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Short term workload prediction: The reservoir computing approach

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  1. Tez No: 918800
  2. Yazar: ALPER ALİMOĞLU
  3. Danışmanlar: Belirtilmemiş.
  4. Tez Türü: Yüksek Lisans
  5. Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control
  6. Anahtar Kelimeler: Echo State Network, Recurrent Neural Network, Machine Learning, Recursive and n-point ahead direct Time Series Prediction
  7. Yıl: 2014
  8. Dil: İngilizce
  9. Üniversite: State Unıversıty Of New York At Bınghamton
  10. Enstitü: Yurtdışı Enstitü
  11. Ana Bilim Dalı: Belirtilmemiş.
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: Belirtilmemiş.

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Özet (Çeviri)

'What's going to happen in the future?' Time series data holds the answers, and machine learning is the leading edge method for interpreting and using the data. Machine learning is a research field that revolves around algorithms by which a computer learns from obtained data, which is from past experiences. The machine learning technique used in this work is called reservoir computing. It is a simplified structure of the human brain that uses a randomly created, recurrent network of artificial neurons called an echo state network. Reservoir computing is dependent on a nonlinear dynamic system. This system is fed with an input sequence that is mapped to a higher dimension space. During the process of training the system, only a linear readout is altered by the state of the reservoir. The input connections and recurrent connections in the system are left unchanged. This approach greatly decreases the time required to train this recurrent neural network, but does not affect the performance of the tasks. Saving energy should not be the foremost priority when IT service demand cannot be met in accordance with the predefined SLAs. The main goal is to design the DC where the IT capacities are just at the right level to meet the instantaneous demand to save energy. However, the main challenge is to predict workload usage in advance since it takes a finite amount of time to activate the servers and to adjust the cooling. The server activation takes a few minutes so it is imperative to have techniques that predict three to a few minutes in advance. Our objective is to predict the workload for a short-term (three minutes ahead) prediction interval using the reservoir computing approach. This research is concentrated on understanding the differences between direct n-point ahead and recursive predictions using reservoir systems in short term predictions.

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