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7th IEEE international conference on Cloud Engineering (IEEE IC2E)

作者： Heintz, Benjamin Hong, Rankyung Singh, Shivangi Khandelwal, Gaurav Tesdahl, Corey Chandra, Abhishek Univ Minnesota Minneapolis MN 55455 USA

ISBN: (纸本)9781728102184

With the rapid growth of large online social networks, the ability to analyze large-scale social structure and behavior has become critically important, and this has led to the development of several scalable graph processing systems. In reality, however, social interaction takes place not only between pairs of individuals as in the graph model, but rather in the context of multi-user groups. Research has shown that such group dynamics can be better modeled through a more general hypergraph model, resulting in the need to build scalable hypergraph processing systems. In this paper, we present MESH, a flexible distributed framework for scalable hypergraph processing. MESH provides an easy-to-use and expressive application programming interface that naturally extends the "think like a vertex" model common to many popular graph processing systems. Our framework provides a flexible implementation based on an underlying graph processing system, and enables different design choices for the key implementation issues of partitioning a hypergraph representation. We implement MESH on top of the popular GraphX graph processing framework in Apache Spark. Using a variety of real datasets and experiments conducted on a local 8-node cluster as well as a 65-node Amazon AWS testbed, we demonstrate that MESH provides flexibility based on data and application characteristics, as well as scalability with cluster size. We further show that it is competitive in performance to HyperX, another hypergraph processing system based on Spark, while providing a much simpler implementation (requiring about 5X fewer lines of code), thus showing that simplicity and flexibility need not come at the cost of performance.

关键词： Computational modeling Manganese Cluster computing Scalability Partitioning algorithms Sparks Social networking (online)

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Using simple PID-inspired controllers for online resilient resource management of distributed scientific workflows

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FUTURE GENERATION COMPUTER SYSTEMS-THE international JOURNAL OF ESCIENCE 2019年 95卷 615-628页

作者： da Silva, Rafael Ferreira Filgueira, Rosa Deelman, Ewa Pairo-Castineira, Erola Overton, Ian M. Atkinson, Malcolm P. Univ Southern Calif Informat Sci Inst Marina Del Rey CA 90292 USA British Geol Survey Lyell Ctr Edinburgh EH14 4AP Midlothian Scotland Univ Edinburgh Sch Informat Edinburgh EH8 9LE Midlothian Scotland Univ Edinburgh MRC Inst Genet & Mol Med Edinburgh Midlothian Scotland Univ Edinburgh Usher Inst Populat Hlth Sci & Informat Edinburgh Midlothian Scotland Queens Univ Belfast Ctr Canc Res & Cell Biol Belfast Antrim North Ireland

Scientific workflows have become mainstream for conducting large-scale scientific research. As a result, many workflow applications and Workflow Management Systems (WMSs) have been developed as part of the cyberinfrastructure to allow scientists to execute their applications seamlessly on a range of distributed platforms. Although the scientific community has addressed this challenge from both theoretical and practical approaches, failure prediction, detection, and recovery still raise many research questions. In this paper, we propose an approach inspired by the control theory developed as part of autonomic computing to predict failures before they happen, and mitigated them when possible. The proposed approach is inspired on the proportional-integral-derivative controller (PID controller) control loop mechanism, which is widely used in industrial control systems, where the controller will react to adjust its output to mitigate faults. PID controllers aim to detect the possibility of a non-steady state far enough in advance so that an action can be performed to prevent it from happening. To demonstrate the feasibility of the approach, we tackle two common execution faults of large scale data-intensive workflows-data storage overload and memory overflow. We developed a simulator, which implements and evaluates simple standalone PID-inspired controllers to autonomously manage data and memory usage of a data-intensive bioinformatics workflow that consumes/produces over 4.4 TB of data, and requires over 24 TB of memory to run all tasks concurrently. Experimental results obtained via simulation indicate that workflow executions may significantly benefit from the controller-inspired approach, in particular under online and unknown conditions. Simulation results show that nearly-optimal executions (slowdown of 1.01) can be attained when using our proposed method, and faults are detected and mitigated far in advance of their occurrence. (C) 2019 Elsevier B.V. All rights re

关键词： Scientific workflows Fault detection and handling Resilient Big Data workflows Autonomic computing

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Newton-ADMM: A distributed GPU-Accelerated Optimizer for Multiclass Classification Problems 20

Newton-ADMM: A Distributed GPU-Accelerated Optimizer for Mul...

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Supercomputing conference

作者： Chih-Hao Fang Sudhir B. Kylasa Fred Roosta Michael W. Mahoney Ananth Grama Computer Science Purdue University West Lafayette US Electrical and Computer Engineering Purdue University West Lafayette US School of Mathematics and Physics University of Queensland Brisbane Australia ICSI Berkeley US

ISBN: (数字)9781728199986

ISBN: (纸本)9781728199993

First-order optimization techniques, such as stochastic gradient descent (SGD) and its variants, are widely used in machine learning applications due to their simplicity and low per-iteration costs. However, they often require larger numbers of iterations, with associated communication costs in distributed environments. In contrast, Newton-type methods, while having higher per-iteration computation costs, typically require a significantly smaller number of iterations, which directly translates to reduced communication costs. We present a novel distributed optimizer for classification problems, which integrates a GPU-accelerated Newton-type solver with the global consensus formulation of Alternating Direction of Method Multipliers (ADMM). By leveraging the communication efficiency of ADMM, a highly efficient GPUaccelerated inexact-Newton solver, and an effective spectral penalty parameter selection strategy, we show that our proposed method (i) yields better generalization performance on several classification problems; (ii) significantly outperforms state-of-the-art methods in distributed time to solution; and (iii) offers better scaling on large distributed platforms.

