# Scheduling the Exchange of Context Information for Time-Triggered Adaptive Systems
## 5 Results and Discussion
### Experimental Setup and Communication Cost Modeling  
In the evaluation of our study, we assessed the performance of our proposed metascheduling algorithm in time-triggered systems, focusing on communication cost as the primary metric. For the genetic algorithm, we utilized Python (version 3.10) along with the DEAP library (version 1.4.3) [31]. The choice is due to the flexibility in implementing both value- and permutation-encoded genomes [29]. The detailed configuration of the genetic algorithm used in our experiments is summarized in Table 1.  
We used the Stanford SNAP Library [32] to generate Directed Acyclic Graphs (DAGs) conforming to application models with job sizes ranging from 10 to 60. These DAGs, created using SNAP's efficient graph representation capabilities and based on a random model, allowed us to simulate various workload scenarios for metascheduler evaluation. The models varied in nodes, edges, indegrees, and outdegrees to reflect a diverse set of application scenarios. The structural format of the application and platform models used in our experiments is defined using abstract JSON schemas. The application schema captures jobs, messages, and an application deadline, while the platform schema defines nodes and interconnecting links. These schemas are illustrated in Figure 6.  
The use of Directed Acyclic Graphs (DAGs) in scheduling and resource allocation research is well-established and widely supported within the community. Tools such as Task Graphs for Free (TGFF) [33] and Synchronous Dataflow For Free (SDF3) [34] were specifically developed to generate and analyze DAG-based workloads, allowing researchers to simulate and evaluate scheduling strategies under controlled and repeatable conditions. TGFF offers parameterizable task graphs with adjustable structural properties, including degree, critical path length, and communication volume. It is extensively used for benchmarking task allocation and scheduling algorithms in heterogeneous systems. Similarly, SDF3 offers advanced modeling capabilities for synchronous dataflow applications, supporting the generation of consistent, deadlock-free graphs that closely resemble digital signal processing (DSP) and multimedia workloads. These tools, along with the underlying DAG abstraction, serve as standard baselines in the design and evaluation of schedulers for embedded, multiprocessor, and MPSoC platforms. Therefore, our use of DAGs generated through the SNAP library aligns with these best practices, enabling comparative analysis across a broad design space. We applied the algorithm to a range of datasets, ensuring a consistent platform model across all experimental runs to isolate performance variations to application model differences. Each dataset, encompassing diverse job sizes and structures generated via the SNAP library, was processed by the algorithm, enabling an evaluation across varying complexities and scenarios. In our study, the computation of communication cost plays a crucial role in evaluating our proposed metascheduling algorithm. This cost is fundamentally associated \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value/Description** \\ \hline GA Library & DEAP (Python) [31] \\ Population Size & 100 \\ Number of Generations & 50 \\ Crossover Probability & 0.3 \\ Mutation Probability & 0.2 \\ Selection Method & Tournament Selection \\ Fitness Function & Minimization of makespan \\ \hline \multicolumn{3}{l}{**Genome Structure and Operators**} \\ \hline Task Order (R) & Encoding: Permutation; Crossover: Partially matched crossover (PMX); Mutation: Randomly swaps two elements \\ End System Allocation (A) & Encoding: Value-based (Integer); Mutation: Replaces element with random integer in valid range \\ Path Index (P) & Encoding: Value-based (Integer); Mutation: Replaces element with random integer in valid range \\ Message Priority Ordering (T) & Encoding: Permutation; Mutation: Random shuffle with a given probability \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Genetic Algorithm Configuration Parameters.