ML-Based Dynamic Operator-Level Query Mapping for Stream Processing Systems in Heterogeneous Computing Environments

초록

Mapping queries to optimal computing devices at the operator-