Resource performance prediction is known to be useful in resource scheduling in the Grid. The disk I/O workload is another important factor that influences the performance of the CPU and the network which are commonly used in resource scheduling. In the case of disk I/O workload time-series, the adaptation of a prediction algorithm to new time-series should be rapid. Further, the prediction should ensure that the prediction error is minimum in the heterogeneous environment. The storage workload (i.e., the disk I/O load) is a dynamic variable. A prediction parameter based on the characteristics of the current workload must be prepared for prediction purposes. In this paper, we propose and implement the OPHB (On-Line Parameter History Bank). This is a method that stabilizes the incoming disk I/O workload time-series fairly quickly with the help of accurately determined ESM (Exponential Smoothing Method) parameters. The parameters are drawn from a history database. In the case of forecasting with ESM, a smoothing parameter must be specified in advance. If the parameter is statically estimated from observed data found in previous executions, the forecasts would be inaccurate because they do not capture the actual I/O behavior. The smoothing parameter has to be adjusted in accordance with the shape of the new disk I/O workload. The ESM algorithms utilise one of the accumulated parameter histories chronicled by OPHB's Deposit operation. When a new time-series is started, an appropriate parameter value is looked up in the Bank by OPHB's Lookup operation. This is used for the time-series. This process is fully adaptive. We evaluate the proposed method with SES (Single Exponential Smoothing) and ARRSES (Auto-Responsive SES) methods.
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DongWoo LEE, Rudrapatna Subramanyam RAMAKRISHNA, "Improving Disk I/O Load Prediction Using Statistical Parameter History in Online for Grid Computing" in IEICE TRANSACTIONS on Information,
vol. E89-D, no. 9, pp. 2484-2490, September 2006, doi: 10.1093/ietisy/e89-d.9.2484.
Abstract: Resource performance prediction is known to be useful in resource scheduling in the Grid. The disk I/O workload is another important factor that influences the performance of the CPU and the network which are commonly used in resource scheduling. In the case of disk I/O workload time-series, the adaptation of a prediction algorithm to new time-series should be rapid. Further, the prediction should ensure that the prediction error is minimum in the heterogeneous environment. The storage workload (i.e., the disk I/O load) is a dynamic variable. A prediction parameter based on the characteristics of the current workload must be prepared for prediction purposes. In this paper, we propose and implement the OPHB (On-Line Parameter History Bank). This is a method that stabilizes the incoming disk I/O workload time-series fairly quickly with the help of accurately determined ESM (Exponential Smoothing Method) parameters. The parameters are drawn from a history database. In the case of forecasting with ESM, a smoothing parameter must be specified in advance. If the parameter is statically estimated from observed data found in previous executions, the forecasts would be inaccurate because they do not capture the actual I/O behavior. The smoothing parameter has to be adjusted in accordance with the shape of the new disk I/O workload. The ESM algorithms utilise one of the accumulated parameter histories chronicled by OPHB's Deposit operation. When a new time-series is started, an appropriate parameter value is looked up in the Bank by OPHB's Lookup operation. This is used for the time-series. This process is fully adaptive. We evaluate the proposed method with SES (Single Exponential Smoothing) and ARRSES (Auto-Responsive SES) methods.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.9.2484/_p
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@ARTICLE{e89-d_9_2484,
author={DongWoo LEE, Rudrapatna Subramanyam RAMAKRISHNA, },
journal={IEICE TRANSACTIONS on Information},
title={Improving Disk I/O Load Prediction Using Statistical Parameter History in Online for Grid Computing},
year={2006},
volume={E89-D},
number={9},
pages={2484-2490},
abstract={Resource performance prediction is known to be useful in resource scheduling in the Grid. The disk I/O workload is another important factor that influences the performance of the CPU and the network which are commonly used in resource scheduling. In the case of disk I/O workload time-series, the adaptation of a prediction algorithm to new time-series should be rapid. Further, the prediction should ensure that the prediction error is minimum in the heterogeneous environment. The storage workload (i.e., the disk I/O load) is a dynamic variable. A prediction parameter based on the characteristics of the current workload must be prepared for prediction purposes. In this paper, we propose and implement the OPHB (On-Line Parameter History Bank). This is a method that stabilizes the incoming disk I/O workload time-series fairly quickly with the help of accurately determined ESM (Exponential Smoothing Method) parameters. The parameters are drawn from a history database. In the case of forecasting with ESM, a smoothing parameter must be specified in advance. If the parameter is statically estimated from observed data found in previous executions, the forecasts would be inaccurate because they do not capture the actual I/O behavior. The smoothing parameter has to be adjusted in accordance with the shape of the new disk I/O workload. The ESM algorithms utilise one of the accumulated parameter histories chronicled by OPHB's Deposit operation. When a new time-series is started, an appropriate parameter value is looked up in the Bank by OPHB's Lookup operation. This is used for the time-series. This process is fully adaptive. We evaluate the proposed method with SES (Single Exponential Smoothing) and ARRSES (Auto-Responsive SES) methods.},
keywords={},
doi={10.1093/ietisy/e89-d.9.2484},
ISSN={1745-1361},
month={September},}
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TY - JOUR
TI - Improving Disk I/O Load Prediction Using Statistical Parameter History in Online for Grid Computing
T2 - IEICE TRANSACTIONS on Information
SP - 2484
EP - 2490
AU - DongWoo LEE
AU - Rudrapatna Subramanyam RAMAKRISHNA
PY - 2006
DO - 10.1093/ietisy/e89-d.9.2484
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2006
AB - Resource performance prediction is known to be useful in resource scheduling in the Grid. The disk I/O workload is another important factor that influences the performance of the CPU and the network which are commonly used in resource scheduling. In the case of disk I/O workload time-series, the adaptation of a prediction algorithm to new time-series should be rapid. Further, the prediction should ensure that the prediction error is minimum in the heterogeneous environment. The storage workload (i.e., the disk I/O load) is a dynamic variable. A prediction parameter based on the characteristics of the current workload must be prepared for prediction purposes. In this paper, we propose and implement the OPHB (On-Line Parameter History Bank). This is a method that stabilizes the incoming disk I/O workload time-series fairly quickly with the help of accurately determined ESM (Exponential Smoothing Method) parameters. The parameters are drawn from a history database. In the case of forecasting with ESM, a smoothing parameter must be specified in advance. If the parameter is statically estimated from observed data found in previous executions, the forecasts would be inaccurate because they do not capture the actual I/O behavior. The smoothing parameter has to be adjusted in accordance with the shape of the new disk I/O workload. The ESM algorithms utilise one of the accumulated parameter histories chronicled by OPHB's Deposit operation. When a new time-series is started, an appropriate parameter value is looked up in the Bank by OPHB's Lookup operation. This is used for the time-series. This process is fully adaptive. We evaluate the proposed method with SES (Single Exponential Smoothing) and ARRSES (Auto-Responsive SES) methods.
ER -