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Multi-core parallel processing technique to prepare the time series data for the early detection of DDoS Flooding Attacks 8

Multi-core parallel processing technique to prepare the time...

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8th international conference on Computing for Sustainable Global Development, INDIACom 2021

作者： Kumar, S. Ratan Kumari, V. Valli Raju, K.V.S.V.N. Anil Neerukonda Institute of Technology and Sciences Dept. of Computer Sc. and Engineering Visakhapatnam India Andhra University Dept. of Computer Sc. and Systems Engineering Visakhapatnam India Gayatri Vidya Parishad College for Degree and PG Courses Dept. of Computer Sc. and Engineering Visakhapatnam India

ISBN: (纸本)9789380544434

distributed Denial of Service (DDoS) attacks pose a considerable threat to Cloud Computing, Internet of Things (IoT) and other services offered on the Internet. The victim server receives terabytes of data per second during the DDoS attack. It may take hours to examine them to detect a potential threat, leading to denial of service to legitimate users. processing vast volumes of traffic to mitigate the attack is a challenging task for network administrators. High-performance techniques are more suited for processing DDoS attack traffic compared to Sequential processing techniques. This paper proposes a Multi-Core parallel processing Technique to prepare the time series data for the early detection of DDoS flooding attacks. Different time series analysis methods are suggested to detect the attack early on. Producing time series data using parallel processing saves time and further speeds up the detection of the attack. The proposed method is applied to the benchmark data set CICDDoS2019 for generating four different time series to detect TCP-based flooding attacks, namely TCP-SYN, TCP-SYN-ACK, TCP-ACK, and TCP-RST. The implementation results show that the proposed method can give a speedup of 2.3 times for processing attack traffic compared to sequential processing © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： Denial-of-service attack

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11th international Symposium on Advanced parallel processing Technologies, APPT 2015

11th International Symposium on Advanced Parallel Processing...

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11th international Symposium on Advanced parallel processing Technologies, APPT 2015

ISBN: (纸本)9783319232157

The proceedings contain 8 papers. The special focus in this conference is on Advanced parallel processing Technologies. The topics include: Improving memory access performance of in-memory key-value store using data prefetching techniques;distributed data collection framework for failure prediction in tianhe supercomputers;optimizing the mapreduce framework for CPU-MIC heterogeneous cluster;stable matching scheduler for single-ISA heterogeneous multi-core processors;RPECA-rumor propagation based eventual consistency assessment algorithm;efficient implementation of MIPS code generator for the ionmonkey javascript compiler;effects of quenched disorder on liquid crystal and visual tracking based on convolutional deep belief network.

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Ranking on Heterogeneous Manifold for Multimodal Information Retrieval 21

Ranking on Heterogeneous Manifold for Multimodal Information...

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21st IEEE international Symposium on parallel and distributed processing with applications, 13th IEEE international conference on Big Data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Jin, Zhenkun Wan, Xingshi Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business University Wuhan Research Institute of Posts and Telecommunications Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science & Technology Wuhan China

ISBN: (纸本)9798350329223

In light of recent advancements in Internet of Multimedia Things (IoMT) and 5G technology, both the variety and quantity of data have been rapidly increasing. Consequently, handling zero-shot cross-modal retrieval (ZSCMR) of unlabeled multi-modal data has emerged as a prominent research topic. Early models utilized generative adversarial networks and class embeddings to assist in building a common feature space. However, this approach resulted in a loss of reliability in the feature distributions of individual modalities. In this paper, we propose a method called Ranking on Heterogeneous Manifold (RHM) to address the aforementioned issue. Our model comprises three feature extractors, each dedicated to extracting features from text, images and multimodal data. Subsequently, the relationship structures derived from these feature extractors are employed to construct a heterogeneous manifold. A diffusion process is then conducted on this heterogeneous manifold, ultimately yielding the final ranking scores. Comprehensive experiments demonstrate that the proposed modal has the capability to acquire more effective knowledge representation, thus enhancing the effectiveness of ZSCMR. © 2023 IEEE.

