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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： Jian Nong Jia Chen Yinqing Wang Wei Qin Xi He Faculty of Innovation Engineering-School of Computer Science and Engineering Macau University of Science and Technology Macau China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology Wuzhou University Wuzhou China High Performance Computing Lab Wuzhou University Wuzhou China

Large energy consumption in data centers has become a challenging problem with the emergence of cloud computing and large scale data centers. In this paper, we present an architectural framework for thermal-aware resource management while considering energy efficiency. The framework consists of a layered architecture and integrates a set of easy-to-use client tools and a thermal-aware task management middleware to schedule tasks based on thermal conditions within a cluster and among different data centers. As part of this paper we focus on the development of a thermal-aware task scheduling component for a single data center. This component is fundamental to our future activities, while considering to balance the temperature distribution in a single data center, thus implicitly minimizing energy cost in data centers.

关键词： Data centers Temperature distribution Schedules Processor scheduling Thermal management Ubiquitous computing Energy efficiency

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Scheduling Coflows for Minimizing the Makespan in Identical Parallel Networks

arXiv

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arXiv 2023年

作者： Chen, Chi-Yeh Chen, Jun Department of Computer Science and Information Engineering National Cheng Kung University Taiwan

With the rapid advancement of technology, parallel computing applications have become increasingly popular and are commonly executed in large data centers. These applications involve two phases: computation and communication, which are executed repeatedly to complete the work. However, due to the ever-increasing demand for computing power, large data centers are struggling to meet the massive communication demands. To address this problem, coflow has been proposed as a networking abstraction that captures communication patterns in data-parallel computing frameworks. This paper focuses on the coflow scheduling problem in identical parallel networks, where the primary objective is to minimize the makespan, which is the maximum completion time of coflows. It is considered one of the most significant NP-hard problems in large data centers. In this paper, we consider two problems: flow-level scheduling and coflow-level scheduling. In the flow-level scheduling problem, distinct flows can be transferred through different network cores, whereas in the coflow-level scheduling problem, all flows must be transferred through the same network core. To address the flow-level scheduling problem, this paper proposes two algorithms: (formula presented)-approximation algorithm and a (formula presented)-approximation algorithm, where m represents the number of network cores. For the coflow-level scheduling problem, this paper proposes a (2m)-approximation algorithm. Finally, we conduct simulations on our proposed algorithm and Weaver’s algorithm, as presented in Huang et al. (2020) in the 2020 IEEE international Parallel and distributed Processing symposium (IPDPS). We also validate the effectiveness of the proposed algorithms on heterogeneous parallel networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Approximation algorithms

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A Solution for A Disaster Recovery Service System in Multi-cloud Environment

A Solution for A Disaster Recovery Service System in Multi-c...

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international Applied Computational Electromagnetics Society symposium (ACES)

作者： Bin Liu Yang Xin Chunyong Zhang College of Information and Network Engineering Anhui Science and Technology University Bengbu China National Engineering Research Center for Disaster Backup and Recovery Beijing University of Posts and Telecommunications Beijing China

The current enterprise information architecture is becoming more complex. As a result, user business data is lost or destroyed, services are interrupted, and systems crash, all of which harm users’ businesses. These factors include equipment failure, network attacks, and human error. Data disaster recovery has become an important method to ensure the sustainability of enterprise business. It is an inevitable trend toward disaster recovery services and technologies that “business in the cloud, cloud disaster recovery, and multi-cloud disaster recovery.” Traditional disaster recovery services systems and single-cloud disaster recovery services systems are unsatisfied with users’ current demands for business system deployment in multi-cloud environments, cloud resource sharing, and cross-cloud disaster recovery. We discuss the structure, composition, and functional ideas of a solution for a disaster recovery service system in multi-cloud environments. To provide cross-cloud disaster recovery management service in a multi-cloud environment with full-stack virtualization in response to user business requirements, it combines conventional disaster recovery technology, blockchain distributed multi-copy data storage and security audit, and system security state awareness.

关键词： Cloud computing Memory Market research Software Security Resource management Time factors

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LoWAR: Enhancing RDMA over Lossy WANs with Transparent Error Correction

LoWAR: Enhancing RDMA over Lossy WANs with Transparent Error...

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international Workshop on Quality of Service

作者： Tianyu Zuo Tao Sun Shuyong Zhu Wenxiao Li Lu Lu Zongpeng Du Yujun Zhang University of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences China Mobile Research Institute

