## 4 Performance Evaluation and Analysis
The task grouping between interconnected layers is represented as \(n\in Task_{N}\) and \(Fgd\) to inherit the edge computing based on task similarity verification. The proposed CLCTS addresses such TAEs through the mediate layer and final output. The controller re-tasking is performed by maximizing the network training for addressing high-time delay systems through the graph structure. This computation is pursued by identifying the conditions \(Fgd\
eq 0\) and \(Fgd=0\) for all \(i\) to determine the concurrent tasking and completion from the current instance. Consider the \(Task_{N}::TM\) and \((Task_{N},Fgd)::TM\) groupings for maximizing the productivity rate with fewer TAEs (Figure 9). This TAE is reduced by 17.41% for the # of edge devices and 12.24% for the # of time slots. Figure 8: Assessment of the completion ratio. Figure 9: Assessment of TAE. This proposed scheme is aided to satisfy the low processing time for the application of EC in the next generation IIoT. This reduces the task acceptance error in task grouping, from which the high completion ratio is achieved (refer to Figure 10). In this article, graph learning is used to define concurrent tasking and completion to identify the dissimilar tasks in edge devices and thereby minimizes task prolonging time. The controller migration is performed based on task groups that are interconnected using graph structures to reduce the high time-delay systems. The maximum resource utilization and similarity of completion is achieved for the controller re-tasking inside and outside the group. The task acceptance error is identified at the time of training the learning network using edge computing and graph learning for verifying similar tasks for better productivity with less running time using the condition \(R_{n}=\left(\frac{\exists-fail_{\mathit{opt}}\times fail_{\mathit{agg}}}{N +n+1}\right)\) and \(\theta_{\Omega_{i}}=\tilde{\Omega}_{\mathit{l}yc}-f\alpha_{\mathit{l}kg}.p_{T} -\mu_{G}.\exists\). This condition is not applicable for the first phase because it relies on all similar tasks grouped in the given time interval. Therefore, the reputation of all the edge devices, along with concurrent task allocation and utilization, is observed through IIoT, and hence, it is constant. From this analysis, the inverse outcome of the first phase is used to reduce TAE. Hence, the running time is lower compared to the other factors. The task processing time is reduced by 7.36% and 7.39% for the varying # Edge Devices and # Time Slots. The production, maintenance, and outcome are improved through the proposed scheme and learning network under high task volumes (Figure 11). In the continuous task grouping, \(R_{n}\) in its current and previous tasks grouping is compared to define the working of acquiring edge devices. If such groups are interconnected through graph learning, as observed using the condition \(Fgd>n\) (later), then the task grouping from \(n\in N\) is halted to prevent TAE and improve the automation and productivity rate towards smart industries. The learning network is trained to prevent task prolonging time and TAE using edge nodes and the tasking/migration/re-tasking information from the edge devices. The learning network is used to identify the re-tasking overhead in cross-layer controllers. These heterogeneous edge networks are employed to achieve high productivity and automation. These network brims are administered based on the task grouping, and controller migration is concurrently analyzed to reduce TAE and thereby increase productivity. In this article, the graph structure is used to reduce the re-tasking overhead in any instance for maximizing productivity. Therefore, less re-tasking overhead from the controller re-tasking is compared to the other factors. The CLCT scheme reduces 8.67% and 12.84% of the overhead for the # of edge nodes and the # of time slots. Figure 10: Assessment of processing time.