Table 4 Literature summary (Auto-Scaling Enabled Scheduling)

[60]

Create a multi-level dynamic autoscaling technique for containerized apps.

a dynamic multi-level (DM) autoscaling technique employing monitoring information from the infrastructure and applications.

Both simulated and actual workload settings.

The DM method outperforms other autoscaling techniques already in use.

DM method implementation for time-sensitive cloud applications in the SWITCH system.

Limited to the context of container-based cloud applications implemented in the SWITCH system.

[61]

Create a system that can flexibly change how many containers are operating in a Kubernetes cluster. To include container migration into the Kubernetes VPA system in a non-disruptive manner.

Resource Utilization Based Autoscaling System (RUBAS)

Multiple scientific benchmarks

RUBAS increases the cluster's CPU and memory consumption by 10% and decreases runtime by 15%, with a 5%–20% overhead for each application.

Dynamic allocation of containers running in a Kubernetes cluster

–

[62]

To make cloud-based applications' management easier and more effective

To improve Kubernetes autoscaling decisions by adapting to actual variability of incoming requests

In a short-term assessment loop, various machine learning forecasting techniques compete with one another. Compact management parameter that application providers can use to find the ideal trade-off between resource over-provisioning and SLA violations.

Simulations and measurements on gathered Web traces were used to assess the scaling engine and management parameter.

In comparison to the default baseline, the multi-forecast scaling engine produces much fewer dropped requests with somewhat higher provided resources.

To improve Kubernetes autoscaling decisions in cloud-based applications

–

[63]

To develop an adaptive autoscaling algorithm for Kubernetes pods

To automatically detect optimal resource set for pods and manage horizontal scaling process

Libra automatically determines the best resource combination for a single pod and updates resource description for the pod and the horizontal scaling procedure in the dynamic environment.

–

–

To improve scalability of Kubernetes pods

–

[64]

To develop an adaptive AI-based autoscaling system for Kubernetes that makes better scaling decisions and is easier for application providers to use

Various AI-based forecast methods

Short-term evaluation loop

Simulations

Collected web traces

The approach yields noticeably fewer dropped requests and slightly more supplied resources.

Kubernetes

–

[65]

Improve the efficiency of Kubernetes resource scaling.

Proposed Kubernetes autoscaler based on Pod replicas prediction.

Conducted experiments to verify the proposed autoscaler

Autoscaler had faster response speed

Can be used to improve resource scaling in Kubernetes

Further research and experimentation is to fully evaluate the proposed autoscaler.

[66]

Present a better automatic scaling plan for Kubernetes based on a variety of node types with pre-loaded images.

The suggested method incorporates the benefits of various node types in the scaling process.

The proposed scheme is tested and compared with the default auto scaler

The suggested approach decreases instability within the active clusters and enhances system performance under high load pressure.

The proposed scheme improved the performance and stability of the system under load pressure

Further testing and evaluation is needed to determine the full range of applications and limitations of the proposed scheme.

[67]

Address the diversity of 5G use cases with maximum flexibility and cost effectiveness.

Improve network functions availability and resilience

Modular design for network functions.

Statistical approach for modeling and resolution of resource allocation problem.

Used Kubernetes infrastructure hosting different network services

The suggested technique protects crucial operations while preventing resource limitation in cluster nodes.

Can be applied to Kubernetes infrastructure hosting network services to improve availability and resilience

Limited information provided on experimental setup and specific results obtained