International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 18, No. 1, 8 Feb. 2026
Cover page and Table of Contents: PDF (size: 1219KB)
Exploring and Implementing Container Scheduling Methods: A Comparative Review and Practical Approach
PDF (1219KB), PP.105-125
Views: 0 Downloads: 0
Author(s)
Kanika Sharma
1,*
Parul Khurana
1
Index Terms
Cloud Computing, Containerization, Isolation, Resource allocation, Scheduling, Virtualization, Virtual Machines
Abstract
Container-based virtualization has become prominent as lightweight virtualization due to its scalability, resource utilization, and portability, especially in microservices. Container scheduler plays an essential role in Container services to optimize performance to reduce the overall cost by managing load balancing. Although Containers serve a lot of benefits, resource allocation is one of the major concerns associated with Container technology. This paper systematically reviews the distinct scheduling techniques used for Containers in a cloud environment, where existing scheduling techniques and their shortcomings have been discussed in detail. In the review process, the various issues and challenges in the Container technology have been identified, and the same has been discussed in this paper. Based on crucial elements including different performance metrics like CPU utilization, memory utilization, load balancing, and many others, it gives an in-depth comparison of the existing scheduling techniques like Ant Colony Optimization(ACO), Particle Swarm Optimization(PSO), Bee Colony Optimization(BCO), Chicken Swarm Optimization(CSO) and Genetic Algorithm(GA) outlining their benefits and drawbacks. This study also proposes a hybrid framework for secure and efficient Container scheduling. This framework can be implemented in the future to provide better results compared to existing approaches.
Cite This Paper
Kanika Sharma, Parul Khurana, "Exploring and Implementing Container Scheduling Methods: A Comparative Review and Practical Approach", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.18, No.1, pp. 105-125, 2026. DOI:10.5815/ijieeb.2026.01.07
Reference
[1]Karthik Kambatla, Vamsee Yarlagadda, Íñigo Goiri, and Ananth Grama. Optimistic scheduling with service guarantees. Journal of Parallel and Distributed Computing, 135:246–258, 2020.
[2]Zhiming Shen, Zhen Sun, Gur-Eyal Sela, Eugene Bagdasaryan, Christina Delimitrou, Robbert Van Renesse, and Hakim Weatherspoon. X-Containers: Breaking down barriers to improve performance and isolation of cloud-native Containers. In Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems, pages 121–135, 2019.
[3]Imtiaz Ahmad, Mohammad Gh AlFailakawi, Asayel AlMutawa, and Latifa Alsalman. Container scheduling techniques: A survey and assessment. Journal of King Saud University-Computer and Information Sciences, 2021.
[4]Emiliano Casalicchio and Stefano Iannucci. The state-of-the-art in Container technologies: Application, orchestration and security. Concurrency and Computation: Practice and Experience, 32(17):e5668, 2020.
[5]Titus Team at Netflix. Scaling Container deployments with titus: Netflix’s Container management platform. https://netflixtechblog.com/scaling-Container-deployments-with-titus-1f7b8f9f902b, May 2020. Accessed: 2025-04-21.
[6]Abdul Saboor, Mohd Fadzil Hassan, Rehan Akbar, Syed Nasir Mehmood Shah, Farrukh Hassan, Saeed Ahmed Magsi, and Muhammad Aadil Siddiqui. Containerized microservices orchestration and provisioning in cloud computing: A conceptual framework and future perspectives. Applied Sciences, 12(12):5793, 2022.
[7]Cai Zhiyong and Xie Xiaolan. An improved Container cloud resource scheduling strategy. In Proceedings of the 2019 4th International Conference on Intelligent Information Processing, pages 383–387, 2019.
[8]Mainak Adhikari and Satish Narayana Srirama. Multi-objective accelerated particle swarm optimization with a Container- based scheduling for internet-of-things in cloud environment. Journal of Network and Computer Applications, 137:35–61, 2019.
