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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

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database Implementation using Citus on Telecommunication data 9

Database Implementation using Citus on Telecommunication Dat...

引用

9th IEEE international conference on computing, Engineering and Design, ICCED 2023

作者： Prawira, Muhammad Satria Abdussalam, Faiz Yossy, Emny Hama Bina Nusantara University BINUS Online Learning Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350370126

The rapid development of information technology has always related to the data collection and processing according to business needs and requirements. This data serves as an important source of information stored in a database and to be displayed in an information system. Along with the advancement of information technology, the demand for faster and larger data processing has become a challenge faced by many industries especially in the Telecommunications field. Therefore, an efficient database system is needed to handle the increasing volume of data, using storage methods such as distributed database and columnar storage to improve data processing performance and reduce its size. The use of these storage methods needs to be empirically and comparatively evaluated. This research uses K6 testing framework on the PostgreSQL database. The result indicates that the use of PostgreSQL database with distributed database and columnar storage methods can increase query processing performance in the database by 48.2% faster for select query commands, decrease by 3.3% for insert query command and 74.6% reduction of table storage size compared to PostgreSQL without these methods. Hence, the use of these storage methods is highly suitable and recommended for implementation in industries that requires fast and large-scale data processing such as Telecommunication Industry. © 2023 IEEE.

关键词： Digital storage

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Experimental Studies of the Complex Method of Increasing the Load processing Efficiency in Distributed data Centers

Experimental Studies of the Complex Method of Increasing the...

引用

2023 IEEE international Black Sea conference on Communications and Networking, BlackSeaCom 2023

作者： Globa, Larysa Raichuk, Andrii Gvozdetska, Nataliia Igor Sikorsky Kyiv Politechnic Institute Institute of Telecomunication System Kyiv Ukraine

ISBN: (纸本)9798350337822

In this paper, the experimental studies for increasing the load processing efficiency in distributed data centers of communication operators are presented. The communication operators widely use the up-to-day technologies function virtualization such as NFV, SDN, Network Slicing, Edge computing, and bDDN, which require a significant amount of calculations, and accordingly consume significant amounts of energy during load processing in data centers. Existing approaches do not provide simultaneous improvements in energy efficiency and performance while meeting SLA requirements. To ensure energy-efficient data processing and improve its processing performance, while maintaining the quality of SLA requirements, an approach was proposed, the essence of which is to take into account the daily load and allocate data processing resources in such a way as to minimize energy consumption and not lose performance during processing. When comparing the proposed approach with Backfill load planning, it was possible to determine an increase in energy efficiency indicators up to 9.953%, and up to 26.382% in comparison with Round Robin. © 2023 IEEE.

关键词： Energy utilization

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DAPHNE Runtime: Harnessing Parallelism for Integrated data Analysis Pipelines

DAPHNE Runtime: Harnessing Parallelism for Integrated Data A...

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29th international conference on Parallel and Distributed computing (Euro-Par)

作者： Vontzalidis, Aristotelis Psomadakis, Stratos Bitsakos, Constantinos Dokter, Mark Innerebner, Kevin Damme, Patrick Boehm, Matthias Ciorba, Florina Eleliemy, Ahmed Karakostas, Vasileios Zamuda, Ales Tsoumakos, Dimitrios ICCS Natl Tech Univ Athens Athens Greece Graz Univ Technol Know Ctr GmbH Graz Austria Graz Univ Technol Graz Austria Tech Univ Berlin Berlin Germany Univ Basel Basel Switzerland Univ Athens Athens Greece Univ Maribor Maribor Slovenia

ISBN: (纸本)9783031488023;9783031488030

Integrated data analysis pipelines combine rigorous data management and processing, high-performance computing and machine learning tasks. While these systems and operations share many compilation and runtime techniques, data analysts and scientists are currently dealing with multiple systems for each stage of their pipeline. DAPHNE is an open and extensible system infrastructure for such pipelines, including language abstractions, compilation and runtime techniques, multilevel scheduling, hardware accelerators and computational storage. In this demonstration, we focus on the DAPHNE runtime that provides the implementation of kernels for local, distributed and accelerator-enhanced operations, vectorized execution, integration with existing frameworks and libraries for productivity and interoperability, as well as efficient I/O and communication primitives.

关键词： Machine Learning Systems High Performance computing Vectorized Execution Distributed Systems

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Discrete Geometric Coded data Layout for Large-scale Object Storage Systems 21

Discrete Geometric Coded Data Layout for Large-scale Object ...

