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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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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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Price Evaluation Model in Second-hand Car System based on BP Neural Network theory 18

Price Evaluation Model in Second-hand Car System based on BP...

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18th IEEE/ACIS international conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed computing (SNPD)

作者： Sun, Ning Bai, Hongxi Geng, Yuxia Shi, Huizhu Hohai Univ Dept IoT Engn Changzhou Peoples R China

ISBN: (纸本)9781509055043

With the rapid growth of the number of private cars and the development of the second-hand car market, second-hand cars have become the main choice when people buy cars. the online second-hand car platform provides both buyers and sellers the chance of online P2P trade. In such systems, the accuracy of second-hand car price evaluation largely determines whether the seller and the buyer can get more efficient trading experience. In this paper, the price evaluation model based on big data analysis is proposed, which takes advantage of widely circulated vehicle data and a large number of vehicle transaction data to analyze the price data for each type of vehicles by using the optimized BP neural network algorithm. It aims to establish a second-hand car price evaluation model to get the price that best matches the car. In this paper, the optimized BP neural network algorithm is used to select the optimal number of hidden neurons in BP neural network, which improves the convergence speed of the network topology and the accuracy of the prediction model. through the sampling simulation experiments, the fitting curve of the prediction price is compared with the real transaction price derived from the optimized model. As a result, the fitting of the optimized model is better as well as the accuracy is higher.

关键词： second-hand car price evaluation BP neural network hidden neuron big data

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parallel and distributed computing, Applications and Technologies, PDCAT Proceedings

Parallel and Distributed Computing, Applications and Technol...

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18th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2017

ISBN: (纸本)9781538631515

the proceedings contain 75 papers. the topics discussed include: a learning automata based dynamic resource provisioning in cloud computing environments;dependability stress testing of cloud infrastructures;local tetra pattern texture features for environmental sound event classification;dynamically improving resiliency to timing errors for stream processing workloads;integer programming model to optimize waste management system (case study: Surakarta city Indonesia);implementing algorithmic skeletons with bulk synchronous parallel ML;cost-aware software-defined hybrid object-based storage system;feature-based adaptive block partition method for data prefetching in streamline visualization;towards supporting modeling variability in e-learning application: a case study;agent collaboration in intelligent parking lot systems: dynamic generation of commitment protocol;managing bytecode and ISA compatibility with an enhanced toolchain;optimization of LevelDB by separating key and value;handling churn in similarity based clustering overlays using weighted benefit;an intelligent network sharing system design with multipath TCP;efficient data gathering in wireless sensor networks with fixed-group method;a divide and conquer state grouping method for bitmap based transition compression;and 5G cellular : survey on some challenging techniques.

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An efficient parallel optimization for co-authorship network analysis 18

An efficient parallel optimization for co-authorship network...

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18th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2017

作者： Valencio, Carlos Roberto De Freitas, Jose Carlos Gratao De Souza, Rogeria Cristiane Neves, Leandro Alves Donega Zafalon, Geraldo Francisco Colombini, Angelo Cesar Tenorio, William Campus São José Do Rio Preto SP Brazil São Carlos SP Brazil

ISBN: (纸本)9781538631515

Co-authorship analysis in science and technology partnerships provides a vision of cooperation patterns between individuals and organizations and is still widely used to understand and assess scientific collaboration patterns. this analysis is conducted by means of bibliometry, which is the quantitative study of scientific production. However, with the evolution of database management systems, there was a significant increase in the volume of stored data, which could difficult the analysis. In this context, the developed work presents an efficient parallel optimization of bibliometric information, in order to allow this scientific analysis in a Big Data environment. Our results show that the time taken to calculate the transitivity value using the sequential approach grows 4.08 times faster than the parallel proposed approach when the number of nodes tends to infinity;the time taken to calculate the average distance and diameter values using the sequential approach grows 5.27 times faster than the parallel proposed approach when the number of nodes tends to infinity. Also, the results found present good values of speed up and efficiency. © 2017 IEEE.

关键词： Big data

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Middleware 2017 - Proceedings of the 2017 international Middleware conference

Middleware 2017 - Proceedings of the 2017 International Midd...

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18th ACM/IFIP/USENIX Middleware conference, Middleware 2017

ISBN: (纸本)9781450347204

the proceedings contain 20 papers. the topics discussed include: HyperDrive: exploring hyperparameters with pop scheduling;sieve: actionable insights from monitored metrics in distributed systems;Rafiki: a middleware for parameter tuning of NoSQL datastores for dynamic metagenomics workloads;Rivulet: a fault-tolerant platform for smart-home applications;binary compatible graphics support in Android for running iOS apps;Sense-Aid: a framework for enabling network as a service for participatory sensing;ORCA: an ORChestration automata for configuring VNFs;improving spark application throughput via memory aware task co-location: a mixture of experts approach;Swayam: distributed autoscaling to meet SLAs of machine learning inference services with resource efficiency;data-driven serverless functions for object storage;a programming model for application-defined multipath TCP scheduling;POLM2: automatic profiling for object lifetime-aware memory management for hotspot big data applications;SPECTRE: supporting consumption policies in window-based parallel complex event processing;efficient covering for top-k filtering in content-based publish/subscribe systems;StreamApprox: approximate computing for stream analytics;X-Search: revisiting private web search using Intel SGX;rectify: black-box intrusion recovery in PaaS Clouds;scheduler activations for interference-resilient SMP virtual machine scheduling;DoubleDecker: a cooperative disk caching framework for derivative clouds;and Ginja: one-dollar cloud-based disaster recovery for databases.

