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A case of electrical circuit switched interconnection network for parallel computers 18

A case of electrical circuit switched interconnection networ...

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18th international conference on parallel and distributed computing, applications and technologies, PDCAT 2017

作者： Hu, Yao Kudoh, Tomohiro Koibuchi, Michihiro National Institute of Informatics 2-1-2 Hitotsubashi Chiyoda-ku Tokyo101-8430 Japan University of Tokyo 2-11-16 Yayoi Bunkyo-ku Tokyo113-8658 Japan

ISBN: (纸本)9781538631515

Circuit switching is a way to minimize network latency and maximize network bandwidth when a limited number of source-and-destination pairs exchange messages which are predictable. Although there are a large number of studies of optical circuit switching (OCS) on HPC systems and datacenters, it is still not mature. In this context, we explore the use of electrical circuit switching (ECS) for the low-latency purpose on HPC systems and datacenters. ECS has the same link bandwidth as existing electrical packet switched networks, and inherits quick update of input-and-output connections from electrical switches. We develop a network topology generator for ECS to minimize the number of time slots optimized to target applications whose traffic patterns are predictable. By performing a quantitative discrete-event simulation, we present that an ideal ECS network outperforms counterpart EPS networks. Evaluation results show that the minimum necessary number of slots (MNNS) can be reduced to a small number in a generated topology while keeping resource amount less than that in a standard mesh network. © 2017 IEEE.

关键词： Timing circuits

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A Prototype Simulator for the Simulation of Complicated Hadoop Framework Behaviors 13

A Prototype Simulator for the Simulation of Complicated Hado...

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13th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Liu, Yang Jing, Weizhe Sichuan Univ Sch Elect Engn & Informat Chengdu 610065 Sichuan Peoples R China

ISBN: (纸本)9781538621653

distributed computing and parallel computing have become the most effective tools for solving complex problems in amount of academia and industrial fields. Among a number of distributed and parallel computing technologies, MapReduce has been proved as one of the most efficient and reliable computing model to deal with distributed tasks. Especially, Hadoop framework, a Java based open source implementation of MapReduce has been widely used and studied. Hadoop supplies tremendously abilities to process large-scale tasks with large volume of data, which significantly facilitates the big data applications. However, Hadoop is lack of a function that to predict the job processing time before actually running a job. And also. Hadoop cannot tell that how the system performs under the condition that the system parameters have been tuned. therefore, this paper presents a prototype Hadoop simulator which aims at simulating the parameters and interactions of the Hadoop framework using agent based simulation. the experimental results show that the simulator offers potentials to be an instrument to study complicated behaviors of the Hadoop framework.

关键词： MapReduce Hadoop simulator parameters agent simulation

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HDSVM: A High Efficiency distributed SVM Framework over Data Stream 15

HDSVM: A High Efficiency Distributed SVM Framework over Data...

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15th IEEE international Symposium on parallel and distributed Processing with applications (ISPA) / 16th IEEE international conference on Ubiquitous computing and Communications (IUCC)

作者： Hou, Yan Wang, Yijie Ma, Xingkong Cheng, Li Natl Univ Def Technol Coll Comp Natl Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781538637906

the application of Support Vector Machine (SVM) over data stream is growing with the increasing real-time processing requirements in classification field, like anomaly detection and real-time image processing. However, the dynamic live data with high volume and fast arrival rate in data streams make it challenging to apply SVM in data stream processing. Existing SVM implementations are mostly designed for batch processing and hardly satisfy the efficiency requirement of stream processing for its inherent complexity. To address the challenges, we propose a high efficiency distributed SVM framework over data stream (HDSVM), which consists of two main algorithms, incremental learning algorithm and distributed algorithm. Firstly, we propose a partial support vectors reserving incremental learning algorithm (PSVIL). By selecting a subset of support vectors based on their distances to classification hyperplane instead of the universal set to update SVM, the algorithm achieves lower time overhead while ensuring accuracy. Secondly, we propose a distribution remaining partition and fast aggregation distributed algorithm (DRPFA) for SVM. the real-time data is partitioned based on the original distribution with clustering instead of random partition, and historical support vectors are partitioned based on their distances to the classification hyperplane. the global hyperplane can be obtained by averaging the parameters of local hyperplanes due to the above partition strategy. Extensive experiments on Apache Storm show that the proposed HDSVM achieve lower time overhead and similar accuracy compared with the state-of-art. Speed-up ratio is increased by 2-8 times within 1% accuracy deviation.

