Log Parsing Evaluation in the Era of Modern Software Systems

Due to the complexity and size of modern software systems, the amount of logs generated is tremendous. Hence, it is infeasible to manually investigate these data in a reasonable time, thereby requiring automating log analysis to derive insights about the functioning of the systems. Motivated by an industry use-case, we zoom-in on one integral part of automated log analysis, log parsing, which is the prerequisite to deriving any insights from logs. Our investigation reveals problematic aspects within the log parsing field, particularly its inefficiency in handling heterogeneous real-world logs. We show this by assessing the 14 most-recognized log parsing approaches in the literature using (i) nine publicly available datasets, (ii) one dataset comprised of combined publicly available data, and (iii) one dataset generated within the infrastructure of a large bank. Subsequently, toward improving log parsing robustness in real-world production scenarios, we propose a tool, Logchimera, that enables estimating log parsing performance in industry contexts through generating synthetic log data that resemble industry logs. Our contributions serve as a foundation to consolidate past research efforts, facilitate future research advancements, and establish a strong link between research and industry log parsing

Log Parsing Evaluation in the Era of Modern Software Systems

Due to the complexity and size of modern software systems, the amount of logs generated is tremendous. Hence, it is infeasible to manually investigate these data in a reasonable time, thereby requiring automating log analysis to derive insights about the functioning of the systems. Motivated by an industry use-case, we zoom-in on one integral part of automated log analysis, log parsing, which is the prerequisite to deriving any insights from logs. Our investigation reveals problematic aspects within the log parsing field, particularly its inefficiency in handling heterogeneous real-world logs. We show this by assessing the 14 most-recognized log parsing approaches in the literature using (i) nine publicly available datasets, (ii) one dataset comprised of combined publicly available data, and (iii) one dataset generated within the infrastructure of a large bank. Subsequently, toward improving log parsing robustness in real-world production scenarios, we propose a tool, Logchimera, that enables estimating log parsing performance in industry contexts through generating synthetic log data that resemble industry logs. Our contributions serve as a foundation to consolidate past research efforts, facilitate future research advancements, and establish a strong link between research and industry log parsing

Concierge: A Service Platform for Resource-Constrained Devices

As mobile and embedded devices become widespread, the management and configuration of the software in the devices is increasingly turning into a critical issue. OSGi is a business standard for the life cycle management of Java software components. It is based on a service oriented architecture where functional units are decoupled and components can be managed independently of each other. However, the focus continuously shifts from the originally intended area of small and embedded devices towards large-scaled enterprise systems. As a result, implementations of the OSGi framework are increasingly becoming more heavyweight and less suitable for smaller computing devices. In this paper, we describe the experience gathered during the design of Concierge, an implementation of the OSGi specification tailored to resource-constrained devices. Comprehensive benchmarks show that Concierge performs better than existing implementations and consumes less resources

Self-adaptive Executors for Big Data Processing

The demand for additional performance due to the rapid increase in the size and importance of data-intensive applications has considerably elevated the complexity of computer architecture. In response, systems offer pre-determined behaviors based on heuristics and then expose a large number of configuration parameters for operators to adjust them to their particular infrastructure. Unfortunately, in practice this leads to a substantial manual tuning effort. In this work, we focus on one of the most impactful tuning decisions in big data systems: the number of executor threads. We first show the impact of I/O contention on the runtime of workloads and a simple static solution to reduce the number of threads for I/O-bound phases. We then present a more elaborate solution in the form of self-adaptive executors which are able to continuously monitor the underlying system resources and detect contentions. This enables the executors to tune their thread pool size dynamically at runtime in order to achieve the best performance. Our experimental results show that being adaptive can significantly reduce the execution time especially in I/O intensive applications such as Terasort and PageRank which see a 34% and 54% reduction in runtime.</p