发表机构
Pandit Deendayal Energy University; Dhirubhai Ambani University（潘迪特·丁达亚尔能源大学; 迪鲁巴伊·安巴尼大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对边缘查询处理资源有限的问题，提出WACI-HJ方法，通过感知工作负载优化分箱，在基准与智能交通数据集上实现缓存行读取量降54%、查询执行时间提10%，还提升了CPU、RAM、I/O能效，可用于智能交通及其他领域。
AI 中文摘要
大规模边缘系统中的边缘设备数量正在快速增长。边缘设备的处理能力、内存和网络带宽有限，这使得边缘查询处理过程中的资源利用和数据管理面临挑战。连接（Joins）是数据库操作中时间和资源成本最高的操作之一。最先进的边缘查询处理方法是Column Imprint-Hash Join（CI-HJ，列印记哈希连接），它采用等高分箱方法来加速哈希连接，但在实时处理中效率不足，且会扫描不必要的缓存行。本文提出了Workload Aware Column Imprint-Hash Join（WACI-HJ，感知工作负载的列印记哈希连接），该方法采用感知工作负载的思路来加速哈希连接，提前预测即将到来的查询工作负载，进一步提升其对实时边缘查询处理的适用性。WACI-HJ包含两个阶段：WACI-HJ生成阶段，包含预处理、预测、分块与哈希模块，在查询到达前基于预测的工作负载计算分箱；以及查询处理与资源利用阶段，负责处理查询并管理CPU、RAM和I/O资源的利用。在基准数据集和真实世界的智能交通数据集上的评估显示，WACI-HJ的缓存行读取量减少了54%，查询执行时间提升了10%，该技术对缩放数据和倾斜数据均有效。尽管PCR是能耗的间接衡量指标，本文还通过能效实验直接测量了能耗，WACI-HJ在CPU、RAM和I/O方面分别获得了1%、38%和49%的性能提升。优化缓存使用和查询执行速度可加快智能交通中的实时流量分析、拥堵管理和路由，此外该技术还可应用于其他领域以加速边缘查询处理。
英文摘要
The number of edge devices in large-scale edge systems is rapidly increasing. Edge devices have limited processing power, memory, and network bandwidth, making resource utilization and data management during edge query processing challenging. Joins are among the costliest database operations in terms of time and resources. The State-of-the-Art edge query processing, Column Imprint-Hash Join CI-HJ, addresses this challenge using equi-height binning to accelerate hash joins. However, it lacks efficiency in real-time processing and scans unnecessary cachelines. This paper presents Workload Aware Column Imprint-Hash Join WACI-HJ, which uses a workload-aware approach to accelerate hash joins. Predicting the upcoming query workload in advance further improves its suitability for real-time edge query processing. WACI-HJ comprises two phases: WACI-HJ Generation Phase, including Pre-processing, Prediction, and Blocking and Hashing modules to compute bins based on the predicted workload before query arrival, and Query Processing and Resource Utilization, which handles query processing and CPU, RAM, and I/O utilization. Evaluations on a benchmark dataset and a real-world Smart Transportation dataset show a 54% reduction in cachelines read and 10% improved query execution time. The proposed technique is effective for both scaled and skewed data. Although PCR is an indirect measure of energy consumption, the work also directly measures energy consumption through energy-efficiency experiments. WACI-HJ shows 1%, 38%, and 49% gain in CPU, RAM, and I/O, respectively. Optimizing cache usage and query execution speeds up real-time traffic analysis, congestion management, and routing in Smart Transportation. Additionally, this technology can be applied to other domains to accelerate edge query processing.
