OBSERVABILITY COMPENSATION PARADIGM: LEVERAGING ADAPTIVE EXECUTION TRACING AND ANALYSIS

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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.