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An Elasticity Description Language for Task-parallel Cloud applications 10

An Elasticity Description Language for Task-parallel Cloud A...

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10th international conference on Cloud computing and Services Science (CLOSER)

作者： Haussmann, Jens Blochinger, Wolfgang Kuechlin, Wolfgang Reutlingen Univ Parallel & Distributed Comp Grp Reutlingen Germany Univ Tubingen Symbol Computat Grp Tubingen Germany

ISBN: (纸本)9789897584244

In recent years, the cloud has become an attractive execution environment for parallel applications, which introduces novel opportunities for versatile optimizations. Particularly promising in this context is the elasticity characteristic of cloud environments. While elasticity is well established for client-server applications, it is a fundamentally new concept for parallel applications. However, existing elasticity mechanisms for client-server applications can be applied to parallel applications only to a limited extent. Efficient exploitation of elasticity for parallel applications requires novel mechanisms that take into account the particular runtime characteristics and resource requirements of this application type. To tackle this issue, we propose an elasticity description language. this language facilitates users to define elasticity policies, which specify the elasticity behavior at both cloud infrastructure level and application level. Elasticity at the application level is supported by an adequate programming and execution model, as well as abstractions that comply with the dynamic availability of resources. We present the underlying concepts and mechanisms, as well as the architecture and a prototypical implementation. Furthermore, we illustrate the capabilities of our approach through real-world scenarios.

关键词： Cloud computing High Performance computing parallel Application Elasticity

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Efficient. Scalable and Robust Data Shuffle Service for distributed MapReduce computing on Cloud

Efficient. Scalable and Robust Data Shuffle Service for Dist...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Rong Gu Xu Huang Haipeng Dai Xiaoyu Geng Xiaofei Chen Yihua Huang Fu Xiao Guihai Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Nanjing University of Posts and Telecommunications Nanjing China

distributed Map Reduce computing frameworks, such as Hadoop, Spark, and Flink, are widely used in various domains which face big data challenges. Inside Map Reduce, Shuffle is a critical stage that bridges up the Map stage and Reduce stage through data partition and network transmission under the hood. With the rapidly growing scale of data and clusters, the all-to-all data transfer in Shuffle becomes inefficient and frequently fails, making Shuffle the bottleneck of many big data processing jobs. To improve the Shuffle efficiency, we propose a hybrid Shuffle model based on file pre-merging and partition reorganization mechanisms. To address the Shuf-fle stability issue, we design a novel data transmission model based on the replication mechanism. Finally, to support scalable Shuffle with high performance, we present a Shuffle parallelism degree tuning approach based on machine learning and searching techniques. We have implemented the proposed Shuffle models and strategies into the widely-used MapReduce-based big data platform Spark for evaluation. Experimental evaluation shows that, compared with the state-of-the-art systems, our proposed methods outperform them by 23% on average. As a practical application case, the proposed Shuffle service has been running in the real-world environment of ByteDance. Inc over one year with significant performance improvement.

关键词： Machine learning parallel processing Big Data Data models Stability analysis Sparks Task analysis

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Implementation of Blockchain Based distributed Architecture for Enhancing Security and Privacy in Peer-To-Peer Networks 1

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10th international Advanced computing conference, IACC 2020

作者： Patidar, Kriti Jain, Swapnil Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore M.P. India

ISBN: (数字)9789811604041

ISBN: (纸本)9789811604034

Blockchain technology has gained maximum popularity in recent years as compared to the other latest technologies. It works as immutable ledger in which the transactions are done in decentralized way. It has started spreading its wings now as it is not only used in financial applications but many other companies in the industry are trying to leverage the benefits offered by blockchain in their projects. As industries are committing towards blockchain based projects, there is a need of blockchain implementation and associated development. this paper introduces the basic requirements of blockchain development and proposes a generic blockchain that can be considered as a mould for implementation in different use cases. In this paper a distributed architecture is implemented using blockchain with improved security and privacy. the expected contribution of this paper is to provide an overview for researchers and developers to build applications utilizing the blockchain as a basis. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Machine Learning-Based, Networking and computing Infrastructure Resource Management 17th

Machine Learning-Based, Networking and Computing Infrastruct...

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17th IFIP WG 12.5 international conference on Artificial Intelligence applications and Innovations (AIAI) / 6th 5G-PINE Workshop / AI-BIO Workshop / DAAI Workshop / DARE Workshop / EEAI Workshop / 10th MHDW Workshop

作者： Chochliouros, Ioannis P. Kostopoulos, Alexandros Payaro, Miquel Verikoukis, Christos di Vimercati, Sabrina De Capitani Vinogradov, Evgenii Ranjbar, Vida Vardakas, John Rahman, Md Arifur Soumplis, Polyzois Varvarigos, Emmanuel Hellen Telecommun Org OTE SA 99 Kifissias Ave Athens 15124 Greece Ctr Tecnol Telecomunicac Catalunya CTTC CERCA Barcelona Spain Univ Milan Milan Italy Katholieke Univ Leuven Leuven Belgium Iquadrat Informat SL Barcelona Spain IS Wireless Warsaw Poland Inst Commun & Comp Syst ICCS Athens Greece

ISBN: (纸本)9783030791599;9783030791575;9783030791568

5G mobile networks will be soon available to handle all types of applications and to provide service to massive numbers of users. In this complex and dynamic network ecosystem, end-to-end performance analysis and optimization will be key features in order to effectively manage the diverse requirements imposed by multiple vertical industries over the same shared infrastructure. To enable such a vision, the MARSAL project [ 1] targets the development and evaluation of a complete framework for the management and orchestration of network resources in 5G and beyond by utilizing a converged optical-wireless network infrastructure in the access and fronthaul/midhaul segments. At the network design domain, MARSAL targets the development of novel cell-free-based solutions. Namely, scalable and cost-efficient wireless access points deployment will be achieved by exploiting the distributed cell-free concept combined with wireless and wired serial fronthaul approaches. We will target the inclusion of these innovative functionalities in the O-RAN project. In parallel, in the fronthaul/midhaul segments MARSAL aims to radically increase the flexibility of optical access architectures for Beyond-5G cell site connectivity via different levels of fixed-mobile convergence. In the network and service management domain, the design philosophy of MARSAL is to provide a comprehensive framework for the management of the entire set of communication and computational network resources by exploiting novel ML-based algorithms of both edge and midhaul data centers, by incorporating the Virtual Elastic Data Centers/Infrastructures paradigm. Finally, at the network security domain, MARSAL aims to introduce mechanisms that provide privacy and security to application workload and data, targeting to allow applications and users to maintain control over their data when relying on the deployed shared infrastructures, while AI and Blockchain technologies will be developed in order to guarantee a secured

关键词： Cell-free distributed cloud Network automation Machine learning Secure multi-tenancy

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Impact of CUDA and OpenCL on parallel and distributed computing

Impact of CUDA and OpenCL on Parallel and Distributed Comput...

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international Conf on Electrical and Electronic Engineering (ICEEE)

作者： Abu Asaduzzaman Alec Trent S. Osborne C. Aldershof Fadi N. Sibai Wichita State University Wichita Kansas USA Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia

ISBN: (纸本)9781665440714;9781665411615

Along with high performance computer systems, the Application Programming Interface (API) used is crucial to develop efficient solutions for modern parallel and distributed computing. Compute Unified Device Architecture (CUDA) and Open computing Language (OpenCL) are two popular APIs that allow General Purpose Graphics Processing Unit (GPGPU, GPU for short) to accelerate processing in applications where they are supported. this paper presents a comparative study of OpenCL and CUDA and their impact on parallel and distributed computing. Mandelbrot set (represents complex numbers) generation, Marching Squares algorithm (represents embarrassingly parallelism), and Bitonic Sorting algorithm (represents distributed computing) are implemented using OpenCL (version 2.x) and CUDA (version 9.x) and run on a Linux-based High Performance computing (HPC) system. the HPC system uses an Intel i7-9700k processor and an Nvidia GTX 1070 GPU card. Experimental results from 25 different tests using the Mandelbrot Set generation, the Marching Squares algorithm, and the Bitonic Sorting algorithm are analyzed. According to the experimental results, CUDA performs better than OpenCL (up to 7.34x speedup). However, in most cases, OpenCL performs at an acceptable rate (CUDA speedup is less than 2x).

关键词： Performance evaluation High performance computing Graphics processing units Computer architecture parallel processing Hardware Generators

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Application of distributed computation of texture features to the analysis of biomedical images

Application of distributed computation of texture features t...

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18th international Scientific and Technical conference on Optical technologies for Communications

作者： Ilyasova, Nataly Yu Demin, Nikita S. Shirokanev, Alexandr S. Samara Natl Res Univ Moskovskoye Shosse 34 Samara 443086 Russia RAS IPSI RAS Branch FSRC Crystallog & Photon Molodogvardeyskaya 151 Samara 443001 Russia

ISBN: (数字)9781510644250

ISBN: (纸本)9781510644250

Medical applications are among the tasks of optical technology. the processing of two-dimensional optical signals and images is an urgent task today. One of the most dangerous eye diseases is diabetic macular retinopathy. the first stage in the laser coagulation operation is the stage of fundus image segmentation. the calculation of texture features for solving this problem takes a lot of time. In this paper, we consider the use of a high-performance algorithm for calculating texture features based on distributed computing to speed up the processing and analysis of medical images. Various use cases of the high-performance algorithm on a single node were investigated and compared with sequential and parallel algorithms. the high-performance algorithm achieves a 40x speedup and more under some parameters. Using a high-performance algorithm, analysis and segmentation is performed in less than 1 minute for standard images. the use of a high-performance algorithm for the analysis and segmentation of fundus images avoids the need for a sequential skip-step algorithm, which, due to interpolation, reduces the execution time, but at the same time, accuracy is lost.

关键词： fundus images image processing laser coagulation image segmentation eye retina texture features high-performance algorithm

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Optimized Smart Agricultural Production Systems using Enhanced parallel Green Anaconda Convolutional Neural Network

Optimized Smart Agricultural Production Systems using Enhanc...

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international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： S. Gomathi Meena Himanshu Rai Goyal Lavanya E. Muthukumar Mamtaben Kamleshbhai Megha Swapnil Parikh PG Department of Computer Science and Computer Applications PERI College of Arts and Science Mannivakkam Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Viruthunagar Tamil Nadu India Department of MBA Nehru College of Management Thirumalayampalayam Coimbatore Tamil Nadu India Department of Computer Science Natubhai V. Patel College of Pure and Applied Sciences A Constituent College of CVM University Vallabh Vidyanagar Gujarat India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Waghodia Gujarat India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Smart agriculture, driven by IoT technologies, is becoming increasingly essential due to global population growth and rising food demands. However, challenges such as energy supply constraints and routing issues often result in data transmission failures, high energy consumption, reduced network lifetime, and delays. To address these issues, this study proposes a novel solution: an enhanced smart agricultural production system based on an Optimized Smart Agricultural Production Systems using Enhanced parallel Green Anaconda Convolutional Neural Network (EPar-GrAnCNN). this system enhances energy management by incorporating the Emperor Penguin Optimizer (EPO) for the formation and selection of cluster heads, aiming to increase power conservation and extend network lifespan. Additionally, the system leverages deep learning techniques, including the Enhanced parallel Convolutional Neural Networks (EPar-CNN) and Green Anaconda Optimization (GAO), to analyze agricultural data and extract valuable insights. the EPar-GrAnCNN model accurately predicts crop health, the severity of water deficits, and other factors affecting crop growth, guiding farmers in the optimal utilization of resources. Computer experiments conducted on the Python platform demonstrate that the proposed approach outperforms existing methods, achieving higher throughput (98.4%), a superior packet delivery ratio (99.3%), and lower energy consumption (0.30 mJ). these results highlight the efficiency of the approach and its potential for further advancement in smart agriculture.

关键词： Smart agriculture Deep learning Production systems Energy consumption Crops Water conservation Convolutional neural networks Internet of things Data mining Water resources

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Use of Semantic technologies for Validation of e-Learning Outcomes 8

Use of Semantic Technologies for Validation of e-Learning Ou...

