116 Slices
Medium 9781601323262

Cost-aware Short-term Load Forecasting of Power System

Hamid R. Arabnia, Lou D'Alotto, Hiroshi Ishii, Minoru Ito, Kazuki Joe, Hiroaki Nishikawa, Georgios Sirakoulis, William Spataro, Giuseppe A. Trunfio, George A. Gravvanis, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti CSREA Press PDF

364

Int'l Conf. Par. and Dist. Proc. Tech. and Appl. | PDPTA'14 |

Cost-aware Short-term Load Forecasting of Power

System

Kai-Chao Yang, Chung-Chieh Huang, and Jia-Shung Wang

Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan

National Chip Implementation Center, Hsinchu, Taiwan

Abstract - To precisely forecast the load in the power system, numerous training data have to be collected from sensors. As time goes by, the continuously increasing data cause more and more storage and bandwidth requirements. In this article, we present a cost-aware short-term load forecasting method which uses zonal prediction and multi-resolution data compression to reduce data size without significantly influence the forecasting accuracy. The user can collect just partial data from sensors for forecasting under a predefined tolerable prediction error, such that the system is robust subject to the precision degradation due to storage or bandwidth limitation. The experimental results demonstrate that 92.72% bandwidth can be saved with the prediction errors slightly increasing from 1.08% to 1.64%. Moreover, we also propose a similar-hour selection approach which helps the neural network to predict the next hour load. By integrating the proposed zonal prediction, similar-hour selection, and three-layer neural network together, the simulation results show that the prediction errors can be reduced to 0.95% ~ 1.18%.

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Medium 9781601323262

A Novel Information Sharing Architecture Constructed by Broadcast Based Information Sharing System (BBISS)

Hamid R. Arabnia, Lou D'Alotto, Hiroshi Ishii, Minoru Ito, Kazuki Joe, Hiroaki Nishikawa, Georgios Sirakoulis, William Spataro, Giuseppe A. Trunfio, George A. Gravvanis, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti CSREA Press PDF

534

Int'l Conf. Par. and Dist. Proc. Tech. and Appl. | PDPTA'14 |

A Novel Information Sharing Architecture Constructed by Broadcast Based Information Sharing System (BBISS)

Keisuke Utsu1, Chee Onn Chow2, Hiroaki Nishikawa3, and Hiroshi Ishii1

1

School of Information and Telecommunication Engineering, Tokai University, Minato, Tokyo, Japan

2

Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

3

Graduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan

Abstract - Existing communication infrastructure may be unavailable in disaster situations. Under the situations, it is difficult to share information composed of multiple packets, such as text, image, and audio data in the communication infrastructure unavailable areas. To enable information sharing without using existing communication infrastructure in the areas, we have proposed a novel system “BroadcastBased Information Sharing System (BBISS)”. The paper evaluates the performance of BBISS by the network simulations. The simulation results can conclude that the proposed method achieves the high information reachability without significantly increasing of the number of packet exchanges.

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Medium 9781601323262

Scalable Self-Tuning Implementation of Smith-Waterman Algorithm for Multicore CPUs

Hamid R. Arabnia, Lou D'Alotto, Hiroshi Ishii, Minoru Ito, Kazuki Joe, Hiroaki Nishikawa, Georgios Sirakoulis, William Spataro, Giuseppe A. Trunfio, George A. Gravvanis, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti CSREA Press PDF

42

Int'l Conf. Par. and Dist. Proc. Tech. and Appl. | PDPTA'14 |

Scalable Self-Tuning Implementation of

Smith-Waterman Algorithm for Multicore CPUs

Faisal Sikder and Dilip Sarkar

Keywords-CPU Oblivious, Scalable Algorithm, Multicore CPU,

OpenMP, Parallel algorithm, Shared memory architecture,

Smith-Waterman algorithm.

Abstract—Improved version of the Smith-Waterman algorithm

(SWA) is most widely used for local alignment of a pattern (or query) sequence with a Database (DB) sequence. This dynamicprogramming algorithm is computation intensive. To reduce time for computing alignment score matrix, parallel versions have been implemented on GPUs and multicore CPUs. These parallel versions have shown significant speedup when compared with their corresponding sequential versions.

Our initial evaluation of an OpenMP parallelization of SWA has shown linear speedup on multicore CPUs, but a closer look at performance data from both sequential and parallel versions have revealed two undesired effects: (i) As the length of the

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Medium 9781601323262

Virtual Machine Instance Scheduling in IaaS Clouds

Hamid R. Arabnia, Lou D'Alotto, Hiroshi Ishii, Minoru Ito, Kazuki Joe, Hiroaki Nishikawa, Georgios Sirakoulis, William Spataro, Giuseppe A. Trunfio, George A. Gravvanis, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti CSREA Press PDF

Int'l Conf. Par. and Dist. Proc. Tech. and Appl. | PDPTA'14 |

459

Virtual Machine Instance Scheduling in IaaS

Clouds

Naylor G. Bachiega, Henrique P. Martins, Roberta

Spolon, Marcos A. Cavenaghi

Departamento de Ciência da Computação

UNESP - Univ Estadual Paulista

Bauru, Brazil

Abstract— With steady increase in the use of computers, problems such as energy demand and space in data centers are occurring worldwide. Many solutions are being designed to solve these situations, among them is Cloud Computing, which uses existing technologies, such as virtualization, trying to solve problems like energy consumption and space allocation in data centers or large companies. The cloud is shared by multiple customers and allows an elastic growth, where new resources such as hardware or software, can be hired and added anytime in the platform. In this model, customers pay for the resources they use and not for all the architecture involved. Therefore, it is important to determine how efficiently those resources are distributed in the cloud. This paper aims to propose and develop a scheduling algorithm for the cloud that could efficiently define the distribution of resources within the architecture.

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Medium 9781601323262

Parallelizing Matrix Exponential based Solver on Shared Memory Systems using Cilk

Hamid R. Arabnia, Lou D'Alotto, Hiroshi Ishii, Minoru Ito, Kazuki Joe, Hiroaki Nishikawa, Georgios Sirakoulis, William Spataro, Giuseppe A. Trunfio, George A. Gravvanis, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti CSREA Press PDF

444

Int'l Conf. Par. and Dist. Proc. Tech. and Appl. | PDPTA'14 |

Parallelizing Matrix Exponential based Solver on Shared Memory

Systems using Cilk

Abdul Jabbar Saeed Tipu, Ammar Hasan, Mohsan Jameel, and Aamir Shafi

School of Electrical Engineering and Computer Science,

National University of Sciences and Technology, Islamabad, Pakistan

Abstract— Matrix Exponential based algorithm (MEXP) is a recently developed method for solving a positive definite system of linear equations. MEXP already outperforms other state of the art algorithms, such as the Preconditioned

Conjugate Gradient method (PCG), in most cases, on customizable hardware platforms such as FPGAs or ASICs. In this paper we have analyzed the performance of MEXP on multicore hardware using a shared-memory model called

Cilk and compare it with the Conjugate Gradient method

(CG) and PCG. Our multithreaded MEXP outperforms the

Cilk based PCG and CG methods in terms of parallelism and execution time as we increase numbers of cores. The comparison of the performance for the tested benchmark problems shows that parallel MEXP relatively gives almost

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