[9]Bo Liu, Pengfei Li, Weiwei Lin, Na Shu, Yin Li, and Victor Chang. A new Container scheduling algorithm based on multi-objective optimization. Soft Computing, 22(23):7741–7752, 2018.
[10]Yang Hu, Huan Zhou, Cees de Laat, and Zhiming Zhao. Ecsched: Efficient Container scheduling on heterogeneous clusters. In European Conference on Parallel Processing, pages 365–377. Springer, 2018.
[11]Karthik Kambatla, Vamsee Yarlagadda, Ínigo Goiri, and Ananth Grama. Ubis: Utilization-aware cluster scheduling. In 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pages 358–367. IEEE, 2018.
[12]Miao Lin, Jianqing Xi, Weihua Bai, and Jiayin Wu. Ant colony algorithm for multi-objective optimization of Container- based microservice scheduling in cloud. IEEE access, 7:83088–83100, 2019.
[13]Oana-Mihaela Ungureanu, Ca˘lin Vla˘deanu, and Robert Kooij. Kubernetes cluster optimization using hybrid shared-state scheduling framework. In Proceedings of the 3rd International Conference on Future Networks and Distributed Systems, pages 1–12, 2019.
[14]Kapil N Vhatkar and Girish P Bhole. Optimal Container resource allocation in cloud architecture: A new hybrid model. Journal of King Saud University-Computer and Information Sciences, 2019.
[15]Mohamed K Hussein, Mohamed H Mousa, and Mohamed A Alqarni. A placement architecture for a Container as a service (caas) in a cloud environment. Journal of Cloud Computing, 8(1):1–15, 2019.
[16]Han Li, Xinhao Wang, Sikun Gao, and Ning Tong. A service performance aware scheduling approach in Containerized cloud. In 2020 IEEE 3rd International Conference on Computer and Communication Engineering Technology (CCET), pages 194–198. IEEE, 2020.
[17]Heithem Abbes, Thouraya Louati, and Christophe Cérin. Dynamic replication factor model for linux Containers-based cloud systems. The Journal of Supercomputing, 76(9):7219–7241, 2020.
[18]Bo Liu, Jiawei Li, Weiwei Lin, Weihua Bai, Pengfei Li, and Qian Gao. K-pso: An improved pso-based Container scheduling algorithm for big data applications. International Journal of Network Management, 31(2):e2092, 2021.
[19]Tarek Menouer. Kcss: Kubernetes Container scheduling strategy. The Journal of Supercomputing, 77(5):4267–4293, 2021.
[20]Siyuan Zheng, Fenfen Huang, Chen Li, and Haobin Wang. A cloud resource prediction and migration method for Container scheduling. In 2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS), pages 76–80. IEEE, 2021.
[21]Yiwen Hu and Yuangang Lei. A Container cloud scheduling strategy based on qos. In The 2nd International Conference on Computing and Data Science, pages 1–5, 2021.
[22]Jigna Acharya and Anil C Suthar. Container scheduling algorithm in docker based cloud. Webology (ISSN: 1735-188X), 19(2), 2022.
[23]Sharma, Kanika, and Parul Khurana. "Performance Evaluation of the Nature-Inspired Algorithms for Container Scheduling for Elastic Containerized Multi-Cloud." 2024 IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS). IEEE, 2024.
[24]SJingze Lv, Mingchang Wei, and Yang Yu. A Container scheduling strategy based on machine learning in microservice architecture. In 2019 IEEE International Conference on Services Computing (SCC), pages 65–71. IEEE, 2019.
[25]Tarek Menouer and Patrice Darmon. Containers scheduling consolidation approach for cloud computing. In Pervasive Systems, Algorithms and Networks: 16th International Symposium, I-SPAN 2019, Naples, Italy, September 16-20, 2019, Proceedings 16, pages 178–192. Springer, 2019.
[26]Xusheng Zhang, Ziyu Shen, Bin Xia, Zheng Liu, and Yun Li. Estimating power consumption of Containers and virtual machines in data centers. In 2020 IEEE International Conference on Cluster Computing (CLUSTER), pages 288–293. IEEE, 2020.
