A Consensus-based Auto-scaling Approach For Serverless Environments

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Kashaniyan Mobina; Ashtiani Mehrdad; Ghassemi Amirhossein

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2024 | Issue:10

Download Full-Text

View:

Download:

Information Seminar Paper

Title

A Consensus-based Auto-scaling Approach For Serverless Environments

Author(s)

Kashaniyan Mobina | Ashtiani Mehrdad | Ghassemi Amirhossein

Keywords

Autoscaling

Cloud Environment

Machine Learning

Workload Prediction

Ensemble Learning

Abstract

Efficient management of computing resources has always been a significant concern for users. An automatic scaling system can help in managing hardware resources by adapting to the system's performance history. It can increase or decrease resources automatically, without human intervention, based on predefined criteria. This ensures smooth program execution without any disruption caused by changes in the operating environment. This study focuses on serverless environments, which rely on functions. We model these functions using graph theory, analyze their dependencies, and identify the most critical bottlenecks in the graph. We then use two approaches, supervised and unsupervised, to predict the scalability of bottleneck resources. To be more sure of the scaling decision, the consensus mechanism compares the predictions of the models, and the best model's result is considered the final scaling decision, which creates consistency between the results obtained from the methods. Results show that supervised approaches perform better than unsupervised approaches in the automatic scaling problem. The models implemented in this research can determine the scaling result with 98% accuracy, which is a 2. 5% improvement compared to previous works.

Cites

No record.

References

No record.

Cite

APA: Copy

Kashaniyan, Mobina, Ashtiani, Mehrdad, & Ghassemi, Amirhossein. (2024). A Consensus-based Auto-scaling Approach For Serverless Environments. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/1147677/en

Vancouver: Copy

Kashaniyan Mobina, Ashtiani Mehrdad, Ghassemi Amirhossein. A Consensus-based Auto-scaling Approach For Serverless Environments. 2024. Available from: https://sid.ir/paper/1147677/en

IEEE: Copy

Mobina Kashaniyan, Mehrdad Ashtiani, and Amirhossein Ghassemi, “A Consensus-based Auto-scaling Approach For Serverless Environments,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2024, [Online]. Available: https://sid.ir/paper/1147677/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops