Conventional CAN bus schedulability analysis as-sumes that all messages with a given identifier have the same worst-case length. In this paper we extend that analysis to a more general model in which messages with a given identifier may have different lengths, that vary according to a known periodic pattern. That is, for some positive integer$s$, we assume that the length of message instances$n$and$n+S$with the same id is the same. By leveraging such patterns, where present, our new analysis allows for a more efficient use of CAN bus bandwidth than the application of conventional analysis, which can be pessimistic. This may be interesting when a given node sends the values of multiple signals with different periods. In such a scenario, the conventional CAN schedulability analysis would require either the use of different ids for different signals (assuming there are enough of them), which leads to a higher bandwidth overhead because of the reduplication of message headers, or using only one id, but pessimistically always assuming the maximum possible length of the message, for safety reasons.

Multicore architectures are mainstream due to ever increasing demand of throughput by modern applications. However, the suboptimal utilization of available resources in these architectures may imply an inevitable energy overhead. This energy overhead can only be avoided if the multicore systems support reconfiguration of available resources as per application demand. To achieve the target objectives (i.e., Energy efficiency with Throughput maximization) in multicore systems, many decision variables need to be optimized or analyzed to find the better trade-off. Heuristic-based approaches are aimed to provide a good-enough solution instead of a lengthy exhaustive search. This paper presents an Evolutionary Algorithm (EA)-based approach, i.e., Nondominated Sorting Genetic Algorithm-II (NSGA-II). Three decision variables, i.e., number of cores, cache size and frequency are used to find best solution. The proposed approach is validated over a set of parallel benchmarks using a cycle accurate simulator. The results show a significant amount of energy saving along with minimal impact on the throughput of the system.