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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716082
• 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。