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the influence of Twitter on education policy making

The influence of Twitter on education policy making

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ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed computing (SNPD)

作者： Mwana Said Omar Alexander Njeru Sun Yi Department of Information Systems Kobe Institute of Computing Kobe Japan

ISBN: (纸本)9781509055050

the explosive growth and influence of social media and microblogging sites has paved a way for internet users to freely express their views, opinions and share their experiences with others. With over 319 million users, Twitter presents a unique and dynamic source of information on people's interests and opinions and a rich source of data for sentiment analysis. this paper will discuss the influence of twitter on education policy making. the study aims at helping education policy makers to develop and implement policies that responds to the publics needs.

关键词： Sentiment analysis Education Twitter Feature extraction Data mining Data visualization

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A centralized HIDS framework for private cloud

A centralized HIDS framework for private cloud

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ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed computing (SNPD)

作者： Zhijian Wang Yanqin Zhu School of Computer Science Technology Soochow University Suzhou China

ISBN: (纸本)9781509055050

Cloud computing is more convenient and efficient than traditional on-premise computing for users, as it provides large scale resources, software, and information to each customer. However, cloud computing systems can be easily threatened by various cyber attacks. therefore, an Intrusion Detection System (IDS) is very necessary for cloud computing system. there is a serious problem which traditional host-based IDS for cloud computing consumes a large amount of system resources. In this paper, we propose a centralized host-based IDS framework to reduce the use of the resources. Using logstash tool to collect the system logs from each virtual machine, and storing them into elasticsearch cluster centrally. After that, we analyze all these logs in the detection center and send the results to each virtual machine. We have validated our framework in the openstack platform. the results show a good performance in reducing the CPU and memory usage.

关键词： Cloud computing Virtual machining Intrusion detection Computational modeling Data collection Real-time systems

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Handling Churn in Similarity Based Clustering Overlays Using Weighted Benefit

Handling Churn in Similarity Based Clustering Overlays Using...

引用

IEEE international conference on parallel and distributed computing, Applications and Technologies (PDCAT)

作者： Irum Fahim Bukhari Aaron Harwood Shanika Karunasekera University of Melbourne

Similarity based clustering (SBC) overlays are decentralized networks of nodes on the Internet edge, where each node maintains some number of direct connections to other nodes that are most "similar" to it. the challenge is: how do the nodes in the overlay converge to and maintain the most similar neighbors, given that the network is decentralized, is subject to churn and that similarity varies over time. Protocols that simultaneously provide fast convergence and low bandwidth consumption are the objective of this research. We present a protocol, that we call Weighted Benefit Scheme (WBS), that improves upon existing state-of-the-art in this area: it has equivalent convergence rate to the Optimum Benefit Protocol while simultaneously handling churn competitively to the Vicinity protocol. We use real world datasets from Yahoo WebScope that comprises of 15,400 users with 354,000 ratings about 1000 songs and our experiments are performed on the simulation test-bed PeerNet.

关键词： Peer-to-peer computing Protocols Convergence Bandwidth Motion pictures Message systems Topology

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Optimistic parallel simulation engine for predicting future situations incorporating observation data

Optimistic parallel simulation engine for predicting future ...

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ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed computing (SNPD)

作者： Masashi Shiraishi Atsuo Ozaki Shusuke Watanabe Information Technology R&D Center Mitsubishi Electric Corporation Kanagawa Japan Kamakura Works Mitsubishi Electric Corporation Kanagawa Japan

ISBN: (纸本)9781509055050

Coping with emergency situations requires effective and prompt decision-making under constantly changing situations. the deployment of various kinds of sensors makes it possible to acquire vast amounts of information. At present, however, most information processing is not automated; therefore, the quality of decision-making depends heavily on the capacities of the decision maker. To solve this problem, we propose a system for predicting future situations through parallel simulations based on observation data. To facilitate the development of such systems, we have been developing an optimistic simulation engine (hereafter referred to as O-SE) that supports parallel simulation of many possible situations and dynamic simulation modification based on observation data acquired by sensors. the design and evaluation results of the O-SE are illustrated in this paper.

关键词： Data models Predictive models Silicon Engines Decision making Sensors Simulation

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