[27]Shubha Brata Nath, Sourav Kanti Addya, Sandip Chakraborty, and Soumya K Ghosh. Green Containerized service consol- idation in cloud. In ICC 2020-2020 IEEE International Conference on Communications (ICC), pages 1–6. IEEE, 2020.
[28]Naylor G Bachiega, Paulo SL Souza, Sarita M Bruschi, and Simone Do RS De Souza. Container-based performance evaluation: a survey and challenges. In 2018 IEEE International Conference on Cloud Engineering (IC2E), pages 398–403. IEEE, 2018.
[29]Aravanan Muniswamy and Radhakrishnan Vignesh. Dsts: A hybrid optimal and deep learning for dynamic scalable task scheduling on Container cloud environment. Journal of Cloud Computing, 11(1):1–19, 2022.
[30]Ruiting Zhou, Zongpeng Li, and Chuan Wu. Scheduling frameworks for cloud Container services. IEEE/acm transactions on networking, 26(1):436–450, 2018.
[31]Kuljeet Kaur, Sahil Garg, Georges Kaddoum, and Song Guo. Esp-vdce: Energy, sla, and price-driven virtual data center embedding. In ICC 2020-2020 IEEE International Conference on Communications (ICC), pages 1–7. IEEE, 2020.
[32]Yanal Alahmad, Tariq Daradkeh, and Anjali Agarwal. Optimized availability-aware component scheduler for applications in Container-based cloud. In 2019 Sixth International Conference on Software Defined Systems (SDS), pages 194–199. IEEE, 2019.
[33]Bruno de Athayde Prata, Carlos Diego Rodrigues, and Jose Manuel Framinan. Customer order scheduling problem to minimize makespan with sequence-dependent setup times. Computers & Industrial Engineering, 151:106962, 2021.
[34]Feifei Chen, Xiaofeng Zhou, and Chao Shi. The Container scheduling method based on the min-min in edge computing. In Proceedings of the 4th International Conference on Big Data and Computing, pages 83–90, 2019.
[35]Ashok Kumar, Rajesh Kumar, and Anju Sharma. Energy aware resource allocation for clouds using two level ant colony optimization. Computing & Informatics, 37(1), 2018.
[36]Leonardo R Rodrigues, Marcelo Pasin, Omir C Alves, Charles C Miers, Mauricio A Pillon, Pascal Felber, and Guilherme P Koslovski. Network-aware Container scheduling in multi-tenant data center. In 2019 IEEE Global Communications Con- ference (GLOBECOM), pages 1–6. IEEE, 2019.
[37]Boxiong Tan, Hui Ma, and Yi Mei. A hybrid genetic programming hyper-heuristic approach for online two-level resource allocation in Container-based clouds. In 2019 IEEE Congress on Evolutionary Computation (CEC), pages 2681–2688. IEEE, 2019.
[38]Mahmoud Imdoukh, Imtiaz Ahmad, and Mohammad Alfailakawi. Optimizing scheduling decisions of Container manage- ment tool using many-objective genetic algorithm. Concurrency and Computation: Practice and Experience, 32(5):e5536, 2020.
[39]Boxiong Tan, Hui Ma, Yi Mei, and Mengjie Zhang. A cooperative coevolution genetic programming hyper-heuristics approach for on-line resource allocation in Container-based clouds. IEEE Transactions on Cloud Computing, 10(3):1500– 1514, 2020.
[40]Benjamin Burvall. Improvement of Container placement using multi-objective ant colony optimization, 2019.
[41]Tao Shi, Hui Ma, and Gang Chen. Energy-aware Container consolidation based on pso in cloud data centers. In 2018 IEEE Congress on Evolutionary Computation (CEC), pages 1–8. IEEE, 2018.
[42]Guisheng Fan, Liang Chen, Huiqun Yu, and Wei Qi. Multi-objective optimization of Container-based microservice schedul- ing in edge computing. Computer Science and Information Systems, 18(1):23–42, 2021.