A DataCentric Approach to Distributed Tracing Modern applications are often implemented as distributed systems consisting of multiple application layers and spread across many machines. Monitoring and diagnosing are fundamental challenges of such systems, and many solutions have been proposed. They all aim to abstract the distributed nature and offer a concise overview of the system. Distributed logging, monitoring and tracing solutions help with root cause analysis or assessing the performance of the system. In data processing situations, data is the main driver and it should be treated accordingly. Microservices are often used to implement highly distributed data processing systems, so troubleshooting from a data point of view is also difficult. Data should be monitored and traced across machines and applications in order to get reliable insights into how the data is stored and processed. However, existing approaches to distributed tracing are based on tracing requests that are propagated throughout the system instead on their content. In this work we take a datacentric perspective to tracing and data processing to investigate how content can be traced in a distributed system. We evaluate existing tracing approaches to see if they can be extended to the content itself, then we take into consideration new ones. We implement three different approaches to datacentric distributed tracing in a highly distributed data processing system built using microservices and discuss their advantages and disadvantages.

DLA: Detecting and Localizing Anomalies in Containerized Microservice Architectures Using Markov Models Containerbased microservice architectures are emerging as a new approach for building distributed applications as a collection of independent services that works together. As a result, with microservices, we are able to scale and update their applications based on the load attributed to each service. Monitoring and managing the load in a distributed system is a complex task as the degradation of performance within a single service will cascade reducing the performance of other dependent services. Such performance degradations may result in anomalous behaviour observed for instance for the response time of a service. This paper presents a Detection and Localization system for Anomalies (DLA) that monitors and analyzes performancerelated anomalies in containerbased microservice architectures. To evaluate the DLA, an experiment is done using R, Docker and Kubernetes, and different performance metrics are considered. The results show that DLA is able to accurately detect and localize anomalous behaviour.