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检索条件"任意字段=International Conference on Parallel Distributed and Grid Computing"

共 34268 条记录，以下是891-900 订阅

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Modeling and Simulation of Collective Algorithms on HPC Network Topologies using Structural Simulation Toolkit

Modeling and Simulation of Collective Algorithms on HPC Netw...

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2024 Workshops of the international conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Chenna, Sai P. Steyer, Michael Kumar, Nalini Garzaran, Maria Thierry, Philippe Intel Corporation United States

ISBN: (纸本)9798350355543

In the last decade, DL training has emerged as an HPC-scale workload running on large clusters, the size of the largest supercomputers on the Top500 list. The dominant communication pattern in distributed data-parallel DL training is allreduce which is used to sum the model gradients across processes during backpropagation phase. Various allreduce algorithms have been developed to optimize communication time in DL training. Given the scale of DL workloads, it is crucial to evaluate the scaling efficiency of these algorithms on a variety of system architectures. We have extended the Structural Simulation Toolkit (SST) to simulate allreduce and barrier algorithms - Rabenseifner, ring, and, dissemination algorithms. We performed a design space exploration (DSE) study with three allreduce algorithms and two barrier algorithms running on six system network topologies for various message sizes. We quantified the performance benefits of using allreduce algorithms which preserve locality between communicating processes. In addition, we evaluated the scaling efficiency of centralized and decentralized barrier algorithms. © 2024 IEEE.

关键词： collectives design-space exploration discrete event simulation distributed training interconnect design network simulation performance modeling

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A comparative study of two different optimization methods applied to the static job scheduling problem in grid computing 9

A comparative study of two different optimization methods ap...

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9th international conference on Computer and Communications, ICCC 2023

作者： Hontinfinde, Rgis Donald Kamoyedji, Ariel Vitouley, Marcos Thyrbus Smako Honfo, Roland Laboratory of Science Engineering and Mathematics Abomey Benin Okayama University Department of Information and Communication Systems Okayama Japan

ISBN: (纸本)9798350317251

In the telecommunications sector where real-time data processing and low latency are essential, grid computing promotes faster response times and reduced network congestion. grid computing has since become a very powerful and indispensable tool for telecommunications operators because it allows companies to optimize their operations, improve the performance of their network and provide better services to their customers. One of the distributed computing systems used in telecommunications is the User-PC (UPC) system. The User-PC computing (UPC) system provides a cost-effective distributed computing platform by utilizing the unused resources of personal computers (PCs). Effective job allocation methods are essential for optimizing system performance. In this study, we introduce a comparative analysis of the static job scheduling problem in the UPC system, focusing on the performance comparison between our proposed hybrid approach, which combines a greedy algorithm and a algorithm genetics, and the approach based on Randomized Multi-Start Local Search. Our hybrid algorithm is composed of two steps. In the first step, a Greedy Algorithm is used to create an initial population of candidate solutions by assigning jobs to available workers based on CPU core utilization. In the second step, a Genetic Algorithm is used to optimize job-worker assignments through selection, crossover and mutation operations. To evaluate the performance of our approach, extensive experiments were conducted with up to 27 jobs and 6 worker PCs. The results show that our hybrid approach achieved a shorter makespan compared to the approach based on Randomized Multi-Start Local Search. Specifically, our algorithm outperformed the multi-start random local search-based method by more than 31% in terms of makespan reduction. The experimental results highlight the effectiveness of our hybrid approach to improve job scheduling performance in the UPC system. © 2023 IEEE.

关键词： Genetic algorithms

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Containerized AI Framework on Secure Shared Multi-GPU Systems 7

Containerized AI Framework on Secure Shared Multi-GPU System...

