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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=688266
• 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。******这些图形可以非常大，非常动态，并以高速添加边缘。例如，阿里巴巴报告说，他们维护的图表有数十亿个顶点（代表客户和目录项目）和超过1000亿个边（代表点击、添加购物车、订单等行为）。这些是流图，其中（通常）边从一个或多个源流到一个或多个处理节点。这些图的流速率非常高，阿里巴巴报告的峰值速率为32万次交易（边）/秒，这给处理带来了重大挑战。这些图上的工作负载有两种类型：事务性（例如，在边缘到达时作用于代表购买的边缘，称为在线事务处理或OLTP）和分析性（例如，基于最近用户活动的推荐，通常称为在线分析处理或OLAP）。当前最先进的技术是使用单独的系统处理这两个工作负载，但是组织已经表达了能够在单个图上的单个系统内处理这两个工作负载的强烈愿望。我研究的长期目标是研究在非常大的流图上以非常高的流速率处理OLTP和OLAP工作负载的体系结构和技术。******在这个广泛的框架内，我打算研究以下具体问题：******1。流式图的连续处理。主要目标是研究能够在大型流图上处理大量持久OLTP查询的健壮系统架构。******流式图的窗口图形分析。目标是研究处理流图上的OLAP工作负载的机制，这是困难的，因为这些是迭代的，需要对数据进行多次传递。我们将研究窗口处理技术，以补充OLTP工作负载所采用的连续处理。硬件辅助图形处理。目的是研究GPU和FPGA在统一架构（CPU+GPU+FPGA）中的使用，以协助高流速率下的大容量OLTP和OLAP处理。