Scaling, Packetizers and Aggregation in Network Calculus

Real-time systems often consist of numerous sub-systems engaged in extensive data exchange. Despite their complexity, a critical challenge lies in effectively incorporating real-time constraints within these systems. To address this challenge, designers typically conduct analyses to establish upper bounds on delays, ensuring they remain within the deadlines of incoming requests. However, adopting a pessimistic approach often results in over-dimensioning the systems. Then, to reduce the pessimism, we want to take into account the fact that a subsystem cannot propagate more requests/data than it can execute. This phenomenon is well-known in network analysis as it reduces the burst of data. As a consequence, this notion is easier to grasp in theories developed to compute delay bounds in networks. That is why we choose, in this paper, to perform the analysis using the Network Calculus theory, since it offers the possibility to easily aggregate flows (i.e. sum flows) and then take into account the phenomenon of smoothing the traffic. To handle tasks and networks, our model relies on packetization and workload scaling. In this paper, we improve some results regarding the already existing elements of Network Calculus and the aggregation. Also, we update and complete definitions and results related to workload scaling.