**4. Results and Discussion**

The proposed SG is entirely simulated using the CloudSim platform, and the coding is written using 32 bit Java language for Windows 7 or 8. The processor used is a Core i5 with 8 GB RAM for higher computational operation. The application server used is Tomcat 5.0.6. The proposed useraware Power Regulatory Model with Location-Based Service Selection approach, is implemented and evaluated for its e fficiency. The technique has been assessed utilizing di fferent setups with a vast number of service points and numbers of users. The strategy has been tried with varying situations of re-enactment for various periods. Table 1 lists the details of the simulation parameters utilized to assess the execution of the proposed technique. Figure 2 shows a preview of the smart meter intended for the proposed system. Figure 3 is a preview of the user interface utilized by the power station sta ff to refresh the unit costs for an assortment of associations. Table 2 shows the completeness ratio of three di fferent algorithms, and it shows that the proposed method has a higher completeness measure value that indicates its e fficiency in providing guaranteed service.




**Figure 2.** Snapshot of the smart meter display.



**Figure 3.** Snapshot of the power station interface.


**Table 2.** Comparative of completeness ratio results.

The proposed solution has been implemented is Hadoop, which is a cloud computing platform integrated with the proposed solution to evaluate the proposed methodology. Three different clouds are created, with each one is running at various locations and on three service providers, which are running at N locations. The proposed solution is hardwired with the electric meter, and wireless communication is enabled to access the cloud service. Another web interface is specially designed for users to use and complete the paymen<sup>t</sup> procedures. To evaluate the performance of the proposed solution, Availability Ratio, Completeness Ratio, Security Value, Overall Performance Ratio, measures were the metrics examined. Availability is the ratio of total requests submitted and total requests handled. Completeness is the ratio between total submitted requests and the number of requests processed successfully. The security level is measured by total requests generated and completed. Overall performance is the ratio of the total number of requests submitted and the number of jobs completed in a particular time-frame.

The performance of the proposed method is compared with three well-known scheduling methods of the grid environment, namely Earlier Deadline First (EDF) [29] algorithms, Security-Aware scheduling strategy for Real-time applications on Clusters (SAREC) [30] and the Security of Real-Time Data-Intensive Applications on Grids (SARDIG) [31]. Table 2 shows the simulation results of the completeness ratio for the four algorithms. The completeness ratio is computed using different deadline bases or latency time (from 100 to 500 s). The proposed algorithm shows a better completeness ratio than the other algorithms as the deadline base or latency increases. Table 2 shows that the proposed

method has a higher ratio, which raised from 0.85 to 0.93, which demonstrates its e fficiency in providing guaranteed service.

Table 3 shows the service availability ratio of four di fferent algorithms, and it shows that the proposed method provides more service availability than other methods. Table 4 shows the simulation results of the security value for the four algorithms. The security value is computed using the total number of requests submitted and several requests fulfilled per number of users present in the network.


**Table 3.** Shows the comparison results of serviceability.


 comparison security 

results

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**Table**

**4.**

Shows

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Figure 4 shows the time complexity of di fferent methods to access the service where the service and data are available at various locations of the region. The time complexity is Φ (N × M), where N—is the number of locations where the service is available, and M—is the number of service providers available. The overall time complexity is computed as follows:

> Time complexity Tc = N × Log (M)

**Figure 4.** The time complexity of di fferent approaches.

Figure 4 shows that the proposed method produces more e fficient results compared to the other algorithms.
