Distributed Systems Support for Processing Big Data from Sensor Networks

分布式系统支持处理来自传感器网络的大数据

• 批准号: RGPIN-2019-06776
• 负责人: Makaroff, Dwight
• 金额: $ 2.04万
• 依托单位: University of Saskatchewan
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749861
• 关键词: Distributed; Systems; Support; Processing; Big

Modern computer systems consist of multiple components that coordinate their input, output and processing to provide timely information to their human or machine users. These components form distributed systems with processing and data transmission possible at every node. One source of system input has been the Internet of Things, with the massive deployment of sensors (including images and video). A typical deployment scenario is the growing market of wearable devices in fitness/health monitoring. Individual devices can generate large amounts of data over short time periods, processed both locally and by a centralized organization. These body-worn devices may or may not have continuous connection to the Internet for appropriate cost-effective data transmission. Delivery protocols are required that prioritize data requirements and are resource-efficient. Some monitoring data must, however, be transferred in real-time. Similar data streams will be generated by home automation and industrial sensors, such as mining, manufacturing, and agriculture. They, too require delivery of large amounts of data at different volumes, rates and latency requirements, constituting streams of big data. When data arrives at a centralized processing location, another problem is created. The total aggregate of data from many sources (individuals, industrial sensors, security cameras and smartphones) overwhelms the capacities of the largest computers available. Parallel processing techniques and frameworks developed by organizations such as Google, Yahoo! and Facebook for Big Data processing have been the preferred solutions for the last dozen years or so. As new frameworks are introduced, their applicability in emerging domains, such as environmental sensor networks and wearable networks requires configuration, tuning and parameter settings. This will require a mixture of manual resource allocation and scheduling policies. The main research goal investigated through this program is to identify and quantify the performance/resource utilization tradeoff in distributed systems consisting of data collection from multiple diverse sources that require aggregate data processing to find common patterns and characteristics. I aim to build techniques to identify configurations that achieve preferred, cost-effective deployments that meet user goals with regards to this tradeoff. Various strategies for reducing resource requirements (such as energy/communication bandwidth/disk space) result in a loss of quality of information or the rate at which the information can be provided. Appropriate adjustments in scheduling, routing or resource allocation between the various components will enable this degradation in service to be mitigated. The outcomes will be methods of evaluating the tradeoffs, case studies using these evaluation methods and development of prototype systems that implement adaptive strategies fo collecting and processing sensor network big data.

现代计算机系统由多个组件组成，这些组件协调输入、输出和处理，向人类或机器用户提供及时的信息。这些组件形成分布式系统，每个节点都可以进行处理和数据传输。系统输入的一个来源是物联网，大量部署传感器（包括图像和视频）。典型的部署场景是健身/健康监测领域的可穿戴设备市场不断增长。单个设备可以在短时间内生成大量数据，这些数据可以在本地处理，也可以由集中组织处理。这些穿戴式设备可以或不可以连续连接到互联网，以实现适当的经济有效的数据传输。交付协议需要优先考虑数据需求并具有资源效率。但是，有些监控数据必须实时传输。类似的数据流将由家庭自动化和工业传感器产生，如采矿、制造业和农业。它们也需要以不同的容量、速率和延迟要求传输大量数据，从而构成大数据流。当数据到达集中处理位置时，另一个问题就产生了。来自许多来源（个人、工业传感器、安全摄像头和智能手机）的数据总量超过了现有最大计算机的容量。并行处理技术和框架由b谷歌、Yahoo！和Facebook的大数据处理在过去十几年里一直是首选的解决方案。随着新框架的引入，它们在新兴领域（如环境传感器网络和可穿戴网络）的适用性需要配置、调整和参数设置。这将需要人工资源分配和调度策略的混合。通过该计划调查的主要研究目标是识别和量化分布式系统中的性能/资源利用权衡，这些系统由来自多个不同来源的数据收集组成，需要进行汇总数据处理以找到共同的模式和特征。我的目标是构建一些技术，以确定哪些配置可以实现首选的、具有成本效益的部署，从而满足用户在这种权衡方面的目标。减少资源需求（如能源/通信带宽/磁盘空间）的各种策略会导致信息质量下降或信息提供速度下降。在调度、路由或各种组件之间的资源分配中进行适当的调整，将使服务中的这种退化得到缓解。结果将是评估权衡的方法，使用这些评估方法的案例研究以及实现自适应策略的原型系统的开发，以收集和处理传感器网络大数据。