Cyber physical systems (CPS) and Internet of Objects (IoT) are generating an unprecedented volume and variety of data that needs to be collected and stored on the cloud before being processed. By the time the data makes its way to the cloud for analysis, the opportunity to trigger a reply might be late. One approach to solve this problem is to analyze the most time-sensitive data at the network edge, close to where it is generated. Thus, only the pre-processed results are sent to the cloud. This computation model is know as *Fog Computing* or *Edge computing*. Critical CPS applications using the fog computing model may have real-time constraints because results must be delivered in a pre-determined time window. Furthermore, in many relevant applications of CPS, the processing can be parallelized by applying the same processing on different sub-sets of data at the same time by the mean parallel programming techniques. This allow to achieve a shorter response time, and then, a larger slack time, which can be used to reduce energy consumption. In this thesis we focus on the problem of scheduling a set of parallel tasks on multicore processors, with the goal of reducing the energy consumption while all deadlines are met. We propose several realistic task models on architectures with identical and heterogeneous cores, and we develop algorithms for allocating threads to processors, select the core frequencies, and perform schedulability analysis. The proposed task models can be realized by using OpenMP-like APIs.
Directeurs de thèse : Richard OLEJNIK, Abou El-Hassen BENYAMINA Rapporteurs : Joel GOOSSENS, Mohamed BENYETTOU Examinateurs : Giuseppe LIPARI, Sophie QUINTON, Yahia LEBBAH, Samira CHOURAQUI
Thesis of the team Émeraude defended on 02/11/2016