PROACTIVE COMPUTING PARADIGM APPLIED TO THE PROGRAMMING OF ROBOTIC SYSTEMS

Doctoral thesis (2023)

This doctoral thesis is concerned with the development of advanced software for robotic systems, an area still in its experimental infancy, lacking essential methodologies from generic software ... [more ▼]

This doctoral thesis is concerned with the development of advanced software for robotic systems, an area still in its experimental infancy, lacking essential methodologies from generic software engineering. A significant challenge within this domain is the absence of a well-established separation of concerns from the design phase. This deficiency is exemplified by Navigation 2, a realworld reference application for (semi-) autonomous robot journeys developed for and on top of the Robot Operating System (ROS): the project’s leading researchers encountered difficulties in maintaining and evolving their complex software, even for supposed-to-be straightforward new functions, leading to a halt in further development. In response, this thesis first presents an alternative design and implementation approach that not only rectifies the issues but also elevates the programming level of consistent robot behaviors. By leveraging the proactive computing paradigm, our dedicated software engineering model provides programmers with enhanced code extension, reusability and maintenance capabilities. Furthermore, a key advantage of the model lies in its dynamic adaptability via on-the-fly strategy change in decision-making. Second, in order to provide a comprehensive evaluation of the two systems, an exhaustive comparative study between Navigation 2 and the same application implemented along the lines of our model, is conducted. This study covers thorough assessments at both compile-time and runtime. Software metrics such as coupling, lack of cohesion, complexity, and various size measures are employed to quantify and visualize code quality and efficiency attributes. The CodeMR software tool aids in visualizing these metrics, while runtime analysis involves monitoring CPU and memory usage through the Datadog monitoring software. Preliminary findings indicate that our implementation either matches or surpasses Navigation 2’s performance while simultaneously enhancing code structure and simplifying modifications and extensions of the code base. [less ▲]

AIR: A Light-Weight Yet High-Performance Dataflow Engine based on Asynchronous Iterative Routing

in AIR: A Light-Weight Yet High-Performance Dataflow Engine based on Asynchronous Iterative Routing (2020, September 01)

Distributed Stream Processing Engines (DSPEs) are currently among the most emerging topics in data management, with applications ranging from real-time event monitoring to processing complex dataflow ... [more ▼]

Distributed Stream Processing Engines (DSPEs) are currently among the most emerging topics in data management, with applications ranging from real-time event monitoring to processing complex dataflow programs and big data analytics. In this paper, we describe the architecture of our AIR engine, which is designed from scratch in C++ using the Message Passing Interface (MPI), pthreads for multithreading, and is directly deployed on top of a common HPC workload manager such as SLURM. AIR implements a light-weight, dynamic sharding protocol (referred to as “Asynchronous Iterative Routing”), which facilitates a direct and asynchronous communication among all worker nodes and thereby completely avoids any additional communication overhead with a dedicated master node. With its unique design, AIR fills the gap between the prevalent scale-out (but Java-based) architectures like Apache Spark and Flink, on one hand, and recent scale-up (and C++ based) prototypes such as StreamBox and PiCo, on the other hand. Our experiments over various benchmark settings confirm that AIR performs as good as the best scale-up SPEs on a single-node setup, while it outperforms existing scale-out DSPEs in terms of processing latency and sustainable throughput by a factor of up to 15 in a distributed setting. [less ▲]