## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Algorithm** & **QoS Parameter** & **Demerits** & **Tools Used** \\ \hline
[6] & Resource and Deadline Aware Dynamic & Tasks deadlines, lower makespan, task & & \\  & Load-balance (RADL) & response time, penalty cost, and task & & \\  & & execution cost & & \\ \hline
[7] & Energy Efficient Virtual Machine Mapping & Energy consumption, execution & & \\  & Algorithm (EVIMA) & makespan, and execution cost & & \\ \hline
[8] & Salfish Optimization-Based Scheduling & Energy consumption and cost & & VM allocation and placement lacking & \\  & Algorithm (GOAS) & Energy consumption and cost & & increase the time of execution. & \\ \hline
[9] & Workflow scheduling algorithm & & & \\  & based on Jaya & & & \\  & & & & \\ \hline
[10] & Improved Firefly Algorithm & & & \\  & & & & \\ \hline
[11] & Opposition-Based Sunflower Optimization & Cost, energy, and makespan. & & Taking more time for execution. & Cloudsim \\  & (OSFO) Algorithm & & & \\ \hline
[12] & Local Pollination-Based Meth Search & & &
\begin{tabular}{c} The algorithm performs better when the \\ number of tasks is low, but as the number \\ increases, the performance of the \\ algorithm decreases. \\ \end{tabular} & Cloudsim \\ \hline
[13] & Spacing Multi-Objective Antiion & Energy, cost, and makespan & &
\begin{tabular}{c} More time for task selection and virtual \\ machine tuning in order to execute the \\ assigned task. \\ \end{tabular} & Cloudsim \\ \hline
[14] & Binary Particle Swarm Optimization (BPSO) & & \
\begin{tabular}{c} Makespan time, energy consumption, \\ and execution cost \\ \end{tabular} & &
\begin{tabular}{c} This algorithm suffers from reliability and \\ service level agreement violation. \\ \end{tabular} & Cloudsim \\ \hline
[15] & Adaptive Neuro-Fuzzy Inference System (ANFIS)-Black Widow Optimization (BWO) & & \
\begin{tabular}{c} Computational time, makespan, energy \\ consumption, computational cost, and \\ resource utilization. \\ \end{tabular} &
\begin{tabular}{c} Lower number of tasks is only considered; \\ moreover, the performance gets affected \\ when the number of tasks is increased. \\ \end{tabular} & Cloudsim \\ \hline
[16] & Diversity-Maintained Multi-Trial Vector & Enhanced exploitation and exploration & & \
\begin{tabular}{c} This algorithm suffers from local \\ optimization, thus not giving an optimal \\ solution in all the cases. \\ \end{tabular} & MATLAB \\  & Differential Evolution Algorithm for & & \\  & Non-Decomposition Large-Scale Global & &
\begin{tabular}{c} Focuses only on workflow scheduling, and \\ the time taken for internal calculation is \\ time-of-execution and memory overhead. \\ \end{tabular} & Cloudsim \\ \hline
[26] & Multi-objective scheduling algorithm for & & \\  & scheduling scientific workflows & & \\  & & & \\ \hline
[27] & Multi-Objective Workflow Optimization & & & \\  & Strategy (MOWOS) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative table for literatures review. The rest of this work is organized as follows: The theoretical formulation of the introduced HWACO algorithm for enhancing the scheduling of task method is discussed in the Section 3. Solutions and their consequent examination have been listed in Section 4. Conclusions and future work considerations have been listed in the Section 5.