# Maximizing Efficiency in Energy Trading Operations through IoT-Integrated Digital Twins
## 4 Performance Analysis
### IoT Orchestration-Based Virtualized Energy Trading Outcomes  
As previously stated, while examining the attributes of smart grid-based systems, the tasks can be classified into two categories: event-driven tasks and periodic tasks. The graphic illustrates the round-trip time analysis for a set of 10 nano-grid energy trading operations. Users have the ability to assign various tags to items based on their significance, such as sensor, light, or load. Users have the ability to give several types of tags to virtual objects. Subsequently, several techniques are employed to manipulate actuators, such as acquiring light and adjusting light intensity. The characteristics include sensing and actuating data. When the user selects the option \"Add virtual objects\", the metadata associated with the virtual objects is stored in the virtual objects' repository. The horizontal axis of the graph depicts the number of smart houses, whilst the vertical axis portrays the evaluation of throughput in relation to the number of energy tasks performed per second. The x-axis represents the values of throughput in milliseconds, while the y-axis represents the number of households. As previously stated, the assessment comprises a total of 24 residences, which are categorized into three groups of 8, 16, and 24, as depicted in the diagram. The analysis shown in Figure 9a depicts the throughput analysis of tasks executed per second for different numbers of houses in a nano-grid. Three sets of bars represent the minimum, maximum, and average times in milliseconds. As the number of houses increases from 8 to 24, there is a corresponding increase in the minimum, maximum, and average times. The minimum time ranges from 3.9 ms to 19.22 ms, the maximum time ranges from 9.2 ms to 26.33 ms, and the average time falls between these values. The chart provides a clear visual representation of the performance characteristics, highlighting the variability in task execution times across different house configurations in the nano-grid. The bar chart shown in Figure 9b illustrates latency analysis for tasks executed per second in a nano-grid, comparing the minimum, maximum, and average latencies across different numbers of houses (8, 16, and 24). As the number of houses increases, there is a corresponding rise in both minimum and maximum latencies, providing insight into the performance characteristics of the system under varying workloads. Similarly, the bar graph shown in Figure 9c compares task failure and task starvation rates across different scheduling methods--FEF, Qayyum et al., and Round Robin--expressed as percentages (0-25%). For task failure rates, Qayyum et al. exhibits the lowest at 2.44%, followed by FEF at 6.32%, and Round Robin at 23.47%. Conversely, in task starvation rates, FEF shows the lowest at 9.34%, followed by Qayyum et al. at 17.32%, and Round Robin at 26.32%. This visualization provides a succinct comparison of response time metrics for each method, aiding in the assessment of their efficiency and reliability in task execution. The proposed methods demonstrate substantial improvements compared to existing studies. For task starvation, the proposed Round Robin (RR) and Time-Aware strategies achieved 13.8% and 17.4%, respectively, notably lower than the existing values of 27.5% and 22.3%. In task failure, the proposed RR and FEF strategies recorded 14.4% and 20.8%, surpassing the existing values of 38.7% and 36.5%. These results indicate the superiority of the proposed approaches in minimizing both task starvation and failure rates, showcasing enhanced system reliability and efficiency. A comparison is conducted with a prior study [17] to address the disparities in the dataset resulting from the restricted accessibility of comparable energy-related apps employing IoT task orchestration. The round-robin and FEF techniques are of considerable importance since they demonstrate the effectiveness of the strategy in minimizing latency for nano-grid residences.