Earticle

초록

영어

With the advent of serverless computing, cloud customers no longer needed to maintain and manage server environments directly. Instead, cloud service providers took on that role, managing and maintaining the server environment according to customer requests, a concept known as Function as a Service (FaaS). This service demonstrated improvements in operational costs and resource utilization over traditional cloud computing, offering various advantages such as enhanced scalability. However, a delay occurred in processing and returning results to user requests, a phenomenon referred to as the cold start problem. This paper proposed the Time Warming Allocation Engine (TWAE) to improve resource management and mitigate the cold start problem in Function as a Service. The proposed engine comprised a collection module, a learning module, a classification module, and an allocation module. Additionally, it utilized a list called Pre-Warming. Through this approach, it suggested directions for improving cold start issues and resource utilization according to different time periods.

목차

Abstract
1. Introduction
2. Related Work
3. Proposed System
3.1 Time Warming Allocation Engine (TWAE)
3.2 Time Warming Allocation Engine Sequence Diagram
3.3 Time Warming Allocation Engine Algorithm
4. EXPERIMENTS AND RESULTS
5. CONCLUSION
Acknowledgement
References

키워드

저자

• Gun-Woo Kim [ Master, Department of Computer Science, Kwangwoon University, Korea ]
• Seok-Jae Moon [ Professor, Department of Artificial Intelligence Institute of Information Technology, KwangWoon University, Korea ]
• Byung-Joon Park [ Professor, Department of Computer Science, Kwangwoon University, Korea. ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380