CRII: CSR: Mobile-Aware Resource Management in Geo-Distributed Multi-Clouds

CRII：CSR：地理分布式多云中的移动感知资源管理

• 批准号: 1755659
• 负责人: Tian Guo
• 金额: $ 17.5万
• 依托单位: Worcester Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1755659&HistoricalAwards=false
• 关键词: CRII; CSR; Mobile; Aware; Resource

Modern mobile applications are increasingly relying on cloud data centers to obtain both compute and storage capabilities. To guarantee good performance for customers, cloud platforms usually provision resources dynamically in order to meet the demand of the fluctuating workload. Cloud providers also price their services dynamically to meet variable demand. Mobile workloads exhibit three distinct characteristics: geographic or spatial fluctuation, rapid changes characterized by a shorter time scale of fluctuation, and more frequent fluctuations, which make current resource provisioning approaches less effective. The MOBILESCALE project proposes new research on resource management for mobile workload to address dual objectives of handling fluctuations and optimizing cost and performance by leveraging application-specific optimization approaches, acknowledging the cost anomalies of cloud transient servers, and unifying resources from multiple cloud providers. The proposed work will involve the design and implementation of a mobile-aware resource management system, a set of mobile application benchmarks, and experimental evaluation of the proposed mobile-aware provisioning algorithms using home-grown mobile apps developed in this project.The project will open doors for research on making modern cloud platforms more intelligent with tailored application-specific optimizations. The MOBILESCALE project can be used and integrated with current general-purpose resource management systems to reduce manual intervention from operators and to mask operational complexity. The broader impacts of the project will include graduate and undergraduate courses that incorporate research results, outreach to expose undergraduates, high school and K-12 students to research in computer systems, and the release of MOBILESCALE source code to the research community through the project website.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

现代移动应用程序越来越依赖云数据中心来获得计算和存储能力。为了保证客户的良好性能，云平台通常会动态配置资源，以满足不断变化的工作负载的需求。云提供商还会动态为其服务定价，以满足不同的需求。移动工作负载表现出三个明显的特征：地理或空间波动、以波动时间尺度更短为特征的快速变化以及更频繁的波动，这使得当前的资源配置方法效率较低。MOBILESCALE项目建议对移动工作负载的资源管理进行新的研究，以解决通过利用特定于应用的优化方法处理波动和优化成本和性能的双重目标，承认云临时服务器的成本异常，并统一来自多个云提供商的资源。拟议的工作将涉及设计和实现移动感知资源管理系统，一组移动应用基准，以及使用该项目开发的自主开发的移动应用对建议的移动感知配置算法进行实验评估。该项目将为通过针对特定应用的优化使现代云平台变得更加智能的研究打开大门。MOBILESCALE项目可以与当前的通用资源管理系统结合使用，以减少操作员的人工干预，并掩盖操作的复杂性。该项目的更广泛影响将包括纳入研究成果的研究生和本科课程，让本科生、高中和K-12学生接触计算机系统研究的外联活动，以及通过项目网站向研究社区发布MOBILESCALE源代码。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

An Experimental Evaluation of Garbage Collectors on Big Data Applications

垃圾收集器大数据应用的实验评估

• DOI: 10.14778/3303753.3303762
• 发表时间: 2019
• 期刊: Proceedings of the VLDB Endowment (VLDB 2019)
• 作者: Lijie Xu;Tian Guo;Wensheng Dou;Wei Wang;Jun Wei
• 通讯作者: Jun Wei

QuRate: power-efficient mobile immersive video streaming

• DOI: 10.1145/3339825.3391863
• 发表时间: 2020-05
• 期刊: Proceedings of the 11th ACM Multimedia Systems Conference
• 作者: Nan Jiang;Yao Liu;Tian Guo;Wenyao Xu;Viswanathan Swaminathan;Lisong Xu;Sheng Wei

MDINFERENCE: Balancing Inference Accuracy and Latency for Mobile Applications

• DOI: 10.1109/ic2e48712.2020.00010
• 发表时间: 2020-02
• 期刊: 2020 IEEE International Conference on Cloud Engineering (IC2E)
• 作者: Samuel S. Ogden;Tian Guo

DistStream: An Order-Aware Distributed Framework for Online-Offline Stream Clustering Algorithms

• DOI: 10.1109/icdcs47774.2020.00075
• 发表时间: 2020-11
• 期刊: 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)
• 作者: Lijie Xu;Xingtong Ye;Kai Kang;Tian Guo;Wensheng Dou;Wei Wang-;Jun Wei

Sync-Switch: Hybrid Parameter Synchronization for Distributed Deep Learning

• DOI: 10.1109/icdcs51616.2021.00057
• 发表时间: 2021-04
• 期刊: 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS)
• 作者: Shijian Li;Oren Mangoubi;Lijie Xu;Tian Guo