Towards Quantum Computing Algorithms for Datacenter Workload Predictions

Qazi, Kashifuddin; Aizenberg, Igor

doi:10.1109/cloud.2018.00131

Cited by 9 publications

(10 citation statements)

References 17 publications

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“…Berdasarkan penelitian [1] penulis menemukan bahwa prediksi nilai tukar dapat digunakan untuk mencapai kesuksesan dalam bidang investasi dan bisnis, sehingga perlu 154 Majalah Ilmiah Teknologi Elektro, Vol. 20, No.1, Januari -Juni 2021ISSN 1693-2951 Putu Risanti Iswardani : Peramalan Nilai Tukar Rupiah… diadakan prediksi nilai tukar agar perkiraan keuangan di dalam sebuah bisnis dan investasi dapat diperkirakan secara akurat.…”

Section: Pendahuluanunclassified

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Peramalan Nilai Tukar Rupiah Terhadap Mata Uang Negara Asia Menggunakan Metode Quantum Neural Network

Iswardani¹,

Sudarma²,

Jasa³

2021

JTE

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Currency is a standardized payment instrument used all around the world. Almost each country has their own currency, and it has a variable value. Asia is one of the biggest continent in the world. Most tourists whom visit Indonesia come from Asia. Thus, that makes the most currency exchanged in Indonesia are currencies from Asia. Currency exchange rate difference between one currency and another affected by several factor. One of those factor is inflation in its country. To overcome that issue, one needs a prediction system that can be used to predict the exchange rate of currencies in the future. Quantum Neural Network is used in this research to predict Indonesian Rupiah exchange rate value to other currency in Asia. Singapore, Hongkong and Japan currencies are particularly used in this research. Results obtained from this research are accuracies. Quantum Neural Network produces 99.78% accuracy on Singapore Dollar to Indonesian Rupiah exchange rate, 99.57% on Hongkong Dollar to Indonesian Rupiah, and 99.60% on Japanese Yen to Indonesian Rupiah.

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Section: Pendahuluanunclassified

“…Pada jurnal [1], mengusulkan metode Hidden Markov Model untuk memprdiksi nilai tukar Rupiah terhadap Dollar Amerika. Penelitian tersebut melakukan pengujian Mean Absolute Presentage Error (MAPE) dengan hasil presentasi sebesar 4.13%.…”

Section: Pendahuluanunclassified

Peramalan Nilai Tukar Rupiah Terhadap Mata Uang Negara Asia Menggunakan Metode Quantum Neural Network

Iswardani¹,

Sudarma²,

Jasa³

2021

JTE

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“…To deal with these challenges, many prediction techniques based on machine learning are widely used and have proven to be superior to the mathematical algorithms above [6], [8], [9], [23], [24]. Most of them require a training phase based on large-scale historical data.…”

Section: Related Workmentioning

confidence: 99%

“…A feedforward artificial neural network predictor was provided by Duy et al [23] to forecast the workload in a computational grids host. Qazi and Aizenberg [24] proposed a complex-valued neural network datacenter workload prediction approach. Kumar and Singh [8] combined a self-adaptive differential evolution method and an artificial neural network to predict workload in cloud computing systems.…”

Section: Related Workmentioning

confidence: 99%

Large-Scale Computing Systems Workload Prediction Using Parallel Improved LSTM Neural Network

Tang

2019

IEEE Access

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In recent years, large-scale computing systems have been widely used as an important part of the computing infrastructure. Resource management based on systems workload prediction is an effective way to improve application efficiency. However, accuracy and real-time functionalities are always the key challenges that perplex the systems workload prediction model. In this paper, we first investigate the dependence on historical workload in large-scale computing systems and build a day and time two-dimensional time-series workload model. We then design a two-dimensional long short-term memory (LSTM) neural network cell structure. Based on this, we propose an improved LSTM prediction model providing its mathematical description and an error back propagation method. Furthermore, to achieve systems resource management real-time requirement, we provide a parallel improved LSTM algorithm that uses a hidden layer week-based dependence and weights parallelization algorithm. The comparative studies, based on the actual workload of the Shanghai Supercomputer Center, demonstrate that our proposed improved LSTM neural network prediction model can achieve higher accuracy and real-time performance in large-scale computing systems. Workload prediction, computing systems, LSTM, neural network, parallel. INDEX TERMS

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“…Reliable workload prediction of monitored devices becomes critical in order to proactively manage the capacity of connected infrastructure, mitigate cyber security risks and simply respond early to the anomalous behaviour of the monitored IT infrastructure [1]. Accurate forecasting of the future host workload plays also a central role for robust scheduling and resources management in data centers and cloud computing and among many expected benefits could lead to reduced operational cost, for example in a form of eliminated or cut idle time of the devices [2], [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Deep Bi-Directional LSTM Networks for Device Workload Forecasting

Ruta

Cen

Vu³

2020

Annals of Computer Science and Information Systems

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Deep convolutional neural networks revolutionized the area of automated objects detection from images. Can the same be achieved in the domain of time series forecasting? Can one build a universal deep network that once trained on the past would be able to deliver accurate predictions reaching deep into the future for any even most diverse time series? This work is a first step in an attempt to address such a challenge in the context of a FEDCSIS'2020 Competition dedicated to network device workload prediction based on their historical time series data. We have developed and pre-trained a universal 3-layer bi-directional Long-Short-Term-Memory (LSTM) regression network that reported the most accurate hourly predictions of the weekly workload time series from the thousands of different network devices with diverse shape and seasonality profiles. We will also show how intuitive human-led post-processing of the raw LSTM predictions could easily destroy the generalization abilities of such prediction model.

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Towards Quantum Computing Algorithms for Datacenter Workload Predictions

Cited by 9 publications

References 17 publications

Peramalan Nilai Tukar Rupiah Terhadap Mata Uang Negara Asia Menggunakan Metode Quantum Neural Network

Peramalan Nilai Tukar Rupiah Terhadap Mata Uang Negara Asia Menggunakan Metode Quantum Neural Network

Large-Scale Computing Systems Workload Prediction Using Parallel Improved LSTM Neural Network

Deep Bi-Directional LSTM Networks for Device Workload Forecasting

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