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Multiscale modeling and distributed computing to predict cosmesis outcome after a lumpectomy

JOURNAL OF COMPUTATIONAL PHYSICS 2013年 244卷 321-335页

作者： Garbey, M. Salmon, R. Thanoon, D. Bass, B. L. Univ Houston Dept Comp Sci Houston TX 77004 USA Methodist Hosp Dept Surg Houston TX 77030 USA Methodist Inst Res Innovat & Educ Houston TX USA Univ La Rochelle Lab Sci Ingn Environm La Rochelle France

Surgery for early stage breast carcinoma is either total mastectomy (complete breast removal) or surgical lumpectomy (only tumor removal). The lumpectomy or partial mastectomy is intended to preserve a breast that satisfies the woman's cosmetic, emotional and physical needs. But in a fairly large number of cases the cosmetic outcome is not satisfactory. Today, predicting that surgery outcome is essentially based on heuristic. Modeling such a complex process must encompass multiple scales, in space from cells to tissue, as well as in time, from minutes for the tissue mechanics to months for healing. The goal of this paper is to present a first step in multiscale modeling of the long time scale prediction of breast shape after tumor resection. This task requires coupling very different mechanical and biological models with very different computing needs. We provide a simple illustration of the application of heterogeneous distributed computing and modular software design to speed up the model development. Our computational framework serves currently to test hypothesis on breast tissue healing in a pilot study with women who have been elected to undergo BCT and are being treated at the Methodist Hospital in Houston, TX. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Multiscale model Cellular automata Breast cancer distributed computing

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Sunburn-using excess energy of small-scale production for distributed computing

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ENERGY EFFICIENCY 2018年第1期11卷 97-119页

作者： Nurminen, J. K. Niemi, T. Strandman, J. Ruokosuo, K. Helsinki Inst Phys Helsinki Finland Aalto Univ Helsinki Finland CERN Helsinki Inst Phys Meyrin Switzerland

The Sunburn solar computer system is based on the idea of consuming the excess electricity of photovoltaic energy systems for useful computing. In this way, the proposed approach allows us to move a part of computing capacity from dedicated data centers to individual solar energy systems and thereby reduce the energy consumption of the data centers. In principle, the proposed mechanism converts the excess electrical energy into valuable computation. The results of computation or data processing are easier to store and transfer than produced electricity. In small-scale systems, the benefit from storing or selling the excess electricity is small, whereas the benefit from selling the generated data is potentially larger, as we demonstrate in this study. Finally, the technical feasibility of the solution is illustrated by constructing and evaluating a prototype implementation using excess solar energy for distributed BOINC computing.

关键词： distributed computing Solar energy Green computing Energy storage Scientific computing distributed data center

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Scalable and efficient data distribution for distributed computing of all-to-all comparison problems

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2017年 67卷 152-162页

作者： Zhang, Yi-Fan Tian, Yu-Chu Kelly, Wayne Fidge, Colin Queensland Univ Technol Sch Elect Engn & Comp Sci GPO Box 2434 Brisbane Qld 4001 Australia Taiyuan Univ Technol Coll Informat Engn Taiyuan 030024 Shanxi Peoples R China

All-to-all comparison problems represent a class of big data processing problems widely found in many application domains. To achieve high performance for distributed computing of such problems, storage usage, data locality and load balancing should be considered during the data distribution phase in the distributed environment. Existing data distribution strategies, such as the Hadoop one, are designed for problems with MapReduce pattern and do not consider comparison tasks at all. As a result, a huge amount of data must be re-arranged at runtime when the comparison tasks are executed, degrading the overall computing performance significantly. Addressing this problem, a scalable and efficient data distribution strategy is presented in this paper with comparison tasks in mind for distributed computing of all-to-all comparison problems. Specifically designed for problems with all-to-all comparison pattern, it not only saves storage space and data distribution time but also achieves load balancing and good data locality for all comparison tasks of the all-to-all comparison problems. Experiments are conducted to demonstrate the presented approaches. It is shown that about 90% of the ideal performance capacity of the multiple machines can be achieved through using the approach presented in this paper. (C) 2016 Elsevier B.V. All rights reserved.

关键词： distributed computing Big data All-to-all comparison Data distribution

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A Comprehensive Survey on Coded distributed computing: Fundamentals, Challenges, and Networking Applications

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IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2021年第3期23卷 1800-1837页

作者： Ng, Jer Shyuan Lim, Wei Yang Bryan Luong, Nguyen Cong Xiong, Zehui Asheralieva, Alia Niyato, Dusit Leung, Cyril Miao, Chunyan Nanyang Technol Univ Alibaba Grp & Alibaba NTU Joint Res Inst Singapore Singapore PHENIKAA Univ Fac Comp Sci Hanoi 12116 Vietnam PHENIKAA Univ PHENIKAA Res & Technol Inst Hanoi 12116 Vietnam Singapore Univ Technol Design Pillar Informat Syst Technol & Design Singapore Singapore Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Univ British Columbia Joint NTU UBC Res Ctr Excellence Act Living Elder Vancouver BC V6T 1Z4 Canada

