## 4 System Model
### Proposed Algorithm
The results of the six different approaches have been compared using these two parameters, and we found that the Grey wolf optimization algorithm with simulated annealing is performing better than all other approaches. Figure 11: Makespan with 2000 tasks. Figure 12: Load variation with 3000 tasks. \[\vec{S}(i+1)=S_{p}(\vec{i})-\vec{P}.\vec{D} \tag{6}\]0 Load variation with 2000 tasks. \[\vec{S}(i+1)=S_{p}(\vec{i})-\vec{P}.\vec{D} \tag{6}\]1 Makespan with 1000 tasks. ## 6 Conclusions and Future Directions

The main focus of the work presented in this paper is to distribute the workload among all servers efficiently and minimize the makespan. We propose a Grey Wolf Optimization (GWO) based Simulated Annealing approach to counter the problem of load balancing in the containerized cloud. We perform our experiment by taking 1000, 2000, and 3000 tasks and the number of containers were taken from 10 to 50. Then, we compared our results with the Genetic and Particle Swarm Optimization algorithm and evaluated the proposed algorithms by considering the parameter load variation and makespan. The experimental result indicates that, in most cases, more than 97% of the tasks were meeting their deadline and the Grey Wolf Optimization Algorithm with Simulated Annealing (GWO-SA) performs better than all other approaches in terms of load variation and makespan. For future perspective, we have found several open issues to address in the containerized cloud environment. Firstly, efficient virtual machine sizing plays an important role in resource utilization. In the future, we will extend the work going ahead with virtual machine sizing. \[\vec{S}(i+1)=S_{p}(\vec{i})-\vec{P}.\vec{D} \tag{6}\]2 Workload Variations. \[\vec{S}(i+1)=S_{p}(\vec{i})-\vec{P}.\vec{D} \tag{6}\]3 Makespan with 3000 tasks. \[\vec{S}(i+1)=S_{p}(\vec{i})-\vec{P}.\vec{D} \tag{6}\]4 Makespan Variations. machine sizing in the containerized cloud. Secondly, categorizing tasks into groups and assigning the same VM for a group of tasks with similar requirement can improve the system performance. Thirdly, migrating VM and container can also balance the workload by migrating tasks from overloaded VM to lightly loaded VM. So, migrating VM or container is better and migrating both is another research challenge. The fourth problem is the resource elasticity, in a dynamic cloud environment, there might be the requirement of additional resources during run time. So, dealing with dynamic resource requirements is another research challenge. We will work on these open issues in our future research. M.K.P. : conceptualization, methodology, writing--original draft preparation; B.S. : data curation, supervision; A.K.T. : methodology, supervision; S.M. : investigation, validation, reviewing and finalizing the paper. All authors have read and agreed to the published version of the manuscript. This research received no external funding. We have used Googlr Cluster Workload Traces 2019 which is publicly available by Google at [[https://research.google/tools/datasets/google-cluster-workload-traces-2019/](https://research.google/tools/datasets/google-cluster-workload-traces-2019/)]([https://research.google/tools/datasets/google-cluster-workload-traces-2019/](https://research.google/tools/datasets/google-cluster-workload-traces-2019/)), accessed on 1 January 2020. The authors declare no conflict of interest. ## References

* [PERSON] (2020) [PERSON] Pricing cloud IaaS computing services. _J. Cloud Comput._**2020**, \(9\), 14. [CrossRef]
* [PERSON] and [PERSON] (2014) [PERSON]; [PERSON] Notes on Cloud computing principles. _J. Cloud Comput._**2014**, \(3\), 21. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] Efficiency Measurement of Cloud Service Providers Using Network Data Envelopment Analysis. _IEEE Trans. Cloud Comput._**2022**, _10_, 348-355. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON] [PERSON] A novel hybrid of Shortest job first and round Robin with dynamic variable quantum time task scheduling technique. _J.