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

As the increase of geographically distributed applications continues, the demand for high-speed, long-distance data transmission across wide area networks (WANs) has significantly increased. Remote Direct Memory Access (RDMA) is extensively deployed in data center networks (DCNs) for its high throughput, low latency, and reduced CPU utilization, and its extension to WANs is expected to fully leverage these benefits. However, existing RDMA solutions, while demonstrating superior performance in data centers, face a performance gap over WANs due to their reliance on DCNs for optimal performance and lack of optimization for WANs’ high latency and loss rates. To bridge this gap, we introduce Lossy Wide-Area RDMA (LoWAR), a high-goodput, high-reliability RDMA solution for lossy WANs. LoWAR incorporates a forward error correction (FEC) shim layer to protect RDMA messages from packet loss, thus minimizing the inefficiency of retransmissions. It also fully offloads processing to RNICs with minimal computational overhead and storage burden, operating transparently on RNICs without requiring modifications to existing applications and networks. We implement a LoWAR prototype with FPGA and evaluate its performance through testbed experiments. The results demonstrate LoWAR’s enhanced performance in lossy WANs: in WANs with 40ms RTT and 0.001% to 0.01% loss rates, LoWAR increases RDMA goodput by 2.05 to 5.01 times, reduces average flow completion times (FCTs) by 3.5% to 12.2%, and eliminates 99th percentile tail FCTs in most scenarios.

关键词： Wide area networks Data centers Packet loss Tail Quality of service Forward error correction Maintenance engineering

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OMB-Py: Python Micro-Benchmarks for Evaluating Performance of MPI Libraries on HPC Systems

OMB-Py: Python Micro-Benchmarks for Evaluating Performance o...

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IEEE international symposium on Parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Nawras Alnaasan Arpan Jain Aamir Shafi Hari Subramoni Dhabaleswar K Panda Department of Computer Science and Engineering The Ohio State University Columbus Ohio USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Python has become a dominant programming language for emerging areas like Machine Learning (ML), Deep Learning (DL), and Data science (DS). An attractive feature of Python is that it provides easy-to-use programming interface while allowing library developers to enhance performance of their applications by harnessing the computing power offered by High Performance computing (HPC) platforms. Efficient communication is key to scaling applications on parallel systems, which is typically enabled by the Message Passing Interface (MPI) standard and compliant libraries on HPC hardware. mpi4py is a Python-based communication library that provides an MPI-like interface for Python applications allowing application developers to utilize parallel processing elements including GPUs. However, there is currently no benchmark suite to evaluate communication performance of mpi4py-and Python MPI codes in general-on modern HPC systems. In order to bridge this gap, we propose OMB-Py-Python extensions to the open-source OSU Micro-Benchmark (OMB) suite-aimed to evaluate communication performance of MPI-based parallel applications in Python. To the best of our knowledge, OMB-Py is the first communication benchmark suite for parallel Python applications. OMB-Py consists of a variety of point-to-point and collective communication benchmark tests that are implemented for a range of popular Python libraries including NumPy, CuPy, Numba, and PyCUDA. Our evaluation reveals that mpi4py introduces a small overhead when compared to native MPI libraries. We plan to publicly release OMB-Py to benefit the Python HPC community.

关键词： Performance evaluation Message passing Benchmark testing Programming Parallel processing Market research Libraries

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Enhancing Smart City and IoT Solutions with Deep Residual Bi-LSTM Model

Enhancing Smart City and IoT Solutions with Deep Residual Bi...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), international Conference on

作者： Madumidha. S S Poorna Chander Rao Sameer Yadav Syed Ghyasuddin Hashmi Jorair Ahmad Ziauddin Syed Department of Information Technology Sri Krishna College of Technology Tamilnadu India EEE Department Geethanjali College of Engineering and Technology Hyderabad Telangana India Department of Commerce and Business Administration University of Allahabad Prayagraj Uttar Pradesh India Department of Information Technology & Security College of Computer Science & Information Technology Jazan University Jazan Saudi Arabia Information Technology & Security College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia Department of Computer Science College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia

There is a limitless supply of useful information and services made available by IoT devices connected to the internet. The Internet of Things (IoT) refers to the vast network of interconnected devices seen in a smart city. The scattered and dynamic natures of entities such as sensing, actuating devices, humans, and computing resources make it challenging to design applications in such large-scale IoT systems, despite the promise nature of the new breed of data and resources. In this research study, the proposed approach takes a coordinated look at the process of creating Internet of Things apps for use in Smart Cities. The proposed approach demonstrates the need for a distributed coordination model capable of monitoring such a vast number of independently operating parts while constructing Smart City IoT applications. Preprocessing, feature selection, and model performance evaluation are all used in the suggested method. Normalization and standardization are used during preprocessing. It uses entropy HOA for feature selection. The model's effectiveness is measured with Deep Residual Bi-LSTM. When compared to BiLSTM and Residual LSTM, two existing approaches, the suggested methodology performs admirably.

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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： Hui Guo Di Xie Jiahao Pang Jie He Caixu Xu Xuejian Zhang Minglang Chen Faculty of Arts and Humanities Macao University of Science and Technology Wuzhou China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China college of Electronical and Information Engineering Wuzhou University Wuzhou China

At present, the self-similarity of images has received wide attention in many fields such as image compression. Most of the images have strong self-similarity through the human eye visual system observation. However, the special images with strong self-similarity for human eye observation such as biomicroscopy are not digitally reflected by a certain algorithm, this paper proposes a self-similarity coefficient algorithm that is applicable to special natural images such as biomicroscopy and aerial photography, which has high self-similarity coefficients for biomicroscopy and other images.

