این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
پژوهشنامه پردازش و مدیریت اطلاعات، جلد ۴۰، شماره ویژه نامه انگلیسی۳، صفحات ۰-۰
عنوان فارسی A Review of QoS-Driven Task Scheduling Algorithms and Their Impact on Data Quality in Process Management
چکیده فارسی مقاله The term "cloud computing" has been widely studied and used by major corporations ever since it was originally created. From the point of view of cloud computing, a variety of research topics and viewpoints have been considered, dealt with, and handled. Some examples of these include resource management, cloud security, and energy efficiency, to mention just a few. But, cloud computing is still faced with the significant obstacle of determining how to most effectively schedule tasks and manage available resources. We need effective scheduling strategies to handle these resources due to the size and dynamic resource provisioning of current data centres. The purpose of this work is to provide an overview of the various task scheduling methods that are utilized in the cloud computing environment till date. An attempt has been made to categorize current methods, investigate problems, and identify important problems that are currently present in this area. Our data reveals that 34% of researchers are concentrating on makespan for QoS (Quality of Service) metrics, 17% on cost, 15% on load balancing, 10% on deadline, and 9% on energy usage. Other criteria for the QoS parameter contribute far less than the ones mentioned above. According to this study, the scheduling algorithms that are used by researchers 80% of the time include the genetic algorithm in bio-inspired systems and particle swarm optimization in swarm intelligence. According to the available literature, 70% of the studies have utilized cloudsim as their simulation tool of choice. This paper also highlights a variety of ongoing problems and potential future directions in QoS-driven task scheduling algorithms for use in cloud computing environments.
کلیدواژه‌های فارسی مقاله کیفیت اطلاعات،عملکرد نوآورانه،تسهیم دانش،مدیریت فرایند کسب و کار،شرکت های کوچک و متوسط،
عنوان انگلیسی A Review of QoS-Driven Task Scheduling Algorithms and Their Impact on Data Quality in Process Management
چکیده انگلیسی مقاله The term "cloud computing (CC)" has been extensively studied and employed by major corporations ever since its inception. Within the realm of cloud computing, various research topics and perspectives have been explored, including resource management, cloud security, and energy efficiency. This paper delves into the intersection of data quality and business process management through the lens of cloud computing. Specifically, it examines how QoS-driven task scheduling algorithms in cloud environments can enhance data quality and optimize business processes. Cloud computing is still faced with the significant obstacle of determining how to most effectively schedule tasks and manage available resources. We need effective scheduling strategies to handle these resources due to the size and dynamic resource provisioning of current data centres. The purpose of this work is to provide an overview of the various task scheduling methods that are utilized in the cloud computing environment till date. An attempt has been made to categorize current methods, investigate problems, and identify important problems that are currently present in this area. Our data reveals that 34% of researchers are concentrating on makespan for QoS (Quality of Service) metrics, 17% on cost, 15% on load balancing, 10% on deadline, and 9% on energy usage. Other criteria for the QoS parameter contribute far less than the ones mentioned above. According to this study, the scheduling algorithms that are used by researchers 80% of the time include the genetic algorithm in bio-inspired systems and particle swarm optimization in swarm intelligence. According to the available literature, 70% of the studies have utilized cloudsim as their simulation tool of choice. Our finding suggests that, Current methodologies mainly employ genetic algorithms and particle swarm optimization, with CloudSim as a popular simulation tool. Ongoing work emphasizes refining scheduling strategies to enhance resource management in dynamic data center environments, offering vital insights into future QoS-driven scheduling algorithms for cloud computing.
کلیدواژه‌های انگلیسی مقاله Resource Allocation,Meta-Heuristic,Cloud computation,Resource scheduling,optimization techniques,Task Scheduling
نویسندگان مقاله Anupam Yadav |
Department of Computer Engineering and Application; GLA University; Mathura, India

Ashish Sharma |
Department of Computer Engineering and Application; GLA University; Mathura, India

نشانی اینترنتی https://jipm.irandoc.ac.ir/article_722070_3e08e01d411ab5d654ab91ab52808222.pdf
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده fa
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات