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A Blockchain-Based Multi-Cloud Storage Data Consistency Verification Scheme

A Blockchain-Based Multi-Cloud Storage Data Consistency Veri...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Feiyu Wang Jian-tao Zhou Hao Wang Xu Guo College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

Users worldwide widely use cloud storage because of its efficiency, convenience, and high availability. Multi-cloud storage is usually selected to ensure the high availability of data. Unfortunately, when data is migrated and replicated between multi-cloud data centers, it is not easy to guarantee data con-sistency. This paper proposes an efficient, secure, and new data consistency verification scheme using blockchain technology. In order to reduce the computation and communication overhead in the verification process, our scheme uses encrypted tags to build Merkle hash tree to generate unique and lightweight verification proofs and does not use third-party auditors. The final theoretical and experimental analysis shows that our scheme has higher security and a faster verification process in multi-cloud storage.

关键词： Cloud computing Data centers Costs Blockchains Behavioral sciences

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On the Provenance Extraction techniques from Large Scale Log Files: A Case Study for the Numerical Weather Prediction Models

On the Provenance Extraction Techniques from Large Scale Log...

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26th international conference on parallel and distributed Computing (Euro-Par)

作者： Tufek, Alper Aktas, Mehmet S. Yildiz Tech Univ Istanbul Turkey

ISBN: (纸本)9783030715939;9783030715922

Day by day, severe meteorological events increasingly highlight the importance of fast and accurate weather forecasting. There are various Numerical Weather Prediction (NWP) models worldwide that are run on either a local or a global scale to predict future weather. NWP models typically take hours to finish a complete run, however, depending on the input parameters and the size of the forecast domain. Provenance information is of central importance for detecting unexpected events that may develop during model execution, and also for taking necessary action as early as possible. Besides, the need to share scientific data and results between researchers or scientists also highlights the importance of data quality and reliability. In this study, we develop a framework for tracking The Weather Research and Forecasting (WRF) model and for generating, storing, and analyzing provenance data. We develop a machine-learning-based log parser to enable the proposed system to be dynamic and adaptive so that it can adapt to different data and rules. The proposed system enables easy management and understanding of numerical weather forecast workflows by providing provenance graphs. By analyzing these graphs, potential faulty situations that may occur during the execution of WRF can be traced to their root causes. Our proposed system has been evaluated and has been shown to perform well even in a high-frequency provenance information flow.

关键词： Machine learning-based provenance extraction Numerical weather prediction models Provenance Provenance analysis Weather forecast models

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Investigation into Massively parallel MIMD Architecture based IPU System through Application Benchmarking

Investigation into Massively Parallel MIMD Architecture base...

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international conference on Information Technology, Computer and Electrical Engineering (ICITACEE)

作者： Shashank Sharma Samrit Kumar Maity Krishan Gopal Gupta Abhishek Das Mohammad Sajeed Sanjay Wandhekar HPC Technologies C-DAC Pune India

We present a performance benchmarking analysis of Intelligent processing Unit (IPU). IPU is a new architecture developed by Graphcore, specifically designed for handling Neural Network (NN) and Graph Computing (GC) workloads. IPU-POD16 is a 5U server solution, from Graphcore, consisting of four IPU servers and one host server. Each IPU server (IPU M2000) consists of 4 MK2 GC200 IPU. Our performance evaluation is focused on two aspects. First -To measure throughput, considering IPU-POD16 as a single machine, Second-To assess the scalability, by varying the number of IPUs assigned to current workload. For compute workload we used the Tensorflow implementation of ResNet-50 and performed training and validation with the Imagenet dataset. Experiments are conducted to measure the performance of the IPU system with respect to scaling, precision, network latency by devising three separate strategies. We reported throughput numbers (images/second) for training and validation, with both real data and synthetic data. Our initial conclusion is that applications are easy to port and run in the IPU platform, due to availability and support of popular frameworks like tensorflow and pytorch. In order to fully utilize the capabilities of the IPU hardware platform, advanced engineering techniques are required. In this case, we measured the performance by benchmarking with limited application varieties. More study and experimentation are required to fully understand and realize the power of the architecture (computation, networking). It would also be interesting to see how applications from other domains (other than AI/ML/DL) take benefit of this new platform.

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parallel algorithm for the unsupervised binning of metagenomic sequences 21

Parallel algorithm for the unsupervised binning of metagenom...

