## 5 Topology and Overload Detection
### Overload Detection Results
For instance, one segment processes sequence numbers 1-100, another covers 301-400, and so on. These ranges are representative of function code instances dynamically assigned during DCS operation. The segment control windows display real-time utilization statistics, such as CPU usage percentage and memory usage in bytes. Four segment groups are logically combined using summation units (\(\sum k\)) to compute intermediate totals. Each summation block aggregates the utilization data from four adjacent segments, effectively reducing monitoring overhead while preserving resolution. These partial results are then fed into a final summation unit, which computes the total system-wide CPU and memory utilization across all monitored segments. This hierarchical structure enables scalable, modular overload detection. By partitioning execution blocks and associating them with segmented logic, the proposed system can isolate overload conditions with greater granularity, enabling early detection and mitigation of potential performance degradation. Furthermore, the use of summation logic allows for efficient aggregation without centralized bottlenecks, making the method well suited for large-scale or real-time industrial automation systems. Figure 11 presents a detailed schematic of the proposed optimal utilization rate computation mechanism in a DCS using segmented CPU load monitoring and hierarchical summation logic. The diagram illustrates multiple instances of SEGCRM blocks, each representing localized control segments responsible for executing specific function code sequences within the system. Each SEGCRM block outputs an individual CPU utilization value (e.g., 10.45%, 13.56%), reflecting the localized processing load at that segment. These values are routed through a parallel summation network, where a primary summation block (\(\sum k\)) consolidates the individual utilizations into a collective intermediate utilization value, in this case shown as 57.2%. This intermediate value corresponds to a logically grouped subset of system segments that have been balanced for execution time and resource consumption. The output of the first summation block is further processed by a second summation unit (\(\sum k\)), which aggregates all segment group totals into a final global utilization rate, displayed here as 88.4%. This hierarchical architecture not only facilitates modular scalability but also supports real-time visibility into system load distribution. The red annotations labeled \"_Optimal Utilization Rate_\" highlight the ability of this design to monitor resource consumption at a granular level while providing a comprehensive system-level assessment. By identifying and summing only the relevant load contributions, this architecture enables more effective overload detection and supports dynamic load balancing strategies, thereby ensuring that system performance remains within optimal operational thresholds. The final utilization rate serves as a comprehensive performance indicator, capturing the aggregated  
Figure 10: Proposed overload detection algorithm of each sequence block and segmentation to finalize DCS. contributions from all individual segments into a single, optimized metric. The observed simulation results closely aligned with the anticipated outcomes, thereby validating the structural soundness and predictive reliability of the proposed framework. These findings demonstrate that the system can effectively allocate computational resources across segmented function blocks, facilitating optimal load balancing and improved execution efficiency. Moreover, the results suggest that the proposed DCS optimization strategy holds significant relevance for a range of application domains, including energy management, production scheduling, and real-time digital signal processing, where resource efficiency and system responsiveness are critical to operational success. In order to apply these simulation results to a safety-critical system, we attempted objective evidence for application in actual power plants.