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.
초록·키워드
Abstract Cloud computing delivers real-time customizable capabilities and functionalities over the Internet, thereby revolutionizing the computing industry. Task scheduling in the cloud model has attracted the researchers’ interest owing to its complexity, heterogeneity, and dynamic properties because tasks vary in size and processing capacity. Consequently, poor scheduling techniques can lead to higher energy usage and service level agreements (SLAs). The literature on task scheduling has mainly dealt with designing and developing scheduling algorithms rather than examining how uncertain factors, such as network bandwidth and instruction rate, affect scheduling. This study proposes a novel task scheduling method using the Osprey Optimization Algorithm (OOA) by examining the impact of the network capacity and instruction rate. To further enhance the search capabilities of the classic OOA and address challenges such as sluggish convergence and local optimum behavior, the OOA is modified with novel methods, namely, Roulette fitness-distance-balance-based (RFDB) selection, Brownian movement, and Lévy flight. Brownian movement and Lévy flight strategies improve exploration capabilities, whereas RFDB ensures a balanced search for global optimal solutions. The simulation results demonstrated that EOOA achieved significant improvements, reducing the makespan by 27%, energy consumption by 36%, and SLA violations by 50% compared to baseline algorithms, highlighting its superior performance in task scheduling across diverse workloads.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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