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5th international conference on Machine Learning and Soft Computing, ICMLSC 2021

作者： Hoang, Vu Vinh, Le Van Hoai, Tran Van Lang, Tran Van Bao, Huynh Quang Faculty of Information Technology HCMC University of Technology and Education Viet Nam Faculty of Computer Science and Engineering Ho Chi Minh City Viet Nam University of Technology VNU-HCM Viet Nam Institute of Applied Mechanics and Informatics VAST Viet Nam

ISBN: (纸本)9781450387613

The binning of metagenomic sequences is one of crucial steps in metagenomic projects which allow the study of uncultured organisms. Although the projects need to analyze a huge amount of data, most available binning methods run in single mode, and thus require much processing time. This paper proposes a parallel binning algorithm for metagenomic sequences without reference databases. The method is able to utilize the strength of computing clusters and shared-memory multiprocessing systems by using MPI and OpenMP techniques. Experimental results show that the proposed algorithm outperforms a single-mode binning algorithm in the aspect of computational performance while still achieving similar classification quality. The source codes and datasets used in this work can be downloaded from https://***/BiMetaPL. © 2021 ACM.

关键词： Multiprocessing systems

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parallel Shift-Invert Spectrum Slicing on distributed Architectures with GPU Accelerators 20

Parallel Shift-Invert Spectrum Slicing on Distributed Archit...

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49th international conference on parallel processing (ICPP)

作者： Williams-Young, David B. Yang, Chao Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA

ISBN: (纸本)9781450388160

The solution of large scale eigenvalue problems (EVP) is often the computational bottleneck for many scientific and engineering applications. Traditional eigensolvers, such as direct (e.g. ScaLAPACK) and Krylov subspace (e.g. Lanczos) methods, have struggled in achieving high scalability on large computing resources due to communication and synchronization bottlenecks which are inherent in their implementation. This includes a difficulty in developing well-performing ports of these algorithms to architectures which rely on the use of accelerators, such as graphics processing units (GPU), for the majority of their floating point operations. Recently, there has been significant research into the development of eigensolvers based on spectrum slicing, in particular shift-invert spectrum slicing, to alleviate the communication and synchronization bottlenecks of traditional eigensolvers. In general, spectrum slicing trades the global EVP for many smaller, independent EVPs which may be combined to assemble some desired subset of the entire eigenspectrum. The result is a method which utilizes more floating point operations than traditional eigensolvers, but in a way which allows for the expression of massive concurrency leading to an overall improvement in time-to-solution on large computing resources. In this work, we will examine the performance of parallel shift-invert spectrum slicing on modern GPU clusters using state-of-the-art linear algebra software.

关键词： Graphics processing unit

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Resource and task clustering based scheduling algorithm for workflow applications in cloud computing environment 6

Resource and task clustering based scheduling algorithm for ...

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6th international conference on parallel, distributed and Grid Computing, PDGC 2020

作者： Maurya, Ashish Kumar Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj India

ISBN: (纸本)9781728171326

Cloud computing consists of distributed resources and used to provide services to the applications which require huge computation power such as scientific, mathematical, weather forecasting, and biomedical applications. These applications are considered as workflow applications containing many numbers of dependent tasks. The scheduling of these dependent tasks on distributed resources is a critical problem in cloud computing. In this paper, we present a scheduling algorithm that considers clustering of resources and tasks for workflow applications in the cloud computing environment. The given algorithm is an enhancement toHySARC algorithm. Like HySARC, the proposed algorithm first forms clusters of resources and tasks and then applies list scheduling techniques on each of the clusters to schedule tasks. We have estimated and compared the performance of the proposed algorithm with HySARC algorithm on the parameters like clustering time, and makespan, and found that the proposed algorithm performed better than the compared algorithm. © 2020 IEEE.

关键词： Cloud computing

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IoT Based Designing of Secure Data Storage System in distributed Cloud System with Big Data using Cryptography Algorithm

IoT Based Designing of Secure Data Storage System in Distrib...

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Ravi Shankar Shukla Department of Computer Science College of Computing and Informatics Saudi Electronic University KSA

ISBN: (纸本)9781665487351

The cloud system plays a versatile role in the storage of vast data for future references. This includes the delivery of the computing sources that includes the servers with storage, various databases, networking system and software analysis. The web based services provides various usages in versatile forms. The cloud computing helps in numerous pathways for the web based service networks. The security of the data and the privacy constraints are the important aspects involved in the cloud computing techniques. This includes the safeguarding the sensitive data with higher precision. This is accompanied with the intelligent cryptographic algorithm to obtain secure data in the cloud system. This helps to save the data through several data processing applications accompanied with the cryptography algorithm. The cryptography algorithm are enabled to obtain encrypt data so that to have secure communication links in the networking system. This helps in the separation of data with the prescribed files in the distributed cloud networks. The operational time is secured by the classification of data in the form of data packets.