distributed computing has become a common approach for large-scale computation tasks due to benefits such as high reliability, scalability, computation speed, and cost-effectiveness. However, distributed computing faces critical issues related to communication load and straggler effects. In particular, computing nodes need to exchange intermediate results with each other in order to calculate the final result, and this significantly increases communication overheads. Furthermore, a distributed computing network may include straggling nodes that run intermittently slower. This results in a longer overall time needed to execute the computation tasks, thereby limiting the performance of distributed computing. To address these issues, coded distributed computing (CDC), i.e., a combination of coding theoretic techniques and distributed computing, has been recently proposed as a promising solution. Coding theoretic techniques have proved effective in WiFi and cellular systems to deal with channel noise. Therefore, CDC may significantly reduce communication load, alleviate the effects of stragglers, provide fault-tolerance, privacy and security. In this survey, we first introduce the fundamentals of CDC, followed by basic CDC schemes. Then, we review and analyze a number of CDC approaches proposed to reduce the communication costs, mitigate the straggler effects, and guarantee privacy and security. Furthermore, we present and discuss applications of CDC in modern computer networks. Finally, we highlight important challenges and promising research directions related to CDC.

关键词： distributed computing Task analysis Encoding Security Internet of Things Resource management Privacy distributed computing communication minimization straggler effects mitigation security coded distributed computing

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Elastic Resource Allocation for Coded distributed computing Over Heterogeneous Wireless Edge Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第4期22卷 2636-2649页

作者： Nguyen, Cong T. Nguyen, Diep N. Hoang, Dinh Thai Phan, Khoa Tran Niyato, Dusit Pham, Hoang-Anh Dutkiewicz, Eryk Univ Technol Sydney Sch Elect & Data Engn Sydney NSW 2007 Australia Vietnam Natl Univ Ho Chi Minh City VNU HCM Ho Chi Minh City Univ Technol HCMUT Fac Comp Sci & Engn Ho Chi Minh City 700000 Vietnam La Trobe Univ Sch Engn & Math Sci Melbourne VIC 3086 Australia Nanyang Technol Univ Sch Comp Sci & Engn Singapore City 639798 Singapore

Coded distributed computing (CDC) has recently emerged to be a promising solution to address the straggling effects in conventional distributed computing systems. By assigning redundant workloads to the computing nodes, CDC can significantly enhance the performance of the whole system. However, since the core idea of CDC is to introduce redundancies to compensate for uncertainties, it may lead to a large amount of wasted energy at the edge nodes. It can be observed that the more redundant workload added, the less impact the straggling effects have on the system. However, at the same time, the more energy is needed to perform redundant tasks. In this work, we develop a novel framework, namely CERA, to elastically allocate computing resources for CDC processes. Particularly, CERA consists of two stages. In the first stage, we model a joint coding and node selection optimization problem to minimize the expected processing time for a CDC task. Since the problem is NP-hard, we propose a linearization approach and a hybrid algorithm to quickly obtain the optimal solutions. In the second stage, we develop a smart online approach based on Lyapunov optimization to dynamically turn off straggling nodes based on their actual performance. As a result, wasteful energy consumption can be significantly reduced with minimal impact on the total processing time. Simulations using real-world datasets have shown that our proposed approach can reduce the system's total processing time by more than 200% compared to that of the state-of-the-art approach, even when the nodes' actual performance is not known in advance. Moreover, the results have shown that CERA's online optimization stage can reduce the energy consumption by up to 37.14% without affecting the total processing time.

关键词： Task analysis Servers Optimization distributed computing Wireless communication Energy consumption Codes Coded distributed computing maximum distance separable code resource allocation INLP Lyapunov optimization edge computing straggling effects

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Parallel multi-view concept clustering in distributed computing

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NEURAL computing & APPLICATIONS 2020年第10期32卷 5621-5631页

作者： Wang, Hao Yang, Yan Zhang, Xiaobo Peng, Bo Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 65576 Peoples R China

Multi-view clustering (MvC) is an emerging task in data mining. It aims at partitioning the data sampled from multiple views. Although a great deal of research has been done, this task remains to be very challenging. We found an important problem in performing the MvC task. MvC needs large amounts of computation. To address this problem, we propose a parallel MvC method in a distributed computing environment. The proposed method builds upon concept factorization with local manifold learning, denoted by parallel multi-view concept clustering (PMCC). Concept factorization learns a compressed representation for the data. Local manifold learning preserves the locally intrinsic geometrical structure in the data. The weight of each view is learned automatically and a cooperative normalized approach is proposed to better guide the learning of a consensus representation for all views. For the proposed PMCC architecture, the calculation of each part is independent. It is clear that our PMCC can be performed in a distributed computing environment. Experimental results using real-world datasets demonstrate the effectiveness of the proposed method.

