Table 2 Literature summary (Multi-Objective Optimization)

[35]

Present a capable controller for managing containers on edge-cloud nodes in industrial IoT contexts while accounting for interference and energy use.

Integer linear programming based on multi-objective optimization.

Data obtained in real time from the Google compute cluster.

By reducing the energy consumption of edge-cloud nodes and scheduling applications optimally with the least amount of interference, the proposed Kubernetes-based energy and interference driven scheduler (KEIDS) improves performance for end users.

Container management and scheduling for Industrial IoT.

The limitations and future potential of KEIDS are not specified in the given text.

[36]

Build a multi-objective scheduling model for container-based microservices and to suggest an ant colony method to handle scheduling issues.

Ant colony algorithm

Real data from Alibaba cluster Trace V2018 having an application with 17 micro servers

In comparison to previous relevant algorithms, the suggested optimization method outperformed them in the optimization of cluster service dependability, cluster load balancing, and network transmission overhead.

Container-based microservice scheduling in cloud architectures

High time complexity plus real cloud container should be used.

[37]

To improve Kubernetes' resource scheduling scheme

The authors examine the source code of Kubernetes' scheduling module, extract its model, and create and carry out a simulation experiment using the model. The K8s scheduling model is then enhanced by combining the ant colony and particle swarm optimization algorithms.

The authors schedule resources for K8s using the Java programming language and the CloudSim tool.

The experimental results demonstrate that the suggested approach outperforms the original scheduling technique, resulting in a lower overall resource cost, a higher maximum node load, and more evenly distributed job assignment.

Kubernetes can deploy containerized applications on a wide scale in private, public, and hybrid cloud environments using the better resource scheduling scheme.

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[38]

To describe in more detail the idea of container placement and migration in edge computing, as well as to examine the scheduling models created for this purpose.

The container placement problem can be abstracted using graph network models or multi-objective optimization models. Algorithms based on heuristics to address the scheduling issue.

–

Most existing container scheduling models are heuristic-based and consider only static edge computing tasks, with limited research on decentralized scheduling systems

Container-based edge computing

Future research in container scheduling should focus on decentralized systems and mobile edge nodes.

[39]

By assigning virtual network functions (VNFs) to appropriate places, virtual networks' resilience can be increased.

Optimization models and heuristic algorithms to solve VNF placement problems

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Implementation of function scheduler plugins that can connect multiple optimization models with Kubernetes and allocate functions automatically

Allocating VNFs to nodes in Kubernetes

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