关键词： Cloud computing Data privacy Buffer storage distributed databases Memory Big Data Software

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parallel and distributed Training of Deep Neural Networks: A brief overview 24

Parallel and Distributed Training of Deep Neural Networks: A...

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24th IEEE international conference on Intelligent Engineering Systems (INES)

作者： Farkas, Attila Kertesz, Gabor Lovas, Robert Inst Comp Sci & Control SZTAKI Budapest Hungary Obuda Univ Doctoral Sch Appl Informat & Appl Math Budapest Hungary

ISBN: (纸本)9781728110592

Deep neural networks and deep learning are becoming important and popular techniques in modern services and applications. The training of these networks is computationally intensive, because of the extreme number of trainable parameters and the large amount of training samples. In this brief overview, current solutions aiming to speed up this training process via parallel and distributed computation are introduced. The necessary components and strategies are described from the low-level communication protocols to the high-level frameworks for the distributed deep learning. The current implementations of the deep learning frameworks with distributed computational capabilities are compared and key parameters are identified to help design effective solutions.

关键词： Deep neural networks

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COMPOFF: A Compiler Cost model using Machine Learning to predict the Cost of OpenMP Offloading

COMPOFF: A Compiler Cost model using Machine Learning to pre...

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IEEE international Symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Alok Mishra Smeet Chheda Carlos Soto Abid M. Malik Meifeng Lin Barbara Chapman Stony Brook University Stony Brook NY USA Brookhaven National Laboratory Upton NY USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

The HPC industry is inexorably moving towards an era of extremely heterogeneous architectures, with more devices configured on any given HPC platform and potentially more kinds of devices, some of them highly specialized. Writing a separate code suitable for each target system for a given HPC application is not practical. The better solution is to use directive-based parallel programming models such as OpenMP. OpenMP provides a number of options for offloading a piece of code to devices like GPUs. To select the best option from such options during compilation, most modern compilers use analytical models to estimate the cost of executing the original code and the different offloading code variants. Building such an analytical model for compilers is a difficult task that necessi-tates a lot of effort on the part of a compiler engineer. Recently, machine learning techniques have been successfully applied to build cost models for a variety of compiler optimization problems. In this paper, we present COMPOFF, a cost model that statically estimates the Cost of OpenMP OFFloading using a neural network model. We used six different transformations on a parallel code of Wilson Dslash Operator to support GPU offloading, and we predicted their cost of execution on different GPUs using COMPOFF during compile time. Our results show that this model can predict offloading costs with a root mean squared error in prediction of less than 0.5 seconds. Our preliminary findings indicate that this work will make it much easier and faster for scientists and compiler developers to port legacy HPC applications that use OpenMP to new heterogeneous computing environment.

关键词： Analytical models Costs Codes parallel programming Computational modeling Machine learning Predictive models

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RIFFLE: A distributed Network Operating System with Re-configurability for Multiple Domains

RIFFLE: A Distributed Network Operating System with Re-confi...

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IEEE international Symposium on parallel and distributed processing with applications and IEEE international conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Boyang Zhou Chunming Wu Shuangxi Chen Zhen Li Hong Fan Zhejiang Lab Intelligent Networks Institute Hangzhou China School of Intelligent Science and Technology Hangzhou Institute for Advanced Study UCAS Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Jiaxing Key Laboratory of Industrial Internet Jiaxing China Research Center for Intelligent Technology Standardization Zhejiang Lab Hangzhou China Department of Electrical Engineering Shanghai University of Electric Power Shanghai China

The distributed network control in software-defined networking is greatly challenged by the design demands of evolving a running distributed control plane (DCP), and by the complexity of handling the control state dynamics. In this paper, we propose RIFFLE to address the problem using a distributed network operating system approach. RIFFLE enables the evolvability of control states of DCP deployed on a global scale. RIFFLE handles spatial and temporal dynamics that occur when updating and migrating the distributed control states, which overcomes the critical barrier to the evolvability. It fills the gap by enabling temporal reconfigurability in DCP. Moreover, RIFFLE reduces the complexities of building and maintaining network control services in DCP by enabling componentization and abstracting the underlying network dynamics. We evaluated the prototype RIFFLE through experiments running in PlanetLab. The results show that the prototype scales well for 135 nodes. We also validate that RIFFLE ensures the continuity and low content request delay for the supported the information-centric network supported during cache update periods owing to the enabled evolvability.

关键词： Runtime Roads Decentralized control Information-centric networking Prototypes Ubiquitous computing Dynamic scheduling

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