关键词： distributed SVM Incremental learning Data stream Support vector selection Distance distribution

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Insecurity of Cheng et al.'s efficient revocation in ciphertext-policy attribute-based encryption based cryptographic cloud storage 15

Insecurity of Cheng et al.'s efficient revocation in ciphert...

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15th IEEE international Symposium on parallel and distributed Processing with applications (ISPA) / 16th IEEE international conference on Ubiquitous computing and Communications (IUCC)

作者： Wang, Changji Wu, Jiayuan Yuan, Yuan Liu, Jing Guangdong Univ Foreign Studies Collaborat Innovat Ctr 21st Century Maritime Silk Sch Informat Sci & Technol Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Sch Data Sci & Comp Guangzhou 510006 Guangdong Peoples R China Guangdong Univ Foreign Studies Sch Finance Guangzhou 510006 Guangdong Peoples R China Yunnan Univ Natl Pilot Sch Software Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9781538637906

Ciphertext-policy attribute-based encryption (CP-ABE) is a promising solution to the problem of fine-grained access control over encrypted data in the cloud. Several CP-ABE based cryptographic cloud storage systems have been proposed in recent years. However, access policy revocation is expensive in these systems, because data owner has to retrieve, re-encrypt and re-upload the data when access policy updates. To optimize the access policy revocation procedure, Cheng et al. proposed a revocation scheme for CP-ABE based cryptographic cloud storage. In their scheme, the original data is first divided into a number of slices, and then uploaded to the cloud storage. When a revocation occurs, the data owner needs only to retrieve, re-encrypt and re-upload one slice instead of the entire data. they claimed that their scheme is efficient and computationally secure. In this paper, we first point out Cheng et al.'s scheme only preserves all-or-nothing property for one time, then we show that it is susceptible to a fatal attack from the malicious revoked data user who stores the symmetric key or generates extra valid slices.

关键词： cloud storage ciphertext-policy attribute-based encryption access policy revocation information dispersal algorithm all-or-nothing transform

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SMiPE: Estimating the progress of recurring iterative distributed dataflows 18

SMiPE: Estimating the progress of recurring iterative distri...

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18th international conference on parallel and distributed computing, applications and technologies, PDCAT 2017

作者： Koch, Jannis thamsen, Lauritz Schmidt, Florian Kao, Odej Technische Universität Berlin Germany

ISBN: (纸本)9781538631515

distributed dataflow systems such as Apache Spark allow the execution of iterative programs at large scale on clusters. In production use, programs are often recurring and have strict latency requirements. Yet, choosing appropriate resource allocations is difficult as runtimes are dependent on hard-to-predict factors, including failures, cluster utilization and dataset characteristics. Offline runtime prediction helps to estimate resource requirements, but cannot take into account inherent variance due to, for example, changing cluster states. We present SMiPE, a system estimating the progress of iterative dataflows by matching a running job to previous executions based on similarity, capturing properties such as convergence, hardware utilization and runtime. SMiPE is not limited to a specific framework due to its black-box approach and is able to adapt to changing cluster states reflected in the current job's statistics. SMiPE automatically adapts its similarity matching to algorithm-specific profiles by training parameters on the job history. We evaluated SMiPE with three iterative Spark jobs and nine datasets. the results show that SMiPE is effective in choosing useful historic runs and predicts runtimes with a mean relative error of 9.1% to 13.1%. © 2017 IEEE.