引用

7th international conference on parallel, distributed and grid computing, PDGC 2022

作者： Kumar, Mandeep Kaur, Gagandeep Chandigarh University University Institute of Computing Mohali India Locuz Enterprise Solutions Ltd Delhi India

ISBN: (纸本)9781665454018

Nowadays, technology has reached the point where Graphics Processing Unit (GPU) drive Artificial Intelligence (AI) workloads with large datasets to speed up the process of solving more complex real-world problems. The DevOps movement has brought container-based deployment models into the world of software development and application deployment. Data scientists can quickly prototype, build, and deploy a containerized application or service, allowing them to manage AI workloads more effectively with portability. Docker is a widely used containerization platform, requiring root privileges for execution, which has associated security concerns and may not be readily adopted in secure shared multi-GPU systems. Singularity addresses the needs of container-based virtualization in a secure environment, which allows the same Docker containers to be used without any changes or security concerns. This work presents the challenges and approaches for quickly deploying complex AI workloads on multi-GPU systems without compromising security by ensuring that AI frameworks run in an isolated container environment with user credentials. In addition, a comprehensive performance evaluation of containers converted from Docker to Singularity in the context of native is performed on a multi-GPU system to solve more complex real-world problems with AI workflow containerization. © 2022 IEEE.

关键词： Graphics processing unit

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distributed Analytics in Smart grids with Edge computing and Computational Intelligence

Distributed Analytics in Smart Grids with Edge Computing and...

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2023 international conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Joshi, Apoorva Preethi, D. Gill, Amandeep Noida Institute of Engineering & Technology Master of Computer Application Uttar Pradesh Greater Noida India Department of Computer Science Bangalore India Vivekananda Global University Department of R&d Cell Jaipur India

ISBN: (纸本)9798350357769

Conventional methods like classification or clustering have limits when it comes to handling the complex task for dispersed data mining across heterogeneous sources and divergent data formats. This is especially true in scenarios where stochastic models or mathematical formulas may not be feasible. Data centralization is a prevalent trend that exacerbates problems including communication channel congestion and privacy difficulties related to sensitive data flows. In order to overcome these obstacles, this paper presents the distributed Academic Engine (DAE), an adaptive edge-based scattered analytic engine designed for distributed networks. The DAE routinely breaks down and divides computations that need data from multiple sources among the nodes that are already in place, enabling the sharing of partial results without disclosing the original data. To get the full expression output, the master node combines these partial results. The expression is decomposed and processing jobs are distributed among nodes using three computational intelligence techniques: genetic algorithm, genetic algorithm using evolution control, the particle swarm optimization. This method's effectiveness is illustrated with a case study on smart grid KPI identification, which shows a noteworthy (over 91%) decrease in communication messages over conventional approaches. This demonstrates how well the suggested method works to reduce the difficulties related to distributed data mining. © 2023 IEEE.

关键词： Computational Intelligence Data Integration and Heterogeneous Data Source Integration distributed Analytics Edge computing Genetic Algorithm Particle Swarm Optimization Smart grid

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Research on Power Quality Detection in Power Systems Based on Improved Particle Swarm Optimization Algorithm

Research on Power Quality Detection in Power Systems Based o...

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2023 international conference on Internet of Things, Robotics and distributed computing, ICIRDC 2023

作者： Huang, Qingsha Xue, Xianhui Foshan Power Supply Bureau of Guangdong Power Grid Co. Ltd. Guangdong Foshan528000 China

ISBN: (纸本)9798350375275

At present, there is a certain gap between China and the international community in terms of understanding and practical measures for power quality issues, and further detailed power quality management work is needed. This article is based on the IPSO (Improved particle swarm optimization algorithm) to analyze the initial position and amplitude changes of voltage dips and voltage interruptions in response to the large proportion of transient quality in power market problems. Due to the varying degrees of flatness deviation that devices that consume electrical energy can withstand during operation in the power system, precision instruments are more sensitive to frequency deviation and therefore require higher requirements. The final research results indicate that the fusion using the full probability calculation formula cannot reflect such changes. This indicates that using the method presented in this article for comprehensive evaluation of power quality is reasonable, and the final evaluation result is correct. And as the evidence increases, this level of credibility increases even faster. It also conforms to the logical belief that the more valid evidence collected, the clearer the understanding of the matter, the more reasonable the judgment made, and the greater the grasp. © 2023 IEEE.

关键词： Power quality

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Machine learning-based malware detection using stacking of opcodes and bytecode sequences 7

Machine learning-based malware detection using stacking of o...

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7th international conference on parallel, distributed and grid computing, PDGC 2022

作者： Sai, Manoj Tyagi, Aakansha Panda, Kajal Kumar, Sanjeev Centre for Development of Advanced Computing Cyber Security Technology Division Mohali India

ISBN: (纸本)9781665454018

Malware detection is a complex problem. The commonly used signature-based technique cannot detect unknown or zero-day malware. Traditional machine learning-based methods can identify unknown malicious programs but require high domain expertise for feature engineering. This research study presents a new method of malware detection using the stacking of static opcode and bytecode features. At first, bytecode and opcode sequences of executables files are extracted using a disassembler. After that, an NLP technique, TF-IDF, is employed to vectorize the extracted feature map. The extracted features as opcodes and bytecodes are concatenated to combine a single feature map, which serves as input to train machine learning classifiers: SVM, k-NN, and Random Forest (RF). The RF classifier obtained the best accuracy, precision, recall, and F-score results as 98.47%, 98.60%,98.46%, and 98.47% even using small data counts of bench-marked Microsoft BIG data set. The accuracy of the best model is comparatively better than the accuracy of using bytecodes and opcodes as individual feature vectors. The empirical results signify that the proposed method is helpful to the security industries with the benefit of being less dependent on manual feature engineering and can reduce the burden on virus databases by handling large-scale malware. © 2022 IEEE.

关键词： Viruses

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Duo: Improving Data Sharing of Stateful Serverless Applications by Efficiently Caching Multi-Read Data 37

Duo: Improving Data Sharing of Stateful Serverless Applicati...

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37th IEEE international parallel and distributed Processing Symposium (IPDPS)

作者： Huang, Zhuo Fan, Hao Cheng, Chaoyi Wu, Song Jin, Hai Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Serv Comp Technol & Syst Lab Cluster & Grid Comp LabSch Comp Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9798350337662

A growing number of applications are moving to serverless architectures for high elasticity and fine-grained billing. For stateful applications, however, the use of serverless architectures is likely to lead to significant performance degradation, as frequent data sharing between different execution stages involves time-consuming remote storage access. Current platforms leverage memory cache to speed up remote access. However, conventional caching strategies show limited performance improvement. We experimentally find that the reason is that current strategies overlook the stage-dependent access patterns of stateful serverless applications, i.e., data that are read multiple times across stages (denoted as multi-read data) are wrongly evicted by data that are read only once (denoted as read-once data), causing a high cache miss ratio. Accordingly, we propose a new caching strategy, Duo, whose design principle is to cache multi-read data as long as possible. Specifically, Duo contains a large cache list and a small cache list, which act as Leader list and Wingman list, respectively. Leader list ignores the data that is read for the first time to prevent itself from being polluted by massive read-once data at each stage. Wingman list inspects the data that are ignored or evicted by Leader list, and pre-fetches the data that will probably be read again based on the observation that multi-read data usually appear periodically in groups. Compared to the state-of-the-art works, Duo improves hit ratio by 1.1x-2.1x and reduces the data sharing overhead by 25%-62%.

关键词： serverless computing data sharing remote storage cache

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Joint Resource Placement and Service Replica Placement Scheme in Mobile Edge computing 21

Joint Resource Placement and Service Replica Placement Schem...

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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： Woldeyes, Surafel Kifetew Zhang, Yongmin Wang, Wei Li, Zhaohui Central South University School of Computer Science and Engineering Changsha410083 China

ISBN: (纸本)9798350329223

Optimal resource placement and service placement are key factors for resource utilization and service availability in mobile edge computing (MEC) systems. However, efficiently utilizing the resources of MEC servers for service replica placement becomes a significant challenge due to the servers' limited resource capacity. To overcome this challenge, in this paper, we propose a comprehensive model for jointly addressing resource placement and service replica placement within the MEC system. The proposed model considers various factors, including the limited capacity of edge servers, the presence of service replicas, server failure probabilities, and the heterogeneous resource demand of services in terms of CPU, network bandwidth, and storage capacities. We then formulated the joint resource placement and service replica placement problem as a multi-objective optimization problem from the perspective of minimizing server resource waste, reducing server energy consumption, and ensuring high service availability. To solve this problem, we first proposed a resource-aware algorithm for the initialization process, which generates high-quality initial solutions that satisfy the server resource constraints. Then we propose an improved non-dominated sorting genetic algorithm II with two-phase chromosome representation to find a suitable placement solution. Evaluation results show the superiority of our approach compared with other benchmark algorithms in terms of minimizing service unavailability, resource waste, and energy consumption. © 2023 IEEE.

关键词： Energy utilization

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Service-oriented Intelligent OODA Loop 26

Service-oriented Intelligent OODA Loop

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26th ACIS international Winter conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed computing, SNPD-Winter 2023

作者： Zhao, Yan Wen, Pengcheng Bai, Linting Liu, Xinzhi Wang, Zhonghua Wu, Na Technique Research Institute Avic Xi'an Aeronautics Computing Xi'an China

ISBN: (纸本)9798350345865

The OODA (Observe, Orient, Decide, Act) loop is a widely used decision-making model in various domains, including military operations, business strategy and cybersecurity. With the emergence of intelligent technologies, the intelligent OODA loop has gained increasing attention in recent years. However, the implementation of the intelligent OODA loop remains a challenging task as the intelligent algorithms introduce additional complexity in service implementation and service communication. To address this issue, this paper proposes to employ a service-oriented architecture for implementing the intelligent OODA loop. The proposed service-oriented intelligent OODA loop comprises several key components, including task decomposition, service description definition, intelligent service communication design, security and flow management strategies, and integration of necessary intelligent hardware. The proposed service-oriented intelligent OODA loop has the potential to provide significant benefits in improving speed, accuracy, scalability, cost-effectiveness and adaptability. © 2023 IEEE.

关键词： Cost effectiveness

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Time Fairness-Based Application Offloading in Mobile Edge computing with Individual QoS Guarantee 21

Time Fairness-Based Application Offloading in Mobile Edge Co...

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作者： Xie, Mengwei Cui, Lizhen Liu, Jianxiong Guo, Wei Li, Feng Shandong University School of Computer Science and Technology Qindao China Shandong University School of Software Jinan China Aerospace Science & Industry Network Information Development Co. Ltd. Beijing China

ISBN: (纸本)9798350329223

In the area of Mobile Edge computing (MEC), how to offload applications with QoS and fairness guarantees has been attracting increasing attention. Most existing application offloading strategies focus on reducing the average completion time of the applications to fulfill the global QoS requirement. However, when there are a huge number of mobile users for the applications, these strategies cannot guarantee the QoS for the individual users;therefore, the mobile users' experiences differ significantly, resulting in unfairness among the users. To address this problem, this paper presents a time fairness-based application offloading strategy for individual mobile users with individual QoS guarantees. This strategy is designed to achieve two primary objectives. Firstly, it seeks to assess the suitability of offloading each application to edge servers by evaluating its completion time, aiming to maximize the number of applications meeting individual QoS time requirements. Secondly, for applications that meet individual QoS time requirements, this strategy ensures time fairness among multiple users by minimizing the variance between application completion times and individual QoS time requirements. The empirical validation presented in this paper provides robust evidence of the effectiveness of our strategy, surpassing existing approaches, with a particular emphasis on achieving time fairness. © 2023 IEEE.

关键词： Quality of service

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