truong duy - Academia.edu (original) (raw)

Papers by truong duy

IEEE Transactions on Signal Processing, 2000

A lattice structure for an -channel linear-phase perfect reconstruction filter bank (LPPRFB) base... more A lattice structure for an -channel linear-phase perfect reconstruction filter bank (LPPRFB) based on the singular value decomposition (SVD) is introduced. The lattice can be proven to use a minimal number of delay elements and to completely span a large class of LPPRFB's: All analysis and synthesis filters have the same FIR length, sharing the same center of symmetry. The lattice also structurally enforces both linear-phase and perfect reconstruction properties, is capable of providing fast and efficient implementation, and avoids the costly matrix inversion problem in the optimization process. From a block transform perspective, the new lattice can be viewed as representing a family of generalized lapped biorthogonal transform (GLBT) with an arbitrary number of channels and arbitrarily large overlap. The relaxation of the orthogonal constraint allows the GLBT to have significantly different analysis and synthesis basis functions, which can then be tailored appropriately to fit a particular application. Several design examples are presented along with a high-performance GLBT-based progressive image coder to demonstrate the potential of the new transforms.

International Journal of Control Automation and Systems, 2010

Supervisory control and data acquisition (SCADA) software which is suitable to distributed contro... more Supervisory control and data acquisition (SCADA) software which is suitable to distributed control systems is a demand for system developers because the characteristics of existing SCADA software packages are hard to satisfy the requirements of distributed systems. For the strengths of component-oriented techniques, this paper proposes a component-oriented architecture of SCADA software to satisfy the demand of distributed control systems. Design pattern and OPC (OLE for Process Control) technology are also used to make the openness for the architecture.

International Journal of Parallel, Emergent and Distributed Systems, 2010

The capability to predict the host load of a system is significant for computational grids to mak... more The capability to predict the host load of a system is significant for computational grids to make efficient use of shared resources. This work attempts to improve the accuracy of host load predictions by applying a neural network predictor to reach the goal of best performance and load balance. We describe the feasibility of the proposed predictor in a dynamic environment, and perform experimental evaluation using collected load traces. The results show that the neural network achieves consistent performance improvement with surprisingly low overhead in most cases. Compared with the best previously proposed method, our typical 20:10:1 network reduces the mean of prediction errors by approximately up to 79%. The training and testing time is extremely low, as this network needs only a couple of seconds to be trained with more than 100,000 samples, in order to make tens of thousands of accurate predictions within just a second.

With energy shortages and global climate change leading our concerns these days, the power consum... more With energy shortages and global climate change leading our concerns these days, the power consumption of datacenters has become a key issue. Obviously, a substantial reduction in energy consumption can be made by powering down servers when they are not in use. This paper aims at designing, implementing and evaluating a Green Scheduling Algorithm integrating a neural network predictor for optimizing server power consumption in Cloud computing. We employ the predictor to predict future load demand based on historical demand. According to the prediction, the algorithm turns off unused servers and restarts them to minimize the number of running servers, thus minimizing the energy use at the points of consumption to benefit all other levels. For evaluation, we perform simulations with two load traces. The results show that the PP20 mode can save up to 46.3% of power consumption with a drop rate of 0.03% on one load trace, and a drop rate of 0.12% with a power reduction rate of 46.7% on the other.

A Prediction-Based Green Scheduler for Datacenters in Clouds

The capability to predict the host load of a system is significant for computational grids to mak... more The capability to predict the host load of a system is significant for computational grids to make efficient use of shared resources. This paper attempts to improve the accuracy of host load predictions by applying a neural network predictor to reach the goal of best performance and load balance. We describe feasibility of the proposed predictor in a dynamic environment, and perform experimental evaluation using collected load traces. The results show that the neural network achieves a consistent performance improvement with surprisingly low overhead. Compared with the best previously proposed method, the typical 20:10:1 network reduces the mean and standard deviation of the prediction errors by approximately 60% and 70%, respectively. The training and testing time is extremely low, as this network needs only a couple of seconds to be trained with more than 100,000 samples in order to make tens of thousands of accurate predictions within just a second.

