导致的无界执行信息。本项目将通过研究自适应动态分析来解决这些挑战，自适应动态分析是动态程序分析的一种全新范式，它在用户指定的预算范围内不断调整其成本和有效性以实现最佳权衡。这种新的范例及其上级可扩展性和成本效益最优性将大大提高动态分析的技术水平，特别是在分布式软件的挑战性环境中。本项目将开发自适应动态分析的基础支撑，包括（1）以混合依赖建模和内置成本效益模型为特征的集成动态分析基础设施的制定，以及（2）以基础设施为授权并以成本效益模型为指导的自适应和分布式动态分析算法的设计，重点是依赖抽象。传统的动态分析，通常采用固定的算法配置在整个分析相比，所研究的框架利用的复杂性的差异，因此，在分析开销（相同的精度水平），不同区域的程序和程序执行的不同部分。这些差异将通过基础设施中的各种监视实用程序来感测，并被用来调整算法配置（例如，分析所使用的动态数据的粒度和选择）。通过智能使用各种计划信息和分析配置，新框架将提供灵活的成本效益平衡，以满足不同的预算需求。同时，通过对分布式分析的自动化、分布式控制，使系统具有很高的可扩展性。通过在运行时做出明智的决策，分析将实现并维持关于给定约束的最佳成本效益权衡（例如，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Dads: dynamic slicing continuously-running distributed programs with budget constraints

爸爸：动态切片连续运行的分布式程序，有预算限制

• DOI: 10.1145/3368089.3417920
• 发表时间: 2020
• 期刊: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
• 作者: Fu, Xiaoqin;Cai, Haipeng;Li, Li
• 通讯作者: Li, Li

A dynamic taint analyzer for distributed systems

• DOI: 10.1145/3338906.3341179
• 发表时间: 2019-08
• 期刊: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
• 作者: Xiaoqin Fu;Haipeng Cai

FlowDist: Multi-Staged Refinement-Based Dynamic Information Flow Analysis for Distributed Software Systems

• 发表时间: 2021
• 作者: Xiaoqin Fu;Haipeng Cai

On the scalable dynamic taint analysis for distributed systems

分布式系统的可扩展动态污点分析

• DOI: 10.1145/3338906.3342506
• 发表时间: 2019
• 期刊: ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE
• 作者: Fu, Xiaoqin

S EADS: Scalable and Cost-effective Dynamic Dependence Analysis of Distributed Systems via Reinforcement Learning

S EADS：通过强化学习对分布式系统进行可扩展且经济高效的动态依赖分析

• DOI: 10.1145/3379345
• 发表时间: 2021
• 期刊: ACM Transactions on Software Engineering and Methodology
• 影响因子: 4.4
• 作者: Fu, Xiaoqin;Cai, Haipeng;Li, Wen;Li, Li
• 通讯作者: Li, Li