An optimized cloud edge server placement method based on k-mean algorithm

Chang Jian; Gong Yan; Liu Cheng; Yang Muchuan; Liu Jiayu; Jiang He

doi:10.1117/12.2671109

28 April 2023 An optimized cloud edge server placement method based on k-mean algorithm

Chang Jian, Gong Yan, Liu Cheng, Yang Muchuan, Liu Jiayu, Jiang He

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126105B (2023) https://doi.org/10.1117/12.2671109
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

In cloud-based edge computing systems, edge server placement is crucial to many applications response time. Generally, edge server placement is always modeled as a multi-objective optimization problem and solved with integer programming algorithms. However, these algorithms are not well scalable, and parameters used in these algorithms depend heavily on experiences. In this paper, we propose an optimized k-mean based edge server placement method. We use k-mean to cluster system sources and application loads and sort the application load to apply the most applicable server to the application. Experimental studies over synthetic data sets validate effectiveness of the method.

Citation Download Citation

Chang Jian, Gong Yan, Liu Cheng, Yang Muchuan, Liu Jiayu, and Jiang He "An optimized cloud edge server placement method based on k-mean algorithm", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126105B (28 April 2023); https://doi.org/10.1117/12.2671109

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available