关键词： Scalability Neural networks Optimization methods Machine learning Convex functions Newton method Standards

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distributed processor load balancing based on multi-objective extremal optimization 1

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12th international conference on Internet and distributed computing Systems, IDCS 2019

作者： De Falco, Ivanoe Laskowski, Eryk Olejnik, Richard Scafuri, Umberto Tarantino, Ernesto Tudruj, Marek Institute of High Performance Computing and Networking CNR Naples Italy Institute of Computer Science Polish Academy of Sciences Warsaw Poland Université Lille—CRISTAL CNRS Lille France Polish-Japanese Academy of Information Technology Warsaw Poland

ISBN: (数字)9783030349141

ISBN: (纸本)9783030349134

The paper proposes and discusses distributed processor load balancing algorithms which are based on nature inspired approach of multi-objective Extremal Optimization. Extremal Optimization is used for defining task migration aiming at processor load balancing in execution of graph-represented distributed programs. The analysed multi-objective algorithms are based on three or four criteria selected from the following four choices: the balance of computational loads of processors in the system, the minimal total volume of application data transfers between processors, the number of task migrations during program execution and the influence of task migrations on computational load imbalance and the communication volume. The quality of the resulting load balancing is assessed by simulation of the execution of the distributed program macro data flow graphs, including all steps of the load balancing algorithm. It is done following the event-driven model in a simulator of a message passing multiprocessor system. The experimental comparison of the multi-objective load balancing to the single objective algorithms demonstrated the superiority of the multi-objective approach. © Springer Nature Switzerland AG 2019.

关键词： Multiobjective optimization

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MedRec: A Network for Personal Information Distribution

MedRec: A Network for Personal Information Distribution

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international conference on computing, networking and Communications (ICNC)

作者： Nchinda, Nchinda Cameron, Agnes Retzepi, Kallirroi Lippman, Andrew MIT Media Lab Cambridge MA 02139 USA

ISBN: (纸本)9781538692233

MedRec is a simple, distributed system for personal control of identity and distribution of personal information. The work is done in the context of a medical information distribution system where patients retain control over who can access their data. We present a new architecture for the MedRec project, creating a network of trusted data repositories, the access to which are determined by a set of "smart contracts". These contracts are stored on a distributed ledger maintained by those who generate data. The distributed nature of the system allows unified access from diverse sources in a single application with no intermediary. This increases patient control while retaining a measure of privacy of both data content and source. MedRec is amenable to extensions for decentralized messaging and distribution of information to third parties such as medical researchers, healthcare proxies, and other institutions. The system is based on a blockchain that contains smart contracts defining user identity and distribution specifics.

关键词： blockchain medical records healthcare distributed ethereum

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Convolutional Neural Network Training with distributed K-FAC 20

Convolutional Neural Network Training with Distributed K-FAC

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Supercomputing conference

作者： J. Gregory Pauloski Zhao Zhang Lei Huang Weijia Xu Ian T. Foster University of Texas at Austin Texas Advanced Computing Center University of Chicago & Argonne National Laboratory

ISBN: (数字)9781728199986

ISBN: (纸本)9781728199993

Training neural networks with many processors can reduce time-to-solution; however, it is challenging to maintain convergence and efficiency at large scales. The Kroneckerfactored Approximate Curvature (K-FAC) was recently proposed as an approximation of the Fisher Information Matrix that can be used in natural gradient optimizers. We investigate here a scalable K-FAC design and its applicability in convolutional neural network (CNN) training at scale. We study optimization techniques such as layer-wise distribution strategies, inverse-free second-order gradient evaluation, and dynamic K-FAC update decoupling to reduce training time while preserving convergence. We use residual neural networks (ResNet) applied to the CIFAR10 and ImageNet-1k datasets to evaluate the correctness and scalability of our K-FAC gradient preconditioner. With ResNet-50 on the ImageNet-1k dataset, our distributed K-FAC implementation converges to the 75.9% MLPerf baseline in 18-25% less time than does the classic stochastic gradient descent (SGD) optimizer across scales on a GPU cluster.

关键词： Training Scalability Neural networks Convolutional neural networks Optimization Convergence Residual neural networks

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Virtual Edge computing Using Vehicular Micro Clouds

Virtual Edge Computing Using Vehicular Micro Clouds

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international conference on computing, networking and Communications (ICNC)

作者： Dressler, Falko Pannu, Gurjashan Singh Hagenauer, Florian Gerla, Mario Higuchi, Takamasa Altintas, Onur Paderborn Univ Heinz Nixdorf Inst Paderborn Germany Paderborn Univ Dept Comp Sci Paderborn Germany Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA Toyota InfoTechnol Ctr USA Mountain View CA USA

ISBN: (纸本)9781538692233

We will discuss the challenges and opportunities of the connected cars vision in relation to the need for distributed data management solutions ranging from the vehicle to the mobile edge and to the data centers. As a novel concept, vehicular micro clouds have been proposed that bridge the gap between fully distributed vehicular networks based on short range vehicle to vehicle communication and cellular based infrastructure for centralized solutions. We will discuss the need for vehicular micro clouds, followed by the architecture, formation of micro clouds, and feasibility of micro clouds. Furthermore, we will cover aspects of efficient data upload and download between cars and a data center facilitated by our micro cloud concept.

关键词： Vehicular Cloud Mobile Edge computing Virtual Cloud Architecture Vehicular networking

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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...

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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 ...

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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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