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伸缩性的需求不断增长，因此分布式软件系统越来越开发和部署。当今使用的大多数关键软件和服务（例如金融系统和医疗网络）本质上都是分布式系统。因此，这些系统的质量，包括各种因素（例如，可靠性和安全性）对现代社会和经济至关重要。动态程序分析是一种模拟使用其执行信息的程序行为的方法和原因，它一直是强大质量保证工具支持的关键推动力。但是，已知传统的动态分析由于其大量开销而面临可伸缩性挑战。平衡分析的有效性（例如精度）和成本（例如时间），这也是一个挑战，正如许多分析技术中所反映的，这些技术有效但没有提供实际上有用的精度和那些精确的精确性水平，而这些精度是非常精确的，但以无法接受的成本。对于分布式软件的动态分析，由于通常具有无限制的执行信息，因此这些挑战通常是较大的代码大小和更大的复杂性，这是因为分布式系统通常以连续（不间断）服务运行的事实。该项目将通过调查自适应动态分析来解决这些挑战，这是动态计划分析的根本新范式，该分析不断调整其成本和有效性，以在用户指定的预算范围内进行最佳的权衡。这种新的范式及其出色的可扩展性和成本效益最佳性，尤其是在富有挑战性的分布式软件背景下，动态分析中的最新状态将大大提高。该项目将开发自适应动态分析的基础基础，包括（1）由混合依赖性建模和内置的成本效益模型的综合动态分析基础架构的制定，以及（2）自适应和分布式动态分析算法的设计，这些算法的重点是依从性依从性型和指导性的依从性。与常规的动态分析相比，通常在整个分析中采用固定算法配置，研究的框架利用了复杂性的差异，因此分析中的分析中的差异（对于相同的精度），不同程序的不同区域和程序执行的不同部分。这些差异将通过基础架构中的各种监视实用程序来感知这些差异，并利用以调整算法配置（例如，分析使用的动态数据的粒度和选择）。通过智能使用各种程序信息和分析配置，新框架将提供灵活的成本效益余额，以满足各种预算需求。同时，它将通过对分布式分析的自动分布式控制来实现高可扩展性。通过在运行时做出明智的决策，分析将实现并维持最佳的成本效益权衡方面的折衷方案（例如，资源限制）和在连续系统执行期间不断变化的运行时环境条件变化。这项奖项反映了NSF的法定任务，并被认为是通过基金会的智力功能和广泛影响的评估来评估NSF的法定任务。

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

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

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

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

Scaling application-level dynamic taint analysis to enterprise-scale distributed systems

将应用程序级动态污点分析扩展到企业级分布式系统

• DOI: 10.1145/3377812.3390910
• 发表时间: 2020
• 期刊: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings
• 作者: Fu, Xiaoqin;Cai, Haipeng
• 通讯作者: Cai, Haipeng