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8th international Scientific conference "Information Technology and Implementation" Workshop, IT and I-WS 2021

作者： Rogushina, Julia Pryima, Serhii Strokan, Oksana Gladun, Anatoly Institute of Software Systems National Academy of Sciences of Ukraine 44 Glushkov Pr. Kyiv03680 Ukraine Dmytro Motornyi Tavria State Agrotechnological University 18 B.Khmelnytsky Ave Zaporizhzhia region Melitopol72312 Ukraine International Research and Training Centre of Information Technologies and Systems National Academy of Sciences and Ministry of Education of Ukraine 44 Glushkov Pr. Kyiv03680 Ukraine

the paper offers means, sources and methods for semantic validation of learning outcomes (including non-formal and informal distant learning) based on the ESCO ontological model and matching of its main concepts. Atomic competencies are used for representation of different components of ontological model that formalizes relations between main subjects and objects of learning process. We analyze specifics of the Web-oriented semantic technologies used for support of elearning. Ontological analysis provides representation of distributed background knowledge about users, their professions, competencies, lifelong learning outcomes, etc., and standards of the Semantic Web project provide the technological foundation for creation of intelligent advisory applications. Validation of learning outcomes of informal and non-formal learning needs in semantic matching of user profiles with classifications of professions and qualifications. Advantages of proposed approach are demonstrated on example of applied information system AdvisOnt developed to combine the market of educational services with the labor market that can use validation of the informal and non-formal learning outcomes. © 2022 Copyright for this paper by its authors.

关键词： Semantic Web

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distributed Orchestration of Regression Models Over Administrative Boundaries 21

Distributed Orchestration of Regression Models Over Administ...

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2021 IEEE/ACM 8th international conference on Big Data computing, applications and technologies (BDCAT '21)

作者： Menuka Warushavithana Caleb Carlson Saptashwa Mitra Daniel Rammer Mazdak Arabi Jay Breidt Sangmi Lee Pallickara Shrideep Pallickara Department of Computer Science Colorado State University USA Colorado State University USA Department of Statistics Colorado State University USA

ISBN: (纸本)9781450391641

Geospatial data collections are now available in a multiplicity of domains. the accompanying data volumes, variety, and diversity of encoding formats within these collections have all continued to grow. these data offer opportunities to extract patterns, understand phenomena, and inform decision making by fitting models to the data. To ensure accuracy and effectiveness, these models need to be constructed at geospatial extents/scopes that are aligned with the nature of decision-making — administrative boundaries such as census tracts, towns, counties, states etc. this entails construction of a large number of models and orchestrating their accompanying resource requirements (CPU, RAM and I/O) within shared computing clusters. In this study, we describe our methodology to facilitate model construction at scale by substantively alleviating resource requirements while preserving accuracy. Our benchmarks demonstrate the suitability of our methodology.

关键词： workload orchestration spatiotemporal data transfer learning modeling

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SequenceShield: A Robust and Accurate DDoS Detection Method via Serializing the Traffic with Direction Information

SequenceShield: A Robust and Accurate DDoS Detection Method ...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Zeyi Deng Wei Yang Minchao Xu Meijie Du Yuzhen Li Zhou Zhou Qingyun Liu Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China National Engineering Laboratory of Information Security Technologies Beijing China

distributed Denial of Services (DDoS) attacks continue to be one of the most challenging threats to the Internet. Previous methods try to reveal the pattern of attacks by using artificially selected statistical features or header fields of network packets. However, they suffer from low accuracy and are not robust in the face of evasion attacks (clever attackers can change some specific header fields of the packet or slow down the packet rate to avoid being detected). To achieve robust and accurate DDoS detection, we propose a method named SequenceShield. Sequence Shield is based on our observation that the direction information of each network packet can reveal the behavior pattern of DDoS attack traffic. Specifically, we innovatively serialize the network traffic between two endpoints into a sequence structure named interaction sequence (IS) with direction information. then the converted IS is sent to a gated recurrent unit (GRU) network to learn the behavior pattern of attacks. SequenceShield only uses each packet's direction and protocol information, which makes our proposed method robust enough in the face of evasion attacks. Moreover, experimental results on two well-known datasets indicate that the classification performance of SequenceShield outperforms the state-of-the-art DDoS detection methods.

关键词： Protocols Neural networks Telecommunication traffic Logic gates Denial-of-service attack Behavioral sciences Internet

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