关键词： Forecasting

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Efficient Analysis at Edge 8

Efficient Analysis at Edge

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8th ACM/SPEC international conference on Performance Engineering (ICPE)

作者： Mangels, Tatiana Murarasu, Alin Oden, Forest Fishkin, Alexey Becker, Daniel Siemens AG Corp Technol Otto Hahn Ring 6 Munich Germany

ISBN: (纸本)9781450348997

Digitalization changes traditional business models by using digital technologies to improve existing offerings and to create new offerings. Current technological trends such as artificial intelligence, autonomous systems, and predictive maintenance are ideal candidate technologies to enable digitalization use cases. Often, these technologies rely on the availability of large amounts of data and the capability to process these data efficiently. In contrast to consumer markets, industrial products must fulfill higher non-functional requirements such as fast response times, 24/7 availability and stability, real-time processing, safety, or security requirements. As a consequence, processing capabilities ranging from multicore and manycores to even high end parallel clusters have to be exploited to achieve necessary performance and stability needs. In this paper, we introduce a distributed Multicore Monitoring Framework (MoMo) which is a reference monitoring solution developed at Siemens Corporate Technology. It can be used to easily build efficient and stable diagnostic solutions which can help to understand the correctness, availability, reliability, and performance of large-scale distributed systems based on live data. Due to its small footprint MoMo can be used to analyze data directly at the data source which, for instance, can significantly reduce the network load. While MoMo's efficiency comes from the usage of multicore processors (CPUs) for running analysis in parallel, its usability is guaranteed by its capability to easily integrate with other monitoring frameworks and its usage of SPL - a domain-specific language which allows user to easily define diagnostic algorithms.

关键词： Monitoring data analysis parallel computing

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Algorithm for readers arrangement without collision in RFID networks 18

Algorithm for readers arrangement without collision in RFID ...

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18th international conference on parallel and distributed computing, applications and technologies, PDCAT 2017

作者： Meddeb, Aref Jaballah, Atef Institute of Computer Science and Communication Technologies University of Sousse Hammam Sousse Tunisia NOCCS Laboratory: Networked Objects Control and Communication Systems Tunisia National Engineering School of Sousse University of Sousse Tunisia

ISBN: (纸本)9781538631515

Radio Frequency IDentification (RFID) was identified as one of the ten best technologies in the 21st century. this technology is frequently used in different sectors: industrial, agricultural and academic. In RFID networks, readers and tags communicate wirelessly through electromagnetic signals. Due to the optimized tag coverage, multiple readers must be deployed in the same working area, causing reader-to-reader or/and readerto- tag collisions. In addition, the RFID reader is characterized by a maximum number of tags that can read them and a maximum interrogation range. then the problem of activating the RFID readers and adjusting their interrogation ranges in order to cover the maximum number of tags without collisions is one of hot spot researches in RFID networks. this problem is known as the Reader Coverage Collision Avoidance Arrangement (RCCAA) problem. In the literature, an algorithm called the Maximum-Weight-Independent-Set-Based Algorithm (MWISBA) was put forward to solve the RCCAA problem. In this algorithm, only the interrogation ranges of readers where adjusted. the interference range was not taken into account. thus, a readerto- reader collision could occur if a reader interrogated a tag located in the overlap area of its interrogation area with the interference area of another reader. To fill in this gap, we propose an improvement of the MWISBA called the MWISBAII which is able to solve the RCCAA problem avoiding all types of collisions. the experimental results show the superiority of our algorithm compared with the state-of-the-art solutions. © 2017 IEEE.

关键词： Radio waves

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A new lower bound of privacy budget for distributed differential privacy 18

A new lower bound of privacy budget for distributed differen...

