OBSERVABILITY COMPENSATION PARADIGM: LEVERAGING ADAPTIVE EXECUTION TRACING AND ANALYSIS

分布式系统以云服务、物联网设备和智能车辆的形式出现，越来越多地被各个行业部门以及普通公众采用。在这些系统中，一项简单的任务，如询问天气或进行在线金融交易，可能涉及在不同节点上运行的几个并行模块。在重复实例化任务时，可以在完全不同的一组节点上执行相同的操作。要观察执行的正确性，或诊断运行时问题(如延迟)的根本原因，应该观察和跟踪几个节点和执行层，这可能会导致大量的CPU、内存和存储开销。此外，处理和分析这一潜在的大型跟踪数据本身可能具有挑战性，因为它需要努力将收集的数据关联、建模、分析和理解为一个整体，特别是对于实时生产系统。因此，需要新的方法在追踪的范围和分辨率与其实现的可观察性水平和对整个系统造成的管理费用之间取得平衡。拟议研究的长期目标是提出一种新的范式，能够根据手头的目标自适应地定制软件系统的可观察性。为了实现这一目标，第一个短期目标是设计算法和战略，以便根据学习到的概况或正在调查的系统的当前行为，动态地自适应地收集数据，并动态地优化收集的数据量、速度和分辨率。这将确保建议的方法将收集关于运行时操作和问题的足够数据，以执行正确性验证和问题分析。第二个目标是开发基于增量式机器学习的跟踪分析和处理模块，以有效地建模和理解运行时系统行为，并为自适应跟踪系统提供输入，以便迭代和在线调整跟踪收集。这将确保系统的可观测性是可取的，同时其运行性能保持在可接受的范围内，尽管存在跟踪开销。最后，第三个目标是开发一些方法和算法来关联性能变化和提取的运行时行为模型，以用于性能错误行为的根本原因分析。这项工作的意义和独创性在于解决了软件开发人员和管理员目前在其系统的可观察性方面所面临的基本挑战。许多加拿大公司，从IT和电话提供商到金融、媒体、交通和能源，已经转向云、物联网和边缘服务，并将通过提高软件可观察性和效率以及降低维护成本，从本研究的建议方法和结果中受益。

Distributed systems have been increasingly adopted by various industry sectors as well as the general public, emerging in the form of cloud services, IoT devices, and smart vehicles. In these systems, a simple task such as asking about the weather or conducting an online financial transaction can involve several parallel modules running on various nodes. Upon repeated instantiations of a task, the same operation may be executed over a completely different set of nodes. To observe the execution correctness, or diagnose the root cause of runtime issues such as a delay, several nodes and execution layers should be observed and traced, potentially incurring large CPU, memory, and storage overhead. Additionally, processing and analyzing this potentially large trace data can be challenging in itself as it requires effort to correlate the collected data, model, analyze, and understand it as a whole, especially for live production systems. Therefore, there is a need for new methodologies to strike a balance between the scope and resolution of tracing with the level of observability it achieves and the overhead it incurs on the system as a whole. The long-term objective of the proposed research is to present a new paradigm that enables adaptive tailoring of software system observability according to the objectives at hand. To achieve this, the first short term objective is to devise algorithms and strategies for dynamic adaptive data collection as well as dynamic optimization of the amount, speed and resolution of the collected data, based on the learned profile or the current behavior of the system under investigation. This will ensure that the proposed methods will collect just enough data around the runtime operations and problems to perform correctness validation and problem analysis. The second objective is to develop incremental machine learning based trace analysis and processing modules to efficiently model and understand runtime system behavior as well as provide input to the adaptive tracing system for the iterative and online adjustment of trace collection. This will ensure that system observability is desirable while its operational performance is maintained within an acceptable range despite the tracing overhead. Finally, the third objective is to develop a number of methods and algorithms to correlate the changes in performance and the extracted runtime behavioral models to be used for root cause analysis of performance misbehavior. The significance and originality of this work are found in addressing the fundamental challenges currently faced by software developers and administrators in the observability of their systems. Many Canadian companies, from IT and phone providers to finance, media, transportation, and energy have already moved to Cloud, IoT and Edge services, and would benefit from the proposed methods and results of this research, through increased software observability and efficiency, and decreased maintenance costs.