# Scheduling the Exchange of Context Information for Time-Triggered Adaptive Systems
## 5 Results and Discussion
### Real-World Benchmark Implementation  
To support the evaluation of our scheduling method, we used an application model based on the automotive benchmark described by [PERSON] et al. [35]. This benchmark, derived from real engine control software, provides detailed information about execution times, communication sizes, and task dependencies. We selected and adapted only the attributes that are directly relevant to our scheduler.  
Each job in the application represents a schedulable software unit. The key attributes are  
execution_time(us): This represents the worst-case execution time (WCET) of each job, in microseconds. We generated these values using a Weibull distribution to reflect runtime variability. For each job, we selected a period group (e.g., 10  ms, 20  ms) and used the corresponding minimum, average, and maximum execution times from Table 4 in [35]. The distribution was scaled to match the average, and the values were constrained to remain within the reported bounds.  
core_node: This field indicates which processing core each job is mapped to. In our method, this mapping is not predefined but is determined as part of the genetic algorithm. The scheduler explores different job-to-core assignments to minimize makespan and reduce contention. While the original benchmark assumes fixed task-to-core mappings for multi-core execution platforms (as discussed in [35]), we reinterpret core-level mapping as an optimization parameter targeting an integrated MPSoC architecture.  
communications: These define data dependencies between jobs. Each message includes:  
1. sender and receiver: the job IDs involved in the communication,
2. size_bytes: the size of the message in bytes, sampled according to the distribution in Table 1 of [35], where most labels are between 1 and 4 bytes,
3. type: whether the message is intra-task or inter-task, as described in [35]; this is used to enforce precedence constraints but not timing semantics.  
The system runs on a 2 \(\times\) 2 MPSoC mesh with four processing cores connected via routers. While the original benchmark targets distributed ECUs connected via a CAN bus, we adapted the workload to run on a tiled MPSoC platform. This allows us to evaluate mapping and scheduling decisions in the context of on-chip multi-core communication and resource contention.  
As shown in Table 6, the metascheduler employing selective context exchange (proposed algorithm) achieves the most favorable timing performance. Its makespan of 7724 us is markedly lower than that of any periodic-sampling alternative and is only marginally higher than the \"No Context Exchange\" reference, whose result (6853 us) is interesting yet practically infeasible because it violates the consistency guarantees mandated by functional-safety standards. Periodic sampling with a 2 TU interval increases the makespan to 23,986 us, a degradation of approximately 211 % relative to the proposed approach. Even at the longest interval considered (10 TU), the makespan (10,279 us) remains 33% larger than that obtained with selective exchange.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Metric** & **Mean** & **Std** & **Var** & **Best** & **CoV (\%)** \\ \hline
**10J Proposed Method** & & & & & \\ \hline \multirow{2}{*}{Makespan} & \multirow{2}{*}{2669.8} & \multirow{2}{*}{115.4} & \multirow{2}{*}{13,317.19} & \multirow{2}{*}{2314} & \multirow{2}{*}{4.30} \\  & & & & & \\ \cline{1-1} \