Highly Scalable Graph Processing

高度可扩展的图形处理

• 批准号: RGPIN-2019-04061
• 负责人: Ozsu, MTamer
• 金额: $ 2.99万
• 依托单位: University of Waterloo
• 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=714121
• 关键词: Highly; Scalable; Graph; Processing

Graph data are of growing importance in many applications, because graphs naturally model complicated structures with explicit representation of the relationships among entities represented by the vertices. One particular type of graph that has increased in importance and poses particular challenges is transactional graphs where each edge represents a transaction between entities represented by the vertices. A common example is a product graph where the vertices represent customers and catalog items and each edge represents a user action (e.g., buying an item, liking an item, putting an item in the shopping cart). Real-time processing of actions and real-time analytics on these graphs is an important problem, and the subject of this discovery grant proposal. These graphs can be very large and very dynamic with edges added at high speed. For example, Alibaba has reported that they maintain graphs with several billion vertices (that represent customers and catalog items) and in excess of 100 billion edges (that represent behaviours such as clicks, adding to shopping cart, orders, etc). These are streaming graphs where (typically) edges are streamed from one or more sources to one or more processing nodes. The streaming rate of these graphs are very high Alibaba reports peak rates of 320k transactions (edges)/sec raising significant processing challenges. The workloads on these graphs are of two types: transactional (e.g., acting on an edge that represents a purchase as soon as the edge arrives what is known as On-line Transaction Processing or OLTP) and analytical (e.g., recommendation based on recent user activity what is usually referred to as On-line Analytical Processing or OLAP). The current state-of-the-art is to handle these two workloads using separate systems, but organizations have expressed a great desire to be able to process both workloads within a single system over a single graph. The long-term objective of my research is to study architectures and techniques to process OLTP and OLAP workloads on very large streaming graphs with very high streaming rates. Within this broad framework, I intend to study the following specific problems: 1. Continuous processing of streaming graphs. The primary objective is to investigate robust system architectures that can process a large number of persistent OLTP queries on large streaming graphs. 2. Windowed graph analytics over streaming graphs. The objective is to investigate mechanism for processing OLAP workloads on streaming graphs, which is difficult because these are iterative requiring multiple passes over data. We will investigate windowed processing techniques to complement continuous processing adopted for OLTP workloads. 3. Hardware assist for graph processing. The objective is to study the use of GPUs and FPGAs in a unified architecture (CPU+GPU+FPGA) to assist high volume OLTP and OLAP processing under high streaming rates.

图数据在许多应用程序中变得越来越重要，因为图自然地用顶点表示的实体之间关系的显式表示来建模复杂的结构。有一种特殊类型的图越来越重要，并提出了特殊的挑战，即事务图，其中每条边表示由顶点表示的实体之间的事务。一个常见的例子是产品图，其中顶点表示客户和目录项，每个边表示用户操作（例如，购买一件商品、喜欢一件商品、将一件商品放入购物车）。动作的实时处理和对这些图形的实时分析是一个重要的问题，也是本发现拨款提案的主题。