引用

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： Tian, Yi Xu, Guangping Yang, Hongzhang Ni, Yue Cao, JiaXin Yang, Lei Tianjin University of Technology School of Computer Science and Engineering Tianjin China Novel Software Technology Tianjin Key Laboratory of Intelligence Computing Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Information Technology Co. Ltd. China

ISBN: (纸本)9798350329223

Regenerating codes are new network codes proposed to reduce the data required for fault repair, which can improve the recovery efficiency of faulty nodes in data storage systems. However, unlike Reed-Solomon code, which repairs at the granularity of bytes, regenerating codes require data stored in large chunks but leads to severe read amplification, which reads out excess data when degraded read objects and increases degraded read time. That reflects a mutual constraint between improving recovery efficiency and degraded read performance, as manifested in the amplification of data reads, a vital issue considered in this *** solve this problem, we propose a new type of data layout - discrete geometric, which splits the object into a series of geometric sequences of data blocks. They are placed discretely into corresponding containers on the disks at different nodes, with containers of the same size made into a strip for encoding. The discrete characteristic ensures lower repair costs for degraded reads. The geometric characteristic ensures the repair performance of regenerating codes by large blocks, and read amplification can be mitigated through small blocks. To reduce IOPS for discrete geometric, we propose Discrete Geometric-Locally Regenerating Codes (DG-LRCs), guaranteeing lower degraded read latency while improving recovery *** results show that the degraded read time of DG-LRCs compared to regenerating codes combined with geometric partitioning is 22.56% lower at 2Gbpsand 60.56% lower at 4Gbps, and the recovery performance is 7.04 times better than that of RS code. © 2023 IEEE.

关键词： Efficiency

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data Analysis of E-Commerce Precision Marketing System Based on Artificial Intelligence Algorithm 2

Data Analysis of E-Commerce Precision Marketing System Based...

引用

2nd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Zhu, Jinfu Ningbo City College of Vocational Technology College of Business Zhejiang Ningbo315100 China

ISBN: (纸本)9798350347456

With the application of the Internet, big data and artificial intelligence technology in precision marketing, the outreach of precision marketing is expanding, and various marketing methods and marketing forms are constantly flocking to the market. The purpose of this paper is to study the data analysis of e-commerce precision marketing system based on artificial intelligence algorithm. Based on artificial intelligence, the current situation of e-commerce precision marketing is analysed to identify the problems in building or using big data of users for precision marketing. Combining technologies such as user portraits, BP neural networks and recommendation algorithms, we focus on all-round identification of users, segmentation of user groups, and then marketing according to user needs to bring users a good user experience and increase corporate profits. The research results show that through the rational use of artificial intelligence algorithms, users can accurately predict their purchasing behavior, and conduct personalized recommendation and marketing based on their needs and preferences. The data analysis of e-commerce precision marketing system based on artificial intelligence algorithms has played an important role in improving the level of enterprise precision operations, achieving precision marketing, and mining user value, and has a broad application prospect. © 2023 IEEE.

关键词： Big data

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A Survey on Mechanisms for Fast Network Packet processing 23

A Survey on Mechanisms for Fast Network Packet Processing

引用

4th international conference on computing, Networks and Internet of Things, CNIOT 2023

作者： Du, Yimin Chang, Keyang Shi, Jinglin Zhou, Yiqing Liu, Min Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400700705

As the Internet of Things becoming widely used, the requirement for high performance network is growing rapidly. Network performance is affected by data transmission based on physical link and packet processing handled by operating system. With the development of network hardware technology and wireless communication technology, network data transmission rate has been further improved, but the packet processing performance based on operating system cannot match it. There are several performance bottlenecks in the traditional packet processing, correspondingly there are various optimization mechanisms to solve these problems and further improve the performance. This paper analyzes some mainstream optimization mechanisms of packet processing based on software. Firstly, we analyze the architecture design of these mechanisms and the workflow when receiving packets. Then we compare the technical methods adopted by each mechanism and problems solved by each mechanism. Finally, we compare the application scenarios and the shortcomings of each mechanism. © 2023 ACM.

关键词： data transfer

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Detection and Classification of DDoS Attacks in Cloud data Using Hybrid LSTM and RNN for Feature Selection

Detection and Classification of DDoS Attacks in Cloud Data U...

