| Autor: |
Hosseinkhani Saman, Saini Hemraj, Feng Zhangying, Wahbeh Ayman, Obeidat Nathir, Gao Jinglin, Al-Essa Mohammad, Btoush Asma, Fan Wenzhong, Aldeen AlRyalat Saif, Nazari Mahboobeh, Omar Bakr Riham, Alipour Mohsen, Wang Xianqin, Hu Yujie, Abu-Khalaf Mahmoud, Wen Congcong, Hu Guang, Wang Haidong, He Wenjie, Zhou Caiping, Cheraghi Roya, Obeidat Zaina, Zhu Lili, Obeidat Mohammed, Jin Yongxi, Sharma Shivi, Wang Guangxin, Rajendra Shirsath Nitin, Yang Xuezhi, Wang Mingxia, Al Oweidat Khaled, Yu Mengdi, Wang Hongzhe, Kamalgiri Goswami Ajaygiri, Ren Huan |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
International Journal of Sensors, Wireless Communications and Control. 10:640-648 |
| ISSN: |
2210-3279 |
| Popis: |
With the fast development of cloud computing methods, exponential growth is faced by several users. It is complicated for traditional data centers to perform several jobs in real-time because of inadequate bandwidth resources. Therefore, the method of fog computing is recommended for supporting and providing fast cloud services. It is not a substitute but is a powerful complement to cloud computing. The reduction of energy consumption through the notion of fog computing has certainly been a challenge for current researchers, industries, and communities. Various industries, including finance and healthcare, require a rich resource-based platform for processing large amounts of data with cloud computing across fog architecture. The consumption of energy across fog servers relies on allocating techniques for services (user requests). It facilitates processing at the edge with the probability of interacting with the cloud. This article proposed energy-aware scheduling by using Artificial Neural Network (ANN) and Modified Multiobjective Job Scheduling (MMJS) techniques. The emphasis of the work is on the reduction of energy consumption rate with less Service Level Agreement (SLA) violation in fog computing for data centers. The result shows that there is a 3.9% reduction in SLA violation when a multiobjective function with Artificial Neural Network is applied. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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