关键词： Multi-view clustering Concept factorization Manifold learning distributed computing

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A FRAMEWORK ARCHITECTURE FOR MULTIMEDIA INFORMATION NETWORKS - BELLCORE IS BUILDING ITS INA ARCHITECTURE ON CURRENT ADVANCES IN distributed computing AND BROAD-BAND COMMUNICATIONS

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IEEE COMMUNICATIONS MAGAZINE 1992年第2期30卷 97-104页

作者： NATARAJAN, N SLAWSKY, GM Development of an architecture for multimedia information networks Bell Communications Research Inc. USA Area of network architecture planning Bellcore USA

An overview of the INA architecture, which builds on the current advances in broadband communication and distributed computing technologies and specifies an architecture for future information networks that are required to transport multimedia information and to manage multimedia communication, is presented. The key functional separations that have to be met in any INA-consistent network, the major components of an INA-consistent network, and the various levels in the architecture are described. The INA architecture is compared to other networking and distributed-processing architecture.< >

关键词： Computer architecture Application software Software development management Technology management Multimedia computing Hardware Software standards Standards development Buildings distributed computing

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Experimental evaluation of a multi-installment scheduling strategy based on divisible load paradigm for SAR image reconstruction on a distributed computing infrastructure

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JOURNAL OF PARALLEL AND distributed computing 2024年 193卷

作者： Chinnappan, Gokul Madathupalyam Veeravall, Bharadwaj Mouthaan, Koen Lee, John Wen-Hao Natl Univ Singapore Dept Elect & Comp Engn 4 Engn Dr 3 Singapore 119077 Singapore DSO Natl Labs Sensors Div 12 Sci Pk Dr Singapore 118225 Singapore

Radar loads, especially Synthetic Aperture Radar (SAR) image reconstruction loads use a large volume of data collected from satellites to create a high-resolution image of the earth. To design near-real-time applications that utilise SAR data, speeding up the image reconstruction algorithm is imperative. This can be achieved by deploying a set of distributed computing infrastructures connected through a network. Scheduling such complex and large divisible loads on a distributed platform can be designed using the Divisible Load Theory (DLT) framework. We performed distributed SAR image reconstruction experiments using the SLURM library on a cloud virtual machine network using two scheduling strategies, namely the Multi-Installment Scheduling with Result Retrieval (MIS-RR) strategy and the traditional EQual-partitioning Strategy (EQS). The DLT model proposed in the MIS-RR strategy is incorporated to make the load divisible. Based on the experimental results and performance analysis carried out using different pixel lengths, pulse set sizes, and the number of virtual machines, we observe that the time performance of MIS-RR is much superior to that of EQS. Hence the MIS- RR strategy is of practical significance in reducing the overall processing time, and cost, and in improving the utilisation of the compute infrastructure. Furthermore, we note that the DLT-based theoretical analysis of MIS-RR coincides well with the experimental data, demonstrating the relevance of DLT in the real world.

关键词： Synthetic Aperture Radar distributed computing Heterogeneous cluster Divisible Load Theory SLURM Multi-Installment Scheduling MIS-RR strategy EQual-partitioning Strategy Makespan

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Wireless distributed computing in cognitive radio networks

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AD HOC NETWORKS 2012年第5期10卷 845-857页

作者： Datla, Dinesh Volos, Hans I. Hasan, S. M. Reed, Jeffrey H. Bose, Tamal Wireless Virginia Tech Blacksburg VA 24061 USA

Individual cognitive radio nodes in an ad-hoc cognitive radio network (CRN) have to perform complex data processing operations for several purposes, such as situational awareness and cognitive engine (CE) decision making. In an implementation point of view, each cognitive radio (CR) may not have the computational and power resources to perform these tasks by itself. In this paper, wireless distributed computing (WDC) is presented as a technology that enables multiple resource-constrained nodes to collaborate in computing complex tasks in a distributed manner. This approach has several benefits over the traditional approach of local computing, such as reduced energy and power consumption, reduced burden on the resources of individual nodes, and improved robustness. However, the benefits are negated by the communication overhead involved in WDC. This paper demonstrates the application of WDC to CRNs with the help of an example CE processing task. In addition, the paper analyzes the impact of the wireless environment on WDC scalability in homogeneous and heterogeneous environments. The paper also proposes a workload allocation scheme that utilizes a combination of stochastic optimization and decision-tree search approaches. The results show limitations in the scalability of WDC networks, mainly due to the communication overhead involved in sharing raw data pertaining to delegated computational tasks. (C) 2011 Elsevier B.V. All rights reserved.

关键词： distributed computing Cognitive radio networks Cognitive engine Power and energy consumption Workload allocation

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Robust distributed computing and sensing algorithm

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COMPUTER 1996年第6期29卷 53-+页

作者： Brooks, RR Iyengar, SS Louisiana State University USA

Sensors that supply data to computer systems are inherently unreliable. When sensors are distributed, reliability is further compromised. How can a system tell good sensor data from faulty? In this article, we describe a hybrid algorithm we developed that satisfies both the precision and accuracy requirements of distributed systems, We used established methods for distributed agreement based on data of limited accuracy. Our hybrid algorithm is suitable for use in both environments and manages to provide increased precision for distributed decision-malting without adversely affecting system accuracy. The hybrid algorithm effectively solves the problem of making the correct decision in the presence of faulty data, enhancing both accuracy and precision.

关键词： Robustness distributed computing Sensor systems Automatic control Control systems Broadcasting Radar tracking Infrared sensors Sensor fusion distributed decision making

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