인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 저자정보
피인용 0
초록·키워드
A decentralized computing system distributed among data-generating hardware and the cloud is called fog computing (FC). The ability to place resources to improve performance is given to users by such a flexible structure. On the other hand, low-delay services and limited resources make it difficult to use new virtualization technologies for task scheduling with resource management in fog computing. Several studies have examined scheduling and load balancing (LB) in cloud computing (CC). Nevertheless, countless LB initiatives have been proposed in fog environments. Task scheduling using a Dual interactive Wasserstein generative adversarial network (DIWGAN) optimized with an Improved Dwarf Mongoose Optimization algorithm is proposed to classify the suitable and not suitable server for the process (ESA-WF-FE-DIWGAN). Initially, the "Cloud-Fog Computing Dataset is used. Afterward, the dataset is fed to the Fog Resource Monitor (FRM). Here, the statistical features like storage, computing, and RAM are extracted. Subsequently, the extracted features are given to DIWGAN to classify the suitable server and the unsuitable server for the process. The scheduling process was done using the Improved Dwarf Mongoose Optimization algorithm. The proposed approach achieves 3.101%, which was a 7.12% higher make span: 24.13% and 13.04% lower total cost; 2.292% and 5.365% higher ARU compared to the existing methods.
본문·목차
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