关键词： Knowledge representation

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Euro-Par 2014 parallel processing 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Fernando Silva Inês Dutra Vítor Santos Costa

ISBN: (数字)9783319098739

ISBN: (纸本)9783319098722

This book constitutes the refereed proceedings of the 20th international conference on parallel and distributed Computing, Euro-Par 2014, held in Porto, Portugal, in August 2014. The 68 revised full papers presented were carefully reviewed and selected from 267 submissions. The papers are organized in 15 topical sections: support tools environments; performance prediction and evaluation; scheduling and load balancing; high-performance architectures and compilers; parallel and distributed data management; grid, cluster and cloud computing; green high performance computing; distributed systems and algorithms; parallel and distributed programming; parallel numerical algorithms; multicore and manycore programming; theory and algorithms for parallel computation; high performance networks and communication; high performance and scientific applications; and GPU and accelerator computing.

关键词： Programming Languages, Compilers, Interpreters Operating Systems System Performance and Evaluation Computer Communication Networks Algorithm Analysis and Problem Complexity

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Algorithms and Architectures for parallel processing 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Meikang Qiu

ISBN: (数字)9783030602451

ISBN: (纸本)9783030602444

This three-volume set LNCS 12452, 12453, and 12454 constitutes the proceedings of the 20th international conference on Algorithms and Architectures for parallel processing, ICA3PP 2020, in New York City, NY, USA, in October 2020.;ICA3PP is covering the many dimensions of parallel algorithms and architectures, encompassing fundamental theoretical approaches, practical experimental projects, and commercial components and systems. As applications of computing systems have permeated in every aspects of daily life, the power of computing system has become increasingly critical. This conference provides a forum for academics and practitioners from countries around the world to exchange ideas for improving the efficiency, performance, reliability, security and interoperability of computing systems and applications. ICA3PP 2020 focus on two broadareas of parallel and distributed computing, i.e. architectures, algorithms and networks, and systems and applications.

关键词： Computational Mathematics and Numerical Analysis Theory of Computation Computer Systems Organization and Communication Networks Control Structures and Microprogramming Input/Output and Data Communications Computer Communication Networks

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A Novel parallel Computation Model with Efficient Local Memory Management for Data-intensive applications 8

A Novel Parallel Computation Model with Efficient Local Memo...

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IEEE 8th international conference on Cloud Computing

作者： Al-Absi, Ahmed Abdulhakim Kang, Dae-Ki Dongseo Univ Div Comp & Informat Engn Busan South Korea

ISBN: (纸本)9781467372879

The provisioning of high-performance computing infrastructure through cloud environments enables data intensive processing to be a viable solution. In this paper, we introduce a novel parallel computation model similar to MapReduce framework. The proposed parallelized model incorporates a parallel execution strategy in worker nodes to decrease execution response times in cloud environments. The parallelized model adopts efficient local memory management techniques in the worker nodes to reduce memory transfer overheads. For evaluation, we compared the proposed framework with the state of art Hadoop MapReduce framework. From experiments on benchmark datasets, it turns out that the parallelized model reduces the execution times by about 45.86%. Those experimental results indicate the efficiency and the scalability of proposed framework on cloud environments.

关键词： parallel Computation MapReduce Hadoop Worker Nodes Task Cloud Computing

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Coordinating the use of GPU and CPU for improving performance of compute intensive applications

Coordinating the use of GPU and CPU for improving performanc...

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2009 IEEE international conference on Cluster Computing and Workshops, CLUSTER '09

作者： Teodoro, George Sachetto, Rafael Sertel, Olcay Gurcan, Metin N. Meira Jr., Wagner Catalyurek, Umit Ferreira, Renato Department of Computer Science Universidade Federal de Minas Gerais Brazil Department of Biomedical Informatics Ohio State University United States

ISBN: (纸本)9781424450121

GPUs have recently evolved into very fast parallel co-processors capable of executing general purpose computations extremely efficiently. At the same time, multi-core CPUs evolution continued and today's CPUs have 4-8 cores. These two trends, however, have followed independent paths in the sense that we are aware of very few works that consider both devices cooperating to solve general computations. In this paper we investigate the coordinated use of CPU and GPU to improve efficiency of applications even further than using either device independently. We use Anthill runtime environment, a data-flow oriented framework in which applications are decomposed into a set of event-driven filters, where for each event, the runtime system can use either GPU or CPU for its processing. For evaluation, we use a histopathology application that uses image analysis techniques to classify tumor images for neuroblastoma prognosis. Our experimental environment includes dual and octa-core machines, augmented with GPUs and we evaluate our approach's performance for standalone and distributed executions. Our experiments show that a pure GPU optimization of the application achieved a factor of 15 to 49 times improvement over the single core CPU version, depending on the versions of the CPUs and GPUs. We also show that the execution can be further reduced by a factor of about 2 by using our runtime system that effectively choreographs the execution to run cooperatively both on GPU and on a single core of CPU. We improve on that by adding more cores, all of which were previously neglected or used ineffectively. In addition, the evaluation on a distributed environment has shown near linear scalability to multiple hosts. © 2009 IEEE.