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18th international conference on parallel and distributed computing, applications and technologies, PDCAT 2017

作者： Shen, Hong Lu, Zhigang School of Computer Science University of Adelaide Adelaide Australia School of Data and Computer Science Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781538631515

distributed data aggregation via summation (counting) helped us to learn the insights behind the raw data. However, such computing suffered from a high privacy risk of malicious collusion attacks. that is, the colluding adversaries infer a victim's privacy from the gaps between the aggregation outputs and their source data. Among the solutions against such collusion attacks, distributed Differential Privacy (DDP) shows a significant effect of privacy preservation. Specifically, a DDP scheme guarantees the global differential privacy (the presence or absence of any data curator barely impacts the aggregation outputs) by ensuring local differential privacy at the end of each data curator. To guarantee an overall privacy performance of a distributed data aggregation system against malicious collusion attacks, part of the existing work on such DDP scheme aim to provide an estimated lower bound of privacy budget for the global differential privacy. However, there are two main problems: low data utility from using a large global function sensitivity;unknown privacy guarantee when the aggregation sensitivity of the whole system is less than the sum of the data curator's aggregation sensitivity. To address these problems while ensuring distributed differential privacy, we provide a new lower bound of privacy budget, which works with an unconditional aggregation sensitivity of the whole distributed system. Moreover, we study the performance of our privacy bound in different scenarios of data updates. Both theoretical and experimental evaluations show that our privacy bound offers better global privacy performance than the existing work. © 2017 IEEE.

关键词： distributed computer systems

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the parallel and distributed Future of Data Series Mining 15

The Parallel and Distributed Future of Data Series Mining

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15th international conference on High Performance computing & Simulation (HPCS)

作者： Palpanas, themis Paris Descartes Univ Paris France

ISBN: (纸本)9781538632505

there is an increasingly pressing need, by several applications in diverse domains, for developing techniques able to index and mine very large collections of sequences, or data series. Examples of such applications come from biology, astronomy, entomology, the web, and other domains. It is not unusual for these applications to involve numbers of data series in the order of hundreds of millions to billions, which are often times not analyzed in their full detail due to their sheer size. In this work, we describe past efforts in designing techniques for indexing and mining truly massive collections of data series, based on indexing techniques for fast similarity search, an operation that lies at the core of many mining algorithms. We show that there are two bottlenecks in mining such massive datasets, namely, the time taken to build the index, and the time required to answer exactly similarity queries. In response to these challenges, we discuss novel techniques that adaptively create data series indexes, allowing users to correctly answer queries before the indexing task is finished. We also show how our methods allow mining on datasets that would otherwise be completely untenable, including the first published experiments using one billion data series. Moreover, we present our vision for the future in big sequence management and mining research: we argue that more efforts should concentrate on parallel (including modern hardware optimization opportunities) and distributed solutions, which have until now been largely unexploited.

关键词： Indexing Data mining distributed databases Algorithm design and analysis Buildings Astronomy

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Advances in Digital technologies: Proceedings of the 8th international conference on applications of Digital Information and Web technologies ICADIWT 2017

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2017年

作者： Jolanta Mizera-Pietraszko R Rodriguez Jorge D M Almazo Prez Pit Pichappan

ISBN: (纸本)9781614997726

Web technology is ubiquitous in modern life, enabling various forms of communication in real time between the users and computers, as well as between network devices, by means of artificial (markup) languages and cascading style sheets (CSS). Multimedia packages implemented in the WWW can also further expand the user groups to include, for example, the amblyopic or the hearing-impaired. According to Microsoft, Web technology also encompasses Web servers and programming languages for building Web applications. But such a breathtaking development that meets dynamically changing new emerging networking standards demands a large-scale infrastructure that will enable us to access digital information in its every form, whatever its purpose. this book presents 20 papers and 3 keynote speeches from the 8th international conference on applications of Digital Information and Web technologies (ICADIWT 2017), held at the Universidad Autnoma de Ciudad Jurez, Jurez City, Chihuahua, Mexico, in March 2017. Over the years, the ICADIWT conference has created its own research community of participants from many countries, who attend the event each year to demonstrate and discuss their research findings. the community is growing every year. the scope of the ICADIWT 2017 conference covers a wide range of research areas, and the papers in the book are divided into 7 subject areas: pattern recognition; distributed computing; mobile technologies; digital technologies for aerospace; medical systems applications; system engineering; and control systems.

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