关键词： Photography Image coding Microscopy Visual systems Ubiquitous computing Biomedical imaging

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Temporal Data Analytics on COVID-19 Data with Ubiquitous computing 18

Temporal Data Analytics on COVID-19 Data with Ubiquitous Com...

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18th IEEE Int Symp on Parallel and distributed Proc with Applicat (ISPA) / 10th IEEE Int Conf on Big Data and Cloud Comp (BDCloud) / IEEE Int Symp on Social Comp and Networking (SocialCom) / IEEE Int Conf on Sustainable Comp and Commun (SustainCom)

作者： Chen, Yubo Leung, Carson K. Shang, Siyuan Wen, Qi Univ Manitoba Dept Comp Sci Winnipeg MB Canada

ISBN: (纸本)9781665414852

With technological advancements in computing and communications, huge amounts of big data are generated and collected at a very rapid rate from a wide variety of rich data sources. Embedded in these big data are useful information and valuable knowledge. An example is healthcare and epidemiological data such as data related to patients who suffered from viral diseases like the coronavirus disease 2019 (COVID-19). Knowledge discovered from these epidemiological data via data science helps researchers, epidemiologists and policy makers to get a better understanding of the disease, which may inspire them to come up ways to detect, control and combat the disease. In this paper, we present a temporal data science algorithm for analyzing big COVID-19 epidemiological data, with focus on the temporal data analytics with ubiquitous computing. The algorithm helps users to get a better understanding of information about the confirmed cases of COVID-19. Evaluation results show the benefits of our system in temporal data analytics of big COVID-19 data with ubiquitous computing. Although the algorithm is designed for temporal data analytics of big epidemiological data, it would be applicable to other temporal data analytics of big data in many real-life applications and services.

关键词： data science coronavirus disease COVID-19 big data temporal data data mining ubiquitous computing

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Maritime Broadband Communications: applications, Challenges and an Offshore 5G-virtual MIMO Paradigm 18

Maritime Broadband Communications: Applications, Challenges ...

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作者： Yu, Cunqian Li, Jingyu Zhang, Chunfang Li, Hang He, Rongxi Lin, Bin Dalian Maritime Univ Coll Informat Sci & Technol Dalian Peoples R China

ISBN: (纸本)9781665414852

Orienting to the competitive exploration of marine resources and the ship intelligent navigation, the broadband and stable maritime communications has regarded as a matter of concern. Due to the particularity of the ocean environment, the maritime communication network is severely restricted compared to the terrestrial one. By understanding the requirement of maritime business, this article deepens the research on maritime communications at offshore areas and ocean and makes a summary of the urgent problems and mainstream communication technologies. Besides, an experimental type of maritime network paradigm based on the coast-5G network involving virtual MIMO technology is present, with a view to providing open issues for the offshore maritime broadband communications.

关键词： maritime broadband communication virtual MIMO 5G network open platform structure

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Accuracy Enhancement in Machine Learning During Blockchain Based Transaction Classification

Accuracy Enhancement in Machine Learning During Blockchain B...

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international Conference on Parallel distributed and Grid computing (PDGC)

作者： Vipin Jain Shehab Mohamed Beram Veera Talukdar Trupti Patil Dharmesh Dhabliya Ankur Gupta Department of Management Teerthanker Mahaveer University Moradabad Uttar Pradesh India Department of CIS School of Engineering and Technology Sunway University Malaysia Dept. of IT and Management Shri Ram College of Commerce Science and Arts Maharashtra Department of Engineering and Technology Bharati Vidyapeeth Deemed to be University Navi Mumbai Maharashtra India Vishwakarma Institute of Information Technology Pune Maharashtra India Department of CSE Vaish College of Engineering Rohtak Haryana India

ISBN: (纸本)9781665454025

Blockchain is a new distributed ledger system that enables for safe online transactions without need for a trusted third party. As blockchain technology continues to advance at a dizzying pace, the question of smart contracts and how they should evolve has risen to the forefront of academic and business discussions. With this paper, we want to shed light on how smart contracts built on the blockchain really function. To better grasp the whole picture, researchers are taking into account scholarly works that have been vetted for their quality and are presenting a comprehensive overview of the relevant literature. Future work on decentralized apps might benefit from this. The first part provides a high-level introduction of smart contracts, covering topics such as their rationale, structure, and use cases. In addition, a thorough analysis of other smart contract development platforms reveals that the blockchain used in research is the most effective platform for improving QoS. In other words, it is developing low-cost smart contracts for distributed applications. Further, we give simulation work that uses the Blockchain transaction as a data set to educate an AI model that can help forecast whether or not the transaction will be successful. Finally, this paper also emphasizes key difficulties and future research implications in the decentralized uses of Blockchain smart contracts.

关键词： Filtering distributed ledger Computational modeling Smart contracts Quality of service Machine learning Predictive models

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