关键词： Graphics processing unit

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parallelized multilevel arc flags improve speedup in shortest path queries

Parallelized multilevel arc flags improve speedup in shortes...

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international conference on Process Automation, Control and Computing

作者： Kalpana, R. Thambidurai, P. Department of Computer Science and Engineering Pondicherry Engineering College Puducherry India Perunthalaivar Kamarajar Institute of Engineering and Technology Karaikal Puducherry India

ISBN: (纸本)9781612847641

Tremendous growth in online applications require shortest path queries to solve its problem where the data volume is very high. Among the various shortest path algorithms considered, Dijkstra's shortest path algorithm[1] proves to have better performance compared to other algorithms. The speedup techniques when applied to these shortest path algorithms will improve the performance of the system. The combined speedup techniques have higher impact over the shortest path queries. In this paper a new combined speedup technique based on arc flags and multilevel method(COAM) is implemented and tested on random graphs, planar graphs, and real world graphs. The pre-processing phase of arc flag and multilevel method is parallelised using OpenMP parallel programming constructs and effect of that produces an improved speedup in the individual basic speedup techniques and combined speedup technique(COAM). © 2011 IEEE.

关键词： parallel programming

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Reliability based routing strategy for performance Optimization in distributed Mobile Computing through clustering 3

Reliability based routing strategy for performance Optimizat...

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international conference parallel distributed Grid Computing

作者： Khan, Faizul Navi Govil, Kapil Agarwal, Alok Teerthanker Mahaveer Univ Moradabad UP India JP Inst Engn & Tech Meerut Uttar Pradesh India

ISBN: (纸本)9781479976836

Technology enhancement makes the computing available everywhere and provides access to widely distributed resources which is known as Mobile Computing. Mobile computing enables innovative applications through the sharing of computing resources among mobile devices such as notebook, smart phones and Personal Digital Assistant (PDAs) without any pre-existing infrastructure. Mobile computing includes a number of technologies and devices. The state of the user, static or mobile, does not affect the information management capability of the mobile platform. A user can continue to access and perform data manipulation during the state of mobility by using mobile computing devices. Delivering data packets between pair of processors is a primary responsibility of any mobile computing network This activity is performed using a routing strategy Maximize reliability of the data packets always a major concern while transmitting the data from one point to another point. In mobile computing it is the process of determining the data path through the network that data packets will move from the one computing device to another computing device. Routing strategy for data transmission is an important factor for achieving high degree of reliability. Multiple data packets are move within a network from source to destination in order to execute on available processing units with the objective of getting maximum reliability of data packets. This research paper present a routing strategy to achieve maximum data packets transmission reliability in mobile computing network during the data transmission.

关键词： Data transmission Mobile computing performance Routing Reliability

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The Scalability of Volunteer Computing for MapReduce Big Data applications 1

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3rd international conference of Pioneering Computer Scientists, Engineers and Educators (ICPCSEE)

作者： Li, Wei Guo, William Cent Queensland Univ Sch Engn & Technol Rockhampton Qld 4702 Australia

ISBN: (数字)9789811063855

ISBN: (纸本)9789811063855;9789811063848

Volunteer Computing (VC) has been successfully applied to many compute-intensive scientific projects to solve embarrassingly parallel computing problems. There exist some efforts in the current literature to apply VC to data-intensive (i.e. big data) applications, but none of them has confirmed the scalability of VC for the applications in the opportunistic volunteer environments. This paper chooses MapReduce as a typical computing paradigm in coping with big data processing in distributed environments and models it on DHT (distributed Hash Table) P2P overlay to bring this computing paradigm into VC environments. The modelling results in a distributed prototype implementation and a simulator. The experimental evaluation of this paper has confirmed that the scalability of VC for the MapReduce big data (up to 10 TB) applications in the cases, where the number of volunteers is fairly large (up to 10K), they commit high churn rates (up to 90%), and they have heterogeneous compute capacities (the fastest is 6 times of the slowest) and bandwidths (the fastest is up to 75 times of the slowest).

关键词： MapReduce

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