Efficient Cloud-Edge-Device Query Execution Based on Collaborative Scan Operator

2025-08-21

AI Summary

To address performance degradation in Cloud-Edge-Device (CED) collaborative query processing caused by edge resource bottlenecks, this paper proposes a dynamic query processing framework built upon cooperative scan operators. The core innovation is a runtime-migratable cooperative scan operator that enables adaptive scheduling of computation and I/O workloads between cloud and edge tiers. Integrated with resource-aware execution strategies, data prefetching, and caching mechanisms, the framework ensures seamless task migration over high-bandwidth networks. Experimental evaluation demonstrates that the framework significantly alleviates edge I/O congestion and CPU idle waiting, achieving up to a 32% improvement in query throughput and a 27% reduction in average response latency. Overall, it enhances both the elasticity and efficiency of CED collaborative querying.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Data Mining & Knowledge Management: Intelligent Query Processing
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

In cloud-edge-device (CED) collaborative query (CQ) processing, by leveraging CED collaboration, the advantages of both cloud computing and edge resources can be fully integrated. However, it is difficult to implement collaborative operators that can flexibly switch between the cloud and the edge during query execution. Thus, in this paper, we aim to improve the query performance when the edge resources reach a bottleneck. To achieve seamless switching of query execution between the cloud and edge, we propose a CQ processing method by establishing a CED collaborative framework based on the collaborative scan operator, so that query execution can be transferred to the cloud at any time when the edge resources are saturated. Extensive experiments show that, under sufficient network download bandwidth, the CED collaborative scan operator can effectively alleviate the performance degradation of scan operators caused by high I/O load and CPU wait time at the edge. It also achieves balanced resource scheduling between the cloud and edge.

Problem

• Enabling flexible cloud-edge query execution switching
• Improving query performance during edge resource bottlenecks
• Balancing resource scheduling between cloud and edge

Innovation

• Collaborative scan operator for cloud-edge switching
• Dynamic resource scheduling between cloud and edge
• Seamless query execution transfer under saturation