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Research on D2D Data Transmission Protocol in NDN

Research on D2D Data Transmission Protocol in NDN

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international conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Bo Cui Xinmiao Jian Qi Chen Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing Hohhot China College of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

Named Data networking (NDN) is a new revolutionary network architecture and a research hotspot of next generation Internet. As a key candidate technology for 5G, D2D communication technology has attracted much attention. Data sharing is the main application of D2D communication, and data transmission is the core of data sharing. At present, there are few researches on D2D data transmission in NDN data sharing. The researches mainly focus on dataset synchronization and distributed file transmission. In addition, the existing data sharing protocol in NDN shows lower efficiency when directly applied to D2D data transmission. According to the main factors that affect the efficiency of data transmission in D-ChronoChat running in NDN, we design three optimized D2D data transmission protocols, and implement the prototype system. In order to evaluate the performance of each protocol and the main factors affecting the performance, we test the transmission delay of each protocol. The test results show that the Multi Threads Batch Packets (MTBP) protocol shows the best performance among the three optimized data transmission protocols. In MTBP, the data transmission rate can reach 6Mbps, nearly 7.4 times higher than the Single Thread Individual Packet protocol (STIP) in D-ChronoChat.

关键词： Protocols Prototypes Device-to-device communication Sensors Data communication Synchronization Next generation networking

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Rocket: Efficient and Scalable All-Pairs Computations on Heterogeneous Platforms 20

Rocket: Efficient and Scalable All-Pairs Computations on Het...

引用

Supercomputing conference

作者： Stijn Heldens Pieter Hijma Ben van Werkhoven Jason Maassen Henri Bal Rob van Nieuwpoort Netherlands eScience Center University of Amsterdam vrije Universiteit Amsterdam

ISBN: (数字)9781728199986

ISBN: (纸本)9781728199993

All-pairs compute problems apply a user-defined function to each combination of two items of a given data set. Although these problems present an abundance of parallelism, data reuse must be exploited to achieve good performance. Several researchers considered this problem, either resorting to partial replication with static work distribution or dynamic scheduling with full replication. In contrast, we present a solution that relies on hierarchical multi-level software-based caches to maximize data reuse at each level in the distributed memory hierarchy, combined with a divide-and-conquer approach to exploit data locality, hierarchical work-stealing to dynamically balance the workload, and asynchronous processing to maximize resource utilization. We evaluate our solution using three real-world applications, from digital forensics, localization microscopy, and bioinformatics, on different platforms, from desktop machine to a supercomputer. Results shows excellent efficiency and scalability when scaling to 96 GPUs, even obtaining super-linear speedups due to a distributed cache.

关键词： Rockets Scalability Microscopy distributed databases Parallel processing Supercomputers Resource management

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Huntalent: A candidates recommendation system for automatic recruitment via LinkedIn

Huntalent: A candidates recommendation system for automatic ...

引用

international conference on Social Networks Analysis, Management and Security (SNAMS)

作者： Shayma Boukari Sondes Fayech Rim Faiz LARODEC University of Tunis Tunis Tunisia

ISBN: (数字)9780738111803

ISBN: (纸本)9781665419734

In the era of Big Data, users do not express or can't clearly express their wishes and demands. Our work focuses on heterogeneous data extraction and analysis in order to make it easily accessible and exploitable by users or decision-makers. Recently recommendation systems are proved to be efficient in many sectors and newly for human resources to facilitate the recruitment process. For this reason, we present Huntalent, a candidate recommendation system that can represent an interesting solution to optimize the recruitment. This project makes use of Apache Spark, a distributed big data processing framework. Spark gives the advantage of handling iterative and interactive algorithms with efficiency and minimal processing time as compared to traditional map-reduce paradigm. We use content based recommendation techniques to recommend and identify potential candidates from the professional social network LinkedIn. The output of this work can be used by employers to find the right candidate for the right position.

关键词： Vocabulary Social networking (online) Cluster computing Big Data Tools Engines Recruitment

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A Hybrid Approach for Routing Path Computation in Software Defined Ultra Dense Network

A Hybrid Approach for Routing Path Computation in Software D...

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Sustainable Technologies for Industry 4.0 (STI), international conference on

作者： Mozdaher Abdul Quader Montaser Abdul Quader Palash Roy Md. Abdur Razzaque Green University of Bangladesh Dhaka Bangladesh Green Networking Research Group University of Dhaka Bangladesh

ISBN: (数字)9781665404891

ISBN: (纸本)9781665404907

In an OpenFlow based Software Defined Network (SDN), computation and installation of flow rules can be done proactively or reactively. Even though the proactive approach of SDN provides better performance in terms of transmission delay compared to its reactive counterpart, cannot be implemented in an extensive, unpredictable, and delay-sensitive network like Software Defined Ultra-dense Network (SD-UDN) of distributed Mobile Edge computing (MEC) servers due to the memory limitations of SDN forwarding devices. Furthermore, the reactive approach fails to provide service to delay-sensitive applications. In this paper we have developed a hybrid approach that retains the property of installing flow rules only along the computed path and reduces the initial path computation delay during packet transmission. The proposed approach also reduces reactive computation burdens by resolving significant computation loads proactively and minimizes the total number of node expansions during path computation with A* algorithm. Emulation results in mininet demonstrate an improvement in the proposed approach's performance in terms of initial path computation delay to about 90% compared to the state-of-the-art reactive approaches.

