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Proceedings. IEEE International Conference on Cloud Computing最新文献

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ArcaDB: A Disaggregated Query Engine for Heterogenous Computational Environments. ArcaDB:异构计算环境的分类查询引擎。
Pub Date : 2024-07-01 Epub Date: 2024-08-28 DOI: 10.1109/cloud62652.2024.00015
Kristalys Ruiz-Rohena, Manuel Rodriguez-Martínez

Modern enterprises rely on data management systems to collect, store, and analyze vast amounts of data related to their operations. Nowadays, clusters and hardware accelerators (e.g., GPUs, TPUs) have become a necessity to scale with the data processing demands in many applications related to social media, bioinformatics, surveillance systems, remote sensing, and medical informatics. Given this new scenario, the architecture of data analytics engines must evolve to take advantage of these new technological trends. In this paper, we present ArcaDB: a disaggregated query engine that leverages container technology to place operators at compute nodes that fit their performance profile. In ArcaDB, a query plan is dispatched to worker nodes that have different computing characteristics. Each operator is annotated with the preferred type of compute node for execution, and ArcaDB ensures that the operator gets picked up by the appropriate workers. We have implemented a prototype version of ArcaDB using Java, Python, and Docker containers. We have also completed a preliminary performance study of this prototype, using images and scientific data. This study shows that ArcaDB can speed up query performance by a factor of 3.5x in comparison with a shared-nothing, symmetric arrangement.

现代企业依赖数据管理系统来收集、存储和分析与其运营相关的大量数据。如今,集群和硬件加速器(如 GPU、TPU)已成为许多与社交媒体、生物信息学、监控系统、遥感和医疗信息学相关的应用扩展数据处理需求的必需品。在这种新形势下,数据分析引擎的架构必须不断发展,以利用这些新的技术趋势。在本文中,我们介绍了 ArcaDB:一种分解查询引擎,它利用容器技术将操作员放置在适合其性能配置的计算节点上。在 ArcaDB 中,查询计划被分派到具有不同计算特性的工作节点上。每个运算符都会被注释为执行时首选的计算节点类型,ArcaDB 会确保运算符被相应的工作节点接收。我们使用 Java、Python 和 Docker 容器实现了 ArcaDB 的原型版本。我们还使用图像和科学数据完成了对该原型的初步性能研究。这项研究表明,与无共享的对称安排相比,ArcaDB 可将查询性能提高 3.5 倍。
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引用次数: 0
Hestia: A Cost-Effective Multi-dimensional Resource Utilization for Microservices Execution in the Cloud Hestia:在云中执行微服务的经济高效的多维资源利用
Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-23498-9_3
Jiahua Huang, K. Kent, Jerome Yen, Yang Wang
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引用次数: 0
Improving Few-Shot Image Classification with Self-supervised Learning 基于自监督学习的少镜头图像分类改进
Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-23498-9_5
Shisheng Deng, Dongping Liao, Xitong Gao, Juanjuan Zhao, Kejiang Ye
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引用次数: 1
Cloud Computing - CLOUD 2022 - 15th International Conference, Held as Part of the Services Conference Federation, SCF 2022, Honolulu, HI, USA, December 10-14, 2022, Proceedings 云计算-云2022 -第15届国际会议,作为服务会议联合会的一部分,SCF 2022,檀香山,HI, USA, 12月10-14日,2022,会议录
Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-23498-9
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引用次数: 0
Towards an Efficient Client Selection System for Federated Learning 面向联邦学习的高效客户端选择系统研究
Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-23498-9_2
Guanyu Ding, Zhengxiong Li, Yusen Wu, Xiaokun Yang, Mehrdad Aliasgari, Hailu Xu
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引用次数: 3
New Commonsense Views Inspired by Infants and Its Implications for Artificial Intelligence 婴儿启发的新常识观点及其对人工智能的影响
Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-23498-9_6
Kai Liu, Ao-nan Wang, N. Li, Han-lin Ma, Hong Gao
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引用次数: 0
Performance Evaluation of Modified Best First Decreasing Algorithms for Dynamic Virtual Machine Placement in Cloud Computing 云计算中动态虚拟机布局的改进最优先降算法性能评价
Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-23498-9_1
J. Akinwumi, I. Adeyanju
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引用次数: 0
Optimizing Cache Accesses with Tensor Memory Format Search for Transformers in TVM TVM中变压器的张量存储器格式搜索优化缓存访问
Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-23498-9_4
Xianghuan He, Xitong Gao, Juanjuan Zhao, Chengxi Gao, Kejiang Ye
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引用次数: 0
Analysis of Data Micro-governance in Full Life Cycle Management of the Leased Assets 租赁资产全生命周期管理中的数据微观治理分析
Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-23498-9_7
Xiaofeng Gou
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引用次数: 0
How to Build an Efficient Data Team 如何打造高效的数据团队
Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-23498-9_8
Rui Zhao
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引用次数: 0
期刊
Proceedings. IEEE International Conference on Cloud Computing
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