A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing
المؤلفون المشاركون
المصدر
Journal of Electrical and Computer Engineering
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-03-14
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
Dynamic resource scheduling is a critical activity to guarantee quality of service (QoS) in cloud computing.
One challenging problem is how to predict future host utilization in real time.
By predicting future host utilization, a cloud data center can place virtual machines to suitable hosts or migrate virtual machines in advance from overloaded or underloaded hosts to guarantee QoS or save energy.
However, it is very difficult to accurately predict host utilization in a timely manner because host utilization varies very quickly and exhibits strong instability with many bursts.
Although machine learning methods can accurately predict host utilization, it usually takes too much time to ensure rapid resource allocation and scheduling.
In this paper, we propose a hybrid method, EEMD-RT-ARIMA, for short-term host utilization prediction based on ensemble empirical mode decomposition (EEMD), runs test (RT), and autoregressive integrated moving average (ARIMA).
First, the EEMD method is used to decompose the nonstationary host utilization sequence into relatively stable intrinsic mode function (IMF) components and a residual component to improve prediction accuracy.
Then, efficient IMF components are selected and then reconstructed into three new components to reduce the prediction time and error accumulation due to too many IMF components.
Finally, the overall prediction results are obtained by superposing the prediction results of three new components, each of which is predicted by the ARIMA method.
An experiment is conducted on real host utilization traces from a cloud platform.
We compare our method with the ARIMA model and the EEMD-ARIMA method in terms of error, effectiveness, and time-cost analysis.
The results show that our method is a cost-effective method and is more suitable for short-term host utilization prediction in cloud computing.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Chen, Jing& Wang, Yinglong. 2019. A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing. Journal of Electrical and Computer Engineering،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1173690
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Chen, Jing& Wang, Yinglong. A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing. Journal of Electrical and Computer Engineering No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1173690
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Chen, Jing& Wang, Yinglong. A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing. Journal of Electrical and Computer Engineering. 2019. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1173690
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1173690
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر