مقاله انگلیسی رایگان در مورد پیشبینی بار در رایانش ابری با تبدیل موجک گسسته و BiGRU – الزویر ۲۰۲۳
مشخصات مقاله | |
ترجمه عنوان مقاله | پیشبینی بار میزبان در رایانش ابری با تبدیل موجک گسسته (DWT) و شبکه واحد بازگشتی دروازهای دوطرفه (BiGRU) |
عنوان انگلیسی مقاله | Host load prediction in cloud computing with Discrete Wavelet Transformation (DWT) and Bidirectional Gated Recurrent Unit (BiGRU) network |
نشریه | الزویر |
انتشار | مقاله سال ۲۰۲۳ |
تعداد صفحات مقاله انگلیسی | ۱۸ صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس میباشد |
نمایه (index) | Scopus – Master Journal List – JCR |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
۶٫۲۳۱ در سال ۲۰۲۰ |
شاخص H_index | ۱۰۹ در سال ۲۰۲۲ |
شاخص SJR | ۱٫۳۰۱ در سال ۲۰۲۰ |
شناسه ISSN | ۱۸۷۳-۷۰۳X |
شاخص Quartile (چارک) | Q1 در سال ۲۰۲۰ |
فرضیه | ندارد |
مدل مفهومی | دارد |
پرسشنامه | ندارد |
متغیر | دارد |
رفرنس | دارد |
رشته های مرتبط | مهندسی کامپیوتر |
گرایش های مرتبط | هوش مصنوعی – مهندسی نرم افزار – مهندسی الگوریتم ها و محاسبات |
نوع ارائه مقاله |
ژورنال |
مجله | ارتباطات کامپیوتری – Computer Communications |
دانشگاه | Department of Computer Science and Engineering and IT, School of Electrical and Computer Engineering, Shiraz University, Iran |
کلمات کلیدی | رایانش ابری – پیشبینی بار میزبان – یادگیری عمیق – تبدیل موجک گسسته (DWT) – واحد دوطرفه دروازهای بازگشتی (BiGRU) |
کلمات کلیدی انگلیسی | Cloud computing – Host load prediction – Deep learning – Discrete Wavelet Transformation (DWT) – Bidirectional Gated-Recurrent Unit (BiGRU) |
شناسه دیجیتال – doi |
https://doi.org/10.1016/j.comcom.2022.11.018 |
لینک سایت مرجع | https://www.sciencedirect.com/science/article/abs/pii/S0140366422004479 |
کد محصول | e17303 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
Abstract ۱ Introduction ۲ Related work ۳ Background ۴ Proposed method ۵ Expriments and discussion ۶ Conclusion Ethical approval Declaration of competing interest References |
بخشی از متن مقاله: |
Abstract Providing pay-as-you-go storage and computing services have contributed to the widespread adoption of cloud computing. Using virtualization technology, cloud service providers can execute several instances on a single physical server, maximizing resource utilization. A challenging issue in cloud data centers is that available resources are rarely fully utilized. The server utilization rate is poor and often below 30%. An accurate host workload prediction enhances resource allocation resulting in more efficient resource utilization. Recently, numerous methods based on deep learning for predicting cloud computing workload have been developed. An efficient strategy must predict long-term dependencies on nonstationary host workload data and be quick enough to respond to incoming requests. This study employs a Bidirectional Gated-Recurrent Unit (BiGRU), Discrete Wavelet Transformation (DWT), and an attention mechanism to improve the host load prediction accuracy. DWT is used to decompose input data into sub-bands with different frequencies and to extract patterns from nonlinear and nonstationary data in order to improve prediction accuracy. The extracted features are fed into BiGRu to predict future workload. The attention mechanism is used in order to extract the temporal correlation features. This hybrid model was evaluated with cluster data sets from Google and Alibaba. Experimental results reveal that our method improves prediction accuracy by 3% to 56% compared to a variety of state-of-the-art methods. Introduction Cloud computing indicates the on-demand accessibility of computer system resources, particularly data storage and computing resources, enabling the users to manage without direct intervention [1], [2]. Organizations can rent cloud computing services as an alternative to investing in their own computing infrastructure or data centers. [3]. The various pay-as-you-go cloud services not only enable clients to purchase resources on-demand [4] but also enables the provision of an infinite amount of resource capacity (e.g., CPU, memory, network, and disk) at a reasonable price without investing in infrastructure or incurring additional expenditures for maintenance [5], [6], [7]. The average capacity utilization rate for regular deployments is less than 40%, although businesses require a relatively large number of servers and other resources to ensure the quality of service (QoS) during peak periods [8], [9]. Fig. 1 depicts boxplots of CPU consumption over two working days for 50 Google cluster machines. Each record of this data represents the cumulative consumption in 5 min. As seen in Fig. 1, however, the average CPU utilization rarely exceeds 50%, and in most cases, it is less than 30%. Conclusion This article proposes a host workload prediction method in cloud computing by combining the DWT, BiGRU model and attention mechanism. In addition to learning long-term dependencies in BiGRU, DWT can decompose nonlinear and nonstationary data into predictable subbands in order to predict future host workload in cloud computing. The proposed approach was evaluated using two real-time host load trace datasets, the Google Cluster Database and the Alibaba Cluster. According to the experimental results, basic techniques cannot learn nonlinear data, mainly when random fluctuations occur in the data. However, the model presented in the proposed method shows good compatibility and achieves better results than DWT-LSTM, DWT-BPNN, DWT-SVR, LSTM, BPNN, and SVR in both datasets. Since distributed computing is increasingly oriented toward lightweight virtualization technologies such as containers, we will predict container workloads in the Docker and Kubernetes environments in future research. We run various applications in these environments, and a series of simulations will run to generate tasks and measure the container’s CPU load. Then, the workload will be predicted using machine learning models, and resource provisioning will be performed. |