## 4 Results and Discussion  
All the finds and experiments were conducted in a simulated environment with initial setup details mentioned in Tables 2 and 3 using the Java (JDK 1.6)-based Cloudsim tool. All the experiments were conducted by varying the task inputs from 100-400 units [20,24]. The performance of the introduced Hybrid Weighted Ant Colony Optimized algorithm was compared with various other algorithms, such as the MTF-BPSO [14],QANA [25], ACO [28], MIN-MIN [29], FCFS [30], with respect to their time taken for execution (makespan) and cost parameters. The main reason to select the ACO algorithm is due to the fact that the introduced algorithm is based on ACO, and, thus, the first algorithm used for comparison was the ACO algorithm, while the other algorithm used for comparison could have been any recently proposed or a traditional one, but out of all the algorithms, FCFS and MIN-MIN were selected due to various reasons. Both of them are the most basic and traditional algorithms which were initially utilized in almost all cloud computing and the fact that most of the algorithms that we know of today are related to these algorithms in some or the other way. These were mainly the task scheduling algorithms which consider makespan and cost as their parameters for evaluation of their performance. Thus, these two algorithms acted as a stepping stone to decide whether the introduced algorithm would be able to optimize the result and achieve the objective goals. The selection for QANA is based on the fact that QANA is one of the state-of-the-art algorithms introduced for solving global optimization problems, that is, QANA is better in terms of performances for returning ideal optimization solutions when compared to other modern algorithms like the MWOA (Modified Whale Optimization Algorithm), EEGWO (Exploration-Enhanced Grey Wolf Optimizer), and many more. Thus, the introduced algorithm was compared with ideal algorithms, such as FCFS and MIN-MIN. At the same time, it was also compared with modern algorithms such as QANA and MTF-BPSO in terms of performance. Finally, MTF-BPSO was selected because MTF-BPSO is one of the recently proposed algorithms which aims to improve the performance task scheduling and quality of service in cloud computing and for the fact that MTF-BPSO outperforms many task scheduling algorithms such as the Sailfish Optimization Algorithm, Opposition-Based Sunflower Optimization Algorithm, and many other algorithms. The values used for initializing the parameters and hyperparameters in all the six algorithms are present in Tables 3 and 4. Parameters are generally values which are calculated during the process of finding the solution set, that is, they are variables that help determine the final optimal solution set. On the other hand, hyperparameters are fixed values which help determine the parametric values during each iteration. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**S/No** & **Entity Type** & **Parameter** & **Value** \\ \hline
1 & User & Number of users & 50 \\ \hline
2 & Task & Size & 400 \\ \hline  & & Number of tasks & 100–400 \\ \hline
3 & Host & Host memory (RAM) & 2048 BM \\ \hline  & & Host bandwidth & 10,000 \\ \hline  & & Host storage & 100,000 \\ \hline
4 & Virtual Machine (VM) & Type of policy & Time-Shared \\ \hline  & & Number of VMs & 40 \\ \hline  & & VM RAM & 512 BM \\ \hline  & & VMM & Xen \\ \hline  & & OS & Windows \\ \hline  & & Number of CPUs & 1 on each \\ \hline
5 & Datacentre & Number of datasets & 4 \\ \hline  & & Number of hosts & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation setup.