NeTS: Small: Fundamental Techniques for Incentive-aware, Efficient, and Reliable Cloudlet Management and Services

NetS：小型：激励感知、高效且可靠的 Cloudlet 管理和服务的基本技术

• 批准号: 1526843
• 负责人: Xin Wang
• 金额: $ 44.12万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526843&HistoricalAwards=false
• 关键词: NeTS; Small; Fundamental; Techniques; Incentive

The goal of this work is to develop some fundamental techniques that can facilitate efficient management of cloudlet resources for high cloud service performance while minimizing the system cost. These techniques are to enable efficient, reliable and low cost mobile cloud services, which will in turn support high-performance wireless applications in an energy- and resource efficient manner. With this research, a cloud service provider can exploit sparse signal processing technique to remotely monitor one or multiple cloudlet clusters running in different location sites with high accuracy but low cost. To reduce the service initialization delay and operational cost, the system can cache application components, and a user can exploit the proposed rate dependent Bloom filter (RDBF) to quickly determine of some cloudlets have its required application components cached. Rather than being constrained by a limited number of options for virtual machine (VM) configurations, the system will be equipped with multi-dimensional pricing and resource negotiation capabilities for a user to flexibly negotiate multiple types of resources to maximize the benefits of both users and cloudlets while taking into account user application preference, performance requirements, and the system resource conditions. Complementary to the research agenda, the project will carry out a broad range of education and outreach activities and facilitate technology transfer through the industrial partners and industry affiliate programs. The proposed research is part of a global effort in exploiting (mobile) cloud computing for advancing some critical fields for national economy and security, including health-care, transportation, smart-grid, homeland security, and education.

这项工作的目标是开发一些基本技术，以促进云小程序资源的高效管理，以获得高云服务性能，同时将系统成本降至最低。这些技术旨在实现高效、可靠和低成本的移动云服务，进而以节能和资源高效的方式支持高性能无线应用。通过这项研究，云服务提供商可以利用稀疏信号处理技术，以高精度和低成本的方式远程监控运行在不同位置的一个或多个云小程序集群。为了减少服务初始化延迟和运行成本，系统可以缓存应用组件，用户可以利用所提出的速率相关布隆过滤器(RDBF)来快速确定哪些小云缓存了其所需的应用组件。该系统将配备多维定价和资源协商功能，用户可以灵活地协商多种类型的资源，从而在考虑用户应用程序偏好、性能要求和系统资源条件的同时，最大化用户和Cloudlet的利益，而不是受限于有限数量的虚拟机(VM)配置选项。作为对研究议程的补充，该项目将开展广泛的教育和外联活动，并通过行业合作伙伴和行业附属计划促进技术转让。这项拟议的研究是全球努力的一部分，目的是利用(移动)云计算来推进一些对国民经济和安全至关重要的领域，包括医疗保健、交通运输、智能电网、国土安全和教育。