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

由于对计算性能和可扩展性的要求越来越高，分布式软件系统被越来越多地开发和部署。目前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。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利用程序的执行信息对其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，众所周知，传统的动态分析由于其巨大的开销而受到可伸缩性方面的挑战。平衡分析的有效性（例如，精度）和成本（例如，时间）也是一个长期的挑战，正如许多分析技术所反映的那样，这些分析技术是有效的，但没有提供实际有用的精度水平，而那些分析技术是有用的，但成本是不可接受的。对于分布式软件的动态分析来说，由于这些软件系统通常具有较大的代码大小和更大的复杂性，以及由于分布式系统通常作为连续（不间断）服务运行而导致的无界执行信息，这些挑战都加剧了。该项目将通过研究自适应动态分析来解决这些挑战，这是一种全新的动态计划分析范式，可以在用户指定的预算范围内不断调整其成本和有效性以达到最佳权衡。这种新范式及其优越的可扩展性和成本效益的最优性，特别是在分布式软件的具有挑战性的环境中，将大大提高动态分析的技术水平。本项目将发展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)设计自适应和分布式动态分析算法，重点关注依赖抽象，由基础设施授权并以成本效益模型为指导。传统的动态分析通常在整个分析过程中采用固定的算法配置，与之相比，所研究的框架利用了程序不同区域和程序执行不同段的复杂性差异，以及相应的分析开销差异（在相同精度水平下）。这些差异将通过基础设施中的各种监控实用程序来感知，并利用它们来调整算法配置（例如，分析使用的动态数据的粒度和选择）。通过智能地使用各种项目信息和分析配置，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，使系统具有较高的可扩展性。通过在运行时做出明智的决策，分析将在给定的约束（例如，资源限制）和连续系统执行期间不断变化的运行时环境条件方面实现并维持最佳的成本效益权衡。该奖项反映了美国国家科学基金会的法定使命，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。

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.