关键词： Emulation Software algorithms Software Delays Servers Software defined networking Ultra-dense networks

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distributed-Memory Parallel Symmetric Nonnegative Matrix Factorization 20

Distributed-Memory Parallel Symmetric Nonnegative Matrix Fac...

引用

Supercomputing conference

作者： Srinivas Eswar Koby Hayashi Grey Ballard Ramakrishnan Kannan Richard Vuduc Haesun Park Georgia Institute of Technology Atlanta USA Wake Forest University Winston-Salem USA Computational Data Analytics Group Oak Ridge National Laboratory Oak Ridge USA

ISBN: (数字)9781728199986

ISBN: (纸本)9781728199993

We develop the first distributed-memory parallel implementation of Symmetric Nonnegative Matrix Factorization (SymNMF), a key data analytics kernel for clustering and dimensionality reduction. Our implementation includes two different algorithms for SymNMF, which give comparable results in terms of time and accuracy. The first algorithm is a parallelization of an existing sequential approach that uses solvers for non symmetric NMF. The second algorithm is a novel approach based on the Gauss-Newton method. It exploits second-order information without incurring large computational and memory costs. We evaluate the scalability of our algorithms on the Summit system at Oak Ridge National Laboratory, scaling up to 128 nodes (4,096 cores) with 70% efficiency. Additionally, we demonstrate our software on an image segmentation task.

关键词： Symmetric matrices Scalability Software algorithms Clustering algorithms Software Task analysis Kernel

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Determining Feature Gaps Of Open Source Cloud Platforms for Mobile Backend as service (MBaaS) in Enterprise Mobile Applications

Determining Feature Gaps Of Open Source Cloud Platforms for ...

引用

IEEE distributed computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Aslam B Nandyal Mohammed Rafi University BDT College of Engineering Davanagere Karnataka INDIA University BDT College of Engineering Davanagere INDIA

ISBN: (数字)9781728198859

ISBN: (纸本)9781728198866

The development of mobile applications for various platforms requires enormous amount of time and effort. Demand for high performance, high quality, cloud enabled, social media and security aware features in a limited time to market environment requires systems which solve the task of integrating the front ends with various back ends. The model of “Mobile Backend as a service” (MBaaS) offers a cloud server infrastructure with Application Programming Interface/Software Development Kits for development on various mobile, web and desktop platforms. This saves considerable amount of app development expense, time, as it eases up the backend development work, thereby enabling the developer to focus on functionality, its promotion and on the improvement of the user experience. Technical requirements and various features of both open source and commercial MBaaS products are compiled. The paper evaluates popular, open source products available for enterprise mobile, web and desktop applications, and documents the fundamental and common set of capabilities provided by MBaaS. The developers, technical architects and managers will benefit from the qualitative analysis of development, deployment and engagement services of each MBaaS platform. The constraints discussed can be a feedback to platform vendors and hence the observations can help the complete ecosystem of mobile application development.

关键词： Social networking (online) Mobile applications Cloud computing Synchronization Business Security Authentication

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An Improvement of a Checkpoint-based distributed Testing Technique on a Big Data Environment 21

An Improvement of a Checkpoint-based Distributed Testing Tec...

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21st international conference on Advanced Communication Technology (ICACT)

作者： Sudsee, Bhuridech Kaewkasi, Chanwit Suranaree Univ Technol Sch Comp Engn Nakhon Ratchasrima 30000 Thailand

ISBN: (纸本)9791188428021

The advancement of storage technologies and the fast-growing number of generated data have made the world moved into the Big Data era. In this past, we had many data mining tools but they are inadequate to process Data-Intensive Scalable computing workloads. The Apache Spark framework is a popular tool designed for Big Data processing. It leverages in-memory processing techniques that make Spark up to 100 times faster than Hadoop. Testing this kind of Big Data program is time consuming. Unfortunately, developers lack a proper testing framework, which cloud help assure quality of their data-intensive processing programs while saving development time and storage usages. We propose distributed Test Checkpointing (DTC) for Apache Spark. DTC applies unit testing to the Big Data software development life cycle and reduce time spent for each testing loop with checkpoint. By using checkpoint technique, DTC keeps quality of Big Data processing software while keeps an inexpensive testing cost by overriding original Spark mechanism so that developers no pain to learn how to use DTC. Moreover, DTC has no addition abstraction layers. Developers can upgrade to a new version of Spark seamlessly. From the experimental results, we found that in the subsequence rounds of unit testing, DTC dramatically speed the testing time up to 450-500% faster. In case of storage, DTC can cut unnecessary data off and make the storage 19.7 times saver than the original checkpoint of Spark. DTC can be used either in case of JVM termination or testing with random values.