IEEE Transactions on Signal Processing, 2000

A lattice structure for an -channel linear-phase perfect reconstruction filter bank (LPPRFB) base... more A lattice structure for an -channel linear-phase perfect reconstruction filter bank (LPPRFB) based on the singular value decomposition (SVD) is introduced. The lattice can be proven to use a minimal number of delay elements and to completely span a large class of LPPRFB's: All analysis and synthesis filters have the same FIR length, sharing the same center of symmetry. The lattice also structurally enforces both linear-phase and perfect reconstruction properties, is capable of providing fast and efficient implementation, and avoids the costly matrix inversion problem in the optimization process. From a block transform perspective, the new lattice can be viewed as representing a family of generalized lapped biorthogonal transform (GLBT) with an arbitrary number of channels and arbitrarily large overlap. The relaxation of the orthogonal constraint allows the GLBT to have significantly different analysis and synthesis basis functions, which can then be tailored appropriately to fit a particular application. Several design examples are presented along with a high-performance GLBT-based progressive image coder to demonstrate the potential of the new transforms.

International Journal of Control Automation and Systems, 2010

Supervisory control and data acquisition (SCADA) software which is suitable to distributed contro... more Supervisory control and data acquisition (SCADA) software which is suitable to distributed control systems is a demand for system developers because the characteristics of existing SCADA software packages are hard to satisfy the requirements of distributed systems. For the strengths of component-oriented techniques, this paper proposes a component-oriented architecture of SCADA software to satisfy the demand of distributed control systems. Design pattern and OPC (OLE for Process Control) technology are also used to make the openness for the architecture.

International Journal of Parallel, Emergent and Distributed Systems, 2010

The capability to predict the host load of a system is significant for computational grids to mak... more The capability to predict the host load of a system is significant for computational grids to make efficient use of shared resources. This work attempts to improve the accuracy of host load predictions by applying a neural network predictor to reach the goal of best performance and load balance. We describe the feasibility of the proposed predictor in a dynamic environment, and perform experimental evaluation using collected load traces. The results show that the neural network achieves consistent performance improvement with surprisingly low overhead in most cases. Compared with the best previously proposed method, our typical 20:10:1 network reduces the mean of prediction errors by approximately up to 79%. The training and testing time is extremely low, as this network needs only a couple of seconds to be trained with more than 100,000 samples, in order to make tens of thousands of accurate predictions within just a second.

With energy shortages and global climate change leading our concerns these days, the power consum... more With energy shortages and global climate change leading our concerns these days, the power consumption of datacenters has become a key issue. Obviously, a substantial reduction in energy consumption can be made by powering down servers when they are not in use. This paper aims at designing, implementing and evaluating a Green Scheduling Algorithm integrating a neural network predictor for optimizing server power consumption in Cloud computing. We employ the predictor to predict future load demand based on historical demand. According to the prediction, the algorithm turns off unused servers and restarts them to minimize the number of running servers, thus minimizing the energy use at the points of consumption to benefit all other levels. For evaluation, we perform simulations with two load traces. The results show that the PP20 mode can save up to 46.3% of power consumption with a drop rate of 0.03% on one load trace, and a drop rate of 0.12% with a power reduction rate of 46.7% on the other.

A Prediction-Based Green Scheduler for Datacenters in Clouds

The capability to predict the host load of a system is significant for computational grids to mak... more The capability to predict the host load of a system is significant for computational grids to make efficient use of shared resources. This paper attempts to improve the accuracy of host load predictions by applying a neural network predictor to reach the goal of best performance and load balance. We describe feasibility of the proposed predictor in a dynamic environment, and perform experimental evaluation using collected load traces. The results show that the neural network achieves a consistent performance improvement with surprisingly low overhead. Compared with the best previously proposed method, the typical 20:10:1 network reduces the mean and standard deviation of the prediction errors by approximately 60% and 70%, respectively. The training and testing time is extremely low, as this network needs only a couple of seconds to be trained with more than 100,000 samples in order to make tens of thousands of accurate predictions within just a second.