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Abstract Containers as a service (CaaS) are a kind of services that permits the organization to handle the containers more effectively. Containers are lightweight, require less computing resources, portable, and facilitate better support for microservices. In the CaaS model, containers are deployed in virtual machines, and the virtual machine runs on the physical machine. The objective of this paper is to estimate the resource required by a VM to execute a number of containers. VM sizing is a directorial process where the system administrators have to optimize the allocated resources within the permitted virtualized space. In this work, the VM sizing is carried out using the Deep Convolutional Long Short Term Memory Network (Deep-ConvLSTM), where the weights are updated by Fractional Pelican Optimization (FPO) Algorithm. Here, the FPO is configured by hybridizing the concept of Fractional Calculus (FC) within the updated location of the Pelican Optimization Algorithm (POA). Moreover, the task grouping is done with Deep Embedded Clustering (DEC), where the grouping is established with respect to the various task parameters, such as task length, submission rate, scheduling class, priority, resource usage, task latency, and Task Rejection Rate (TRR). In addition, the performance analysis of VM sizing is done by taking 100, 200, 300, and 400 tasks. We got the best resource utilization of 0.104 with 300 tasks, a response time of 262ms with 100 tasks, and a TRR of 0.156 with 100 tasks and makespan of 0.5788 with 100 tasks.

Container-based virtualization has gained significant popularity in recent years because of its simplicity in deployment and adaptability in terms of cloud resource provisioning. Containerization technology is the recent development in cloud computing systems that is more efficient, reliable, and has better overall performance than a traditional virtual machine (VM) based technology. Containerized clouds produce better performance by maximizing host-level resource utilization and using a load-balancing technique. To this end, this article concentrates on distributing the workload among all available servers evenly. In this paper, we propose a Grey Wolf Optimization (GWO) based Simulated Annealing approach to counter the problem of load balancing in the containerized cloud that also considers the deadline miss rate. We have compared our results with the Genetic and Particle Swarm Optimization algorithm and evaluated the proposed algorithms by considering the parameter load variation and makespan. Our experimental result shows 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.