- 1 Reinforcement Learning Applications for Performance Improvement in Cloud Computing
- 2 A Systematic Review of Deep Learning Approaches for Computer Network and Information Security
- 3 Resource allocation optimization using artificial intelligence methods in various computing paradigms: A Review
- 4 Towards Metaheuristic Scheduling Techniques in Cloud and Fog: An Extensive Taxonomic Review
The latest trending research work in the field of Cloud computing includes the implementation of Artificial Intelligence, Deep Learning, and Reinforcement learning along with Metaheuristic algorithms, opening up new dimensions to improve performance, specifically in resource scheduling, task scheduling, energy efficiency, etc.
Here is a brief review of a few articles related to Deep learning implementation in cloud computing published in January 2022, and here they are:
A Systematic Review – This paper will provide an overview of various reinforcement learning-based published works and their advancement during the last 10 years. This paper emphasizes the study of resource allotment problems and Virtual Machine problems.
This research survey consolidates the details of 32 articles that published their work on deep learning implementation for improving network anomaly detection, intrusion detection, network traffic analysis, and classification. Also, this paper discussed some open issues and future recommendations for further improvement. If you are struggling to find your research topic, you may follow the recommendation leads from this paper.
This paper referred to different approaches for container scheduling, like heuristic, metaheuristic, mathematical modeling, and machine learning. And summaries of the published research work on container orchestration and container scheduling. It is a good read for those interested in scheduling problems as this paper refers to the main features, advantages, and disadvantages of some existing algorithms from the past four years.
Resource allocation optimization using artificial intelligence methods in various computing paradigms: A Review
This paper reviewed a broader aspect of artificial intelligent methods for optimizing and increasing the efficiency of network node communications(dataflow) and resource allocation. Also, this paper summarizes various methods used to solve the resource allocation problem in different computing environments. It analyses their performance on response time, energy efficiency, throughput, cost, service-consuming delay, convergence time, and latency. This is a long article, but I believe it is worth reading to have a baseline understanding of the broader range of resource allocation problems in a cloud computing environment.
This paper presented a comprehensive taxonomic review and analysis of recent metaheuristic scheduling techniques using exhaustive evaluation criteria in the cloud and fog environment. This is an open-source article and a good read for those just starting their research work based on metaheuristic approaches.
So this is it; I hope you found this helpful.
It’s just another try to make my contribution to the Cloudsim research community.
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