关键词： distributed Checkpointing Apache Spark Big Data Testing Software Testing

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IoT based Energy Management System for Smart Grid

IoT based Energy Management System for Smart Grid

引用

Advances in computing, Communication & Materials (ICACCM), 2020 international conference on

作者： Munish Kumar Ahmad Faiz Minai Akhlaque A. Khan Satish Kumar Integral University Lucknow INDIA

ISBN: (数字)9781728197852

ISBN: (纸本)9781728197869

In the modern days the word “Smart Grid” becomes a common word which can also be used as a junction which monitor demand side energy as well as the incoming energy and make it balance when the demand is on the peak or in low position it contributes the saved electric energy with the help of Internet of Things (loT). The IoT make power grid (smart grid) more reliable and economical. By IoT there is no lack of information in demand and supply and also it helps to save the energy. It also make pre-pattern of electricity consumption as the load demand and send signal to generation side for the required energy for the specific time period. The smart grid with IoT integrates the high speed response and storage system also with energy consumption. This type of networking by using IoT makes the operation of Smart Grid more reliable and burden free from the high demand periods (because the stored energy is used for the required demand). It also makes a better environment to collect the power from distributed generation and fasten the use of operation in the smart grid environment using loT. In this paper the use of IoT is discussed to work with Smart Grid for the better, economical, loss free power and also it helps in energy saving by reduce wastage. IoT is also used for the transfer of real time information to the Smart Grid and when the increase or decrease of demand takes places it helps in the saving of energy. User and supplier communication has been established by IoT for the proposed system.

关键词： Smart grids Batteries Feeds Wind turbines Renewable energy sources distributed power generation

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GEMS: GPU-Enabled Memory-Aware Model-Parallelism System for distributed DNN Training 20

GEMS: GPU-Enabled Memory-Aware Model-Parallelism System for ...

引用

Supercomputing conference

作者： Arpan Jain Ammar Ahmad Awan Asmaa M. Aljuhani Jahanzeb Maqbool Hashmi Quentin G. Anthony Hari Subramoni Dhableswar K. Panda Raghu Machiraju Anil Parwani The Ohio State University

ISBN: (数字)9781728199986

ISBN: (纸本)9781728199993

Data-parallelism has become an established paradigm to train DNNs that fit inside GPU memory on large-scale HPC systems. However, model-parallelism is required to train out-of-core DNNs. In this paper, we deal with emerging requirements brought forward by very large DNNs being trained using high-resolution images common in digital pathology. To address these, we propose, design, and implement GEMS; a GPU-Enabled Memory-Aware Model-Parallelism System. We present several design schemes like GEMS-MAST, GEMS-MASTER, and GEMS-Hybrid that offer excellent speedups over state-of-the-art systems like Mesh-TensorFlow and FlexFlow. Furthermore, we combine model-parallelism and data-parallelism to train a 1000-1ayer ResNet-lk model using 1,024 Volta V100 GPUs with 97.32% scaling-efficiency. For the real-world histopathology whole-slide-image (WSI) of 100,000 x 100,000 pixels, we train custom ResNet-110-v2 on image tiles of size 1024 x 1024 and reduce the training time from seven hours to 28 minutes.

关键词： Training Histopathology Computational modeling High performance computing Graphics processing units distributed databases Data models

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Investigation of a Hybrid Sensor- and Computational Network for Numerical Weather Prediction Calculations 22nd

Investigation of a Hybrid Sensor- and Computational Network ...

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22nd international conference on distributed Computer and Communication Networks (DCCN)

作者： Vas, Adam Toth, Laszlo Univ Debrecen Fac Informat Kassai St 26 H-4028 Debrecen Hungary

ISBN: (纸本)9783030366254;9783030366247

In this paper we investigate a hybrid approach to integrate our distributed Sensor Network for Prediction Calculations (DSN-PC) system's measurements into a hybrid computational network which assimilates them with publicly available atmospheric analysis dataset. The forecast calculations are based on a simple and relatively easily implementable algorithm. The parallelization is provided on the mathematical layer which is the highest possible level of parallelizing an algorithm for the solution of a system of differential equations. Our future goal is to move to solely DSN-PC measurement-based computations to make weather forecasts, but we do not have sufficient number of stations installed yet. However, a hybrid solution where we included NOAA Global Forecast System (GFS) data in the production of the initial values made it possible to test our sensor network's capability of performing such calculations. To evaluate the viability of our approach we analyzed the error of the forecasts. Below the results of the distributed calculations are shown along with the method of the assimilation of the two datasets.

关键词： Sensor network distributed computing Numerical calculations Weather prediction Data assimilation

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