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Daniel Casini, Scuola Superiore Sant'Anna of Pisa, Italy, OH14, E04

Event Date: January 23, 2020 16:15

New models and analyses for contemporary real-time workloads

Nowadays, real-time workloads are becoming always more computationally demanding, giving rise to the need to adopt more powerful computing platforms. This is the case of multi-core systems: nevertheless, their adoption increases the analysis complexity due to multiple sources of unpredictability. To exploit the available computational power, tasks running upon multi-core platforms are often characterized by a parallel structure and non-trivial dependencies. The analysis complexity is further exacerbated by the scheduling effects imposed by the operating systems and, sometimes, by middleware frameworks that handle the actual workload on behalf of the operating system. As a consequence, analyzing a modern real-time system is always becoming more complex, hence requiring new models and analysis techniques. This talk addresses these issues from different perspectives.
In the first part of the talk, an overview of how to model and analyze complex contemporary workloads is given. First, dynamic workloads are addressed, where tasks can join and leave while the system is operating. Then, how specific frameworks can affect the timing of applications is discussed, targeting the Robotics Operating System (ROS) and Tensorflow.
In the second part of the talk, models in which tasks are represented as direct acyclic graphs (DAGs) are considered, and methods for guaranteeing both timing constraints and memory feasibility are presented. In particular, solutions for bounding the worst-case memory space requirement for parallel tasks running on multi-core platforms with scratchpad memories are discussed.

Short bio:
Daniel Casini is a Ph.D. Candidate at the Real-Time Systems (ReTiS) Laboratory of the Scuola Superiore Sant'Anna of Pisa, working under the supervision of Prof. Alessandro Biondi and Prof. Giorgio Buttazzo.
He graduated (cum laude) in Embedded Computing Systems Engineering, a Master's degree jointly offered by the Scuola Superiore Sant'Anna of Pisa and University of Pisa.
His research interests include software predictability in multi-processor systems, schedulability analysis, synchronization protocols, and the design and implementation of real-time operating systems and hypervisors.

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