引用

2023 international conference on Circuit Power and computing Technologies, ICCPCT 2023

作者： Karthika, R.A. Sriramya, P. Rohini, A. SRMIST Dept. of Computational Intelligence Chennai India Saveetha School of Engineering SIMATS Dept. of Artificial Intelligence and Data Science Chennai India Presidency University Dept. of CSE Bengaluru India

ISBN: (纸本)9798350333244

Cyber threats are the main source of concern in the sector it is deployed. DDoS attacks are at the very top of the list. The rapid growth of cloud migration also expands the attack surface. Different forms of DDoS attacks exist, such as DoS and DDoS etc., There are several existing machine learning algorithms to detect DDoS attacks. There are also many hybrids of machine learning algorithms which are introduced for detecting DDoS attacks to increase the accurateness of detection. Here we use hybrid Long Short-Term Memory Network (LSTM) and Recurrent Neural Network (RNN) for feature selection and deliver an improved detection system. The proposed exploratory model of Multi-layer Perceptron (MLP) classifiers uses deep neural network algorithms. The final outcome shows higher accuracy and precision by dealing with a huge dataset that was gathered. The accuracy and precision of 98.85% and 92 % respectively for grouping in structure. © 2023 IEEE.

关键词： Denial-of-service attack

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Predictive Queue-based Low Latency Congestion Detection in data Center Networks 21

Predictive Queue-based Low Latency Congestion Detection in D...

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作者： Dong, Pingping Lu, Xiaojuan Huang, Tairan Chen, Liying Yang, Yang Zhang, Lianming Hunan Normal University College of Information Science and Engineering Changsha China

ISBN: (纸本)9798350329223

End-to-end congestion control in lossless data center networks (DCN) depends on congestion detection. However, many current congestion control mechanisms ignore the mutually reinforcing relationship with congestion detection and flow identification, resulting in wasted link resources. For accurate congestion detection, the currently popular ternary congestion detection (TCD) technique sets three separate port states. However, the flow needs to go through multiple hops before deciding the flow category, which leads to serious delays. In order to achieve accurate congestion detection and low delay flow identification, we propose a predictive queue-based low latency congestion detection, called hop-by-hop flow identification (HFI) based on congestion detection. HFI predicts the dynamic queueing threshold based on the data transmission rate to provide timely and proactive congestion detection, then combined it with the congestion information notification from the next hop to identify the congested flow hop by hop. Experimental results show that DCQCN+HFI obtains shorter flow completion time, and improves link utilization by 55%, 7.5% compared to DCQCN, DCQCN+TCD. © 2023 IEEE.

关键词： Queueing theory

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Leveraging Neural Networks to Enhance Large-Scale data processing Performance 3

Leveraging Neural Networks to Enhance Large-Scale Data Proce...

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3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Pandey, Megha Vinay Kumar, S.B. Khurana, Jatin Kalpana, B. Pathak, Kishor R. Kumar, Mukesh Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India 1 Electronics and Communication Engineering Faculty of Engineering and Technology Karnataka Bangalore India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India

ISBN: (纸本)9798350319125

Recent advances in neural networks have enabled them to become powerful tools in data processing for tasks such as pattern recognition and classification. This has enabled neural networks to be applied to large-scale data processing problems, where they have been shown to perform very well. However, due to the large amount of data involved in such tasks, it is essential that the processing performance is also improved. This paper presents several methods to leverage available resources such as distributed computing and improved algorithmic techniques to improve the performance of large-scale data processing tasks using neural networks. Examples of such techniques are given for both supervised and unsupervised learning tasks, which can significantly enhance the performance of large-scale data processing applications. The results of several experiments are presented, demonstrating the effectiveness of the proposed techniques. The authors also provide insights for future work that could further enhance the capabilities of neural networks when applied to large-scale data processing applications. © 2023 IEEE.

关键词： Applications data Networks presented processing

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Multi-Server Based Task Offloading Strategy in Mobile Edge computing

Multi-Server Based Task Offloading Strategy in Mobile Edge C...

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2023 international conference on algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Wei, Zeju Gao, Jingnan Ruan, Peiguang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China

ISBN: (纸本)9781510671881

Edge computing, as a complement to cloud computing, has many unique advantages. In mobile edge computing (MEC) scenarios, tasks on terminal devices can be offloaded to edge servers for computing purposes, reducing the shortage of computing resources on terminal devices. Moreover, the resulting delay is much shorter than uploading the task to the cloud center directly. However, many scenarios in reality show that there are dependencies between tasks, and a task cannot be calculated until all its predecessor tasks have been calculated. In this paper, we summarize a variety of task dependencies. At the same time, we build an efficient task offloading model based on two levels of edge servers with different functions. In particular, the second-level edge server has the routing decision function, which provides us with diversified offloading methods. Finally, we used improved NSGA-Ⅱ for task offloading. Compared with other offloading methods, our algorithm performs better in latency and energy consumption. © 2023 SPIE. All rights reserved.

关键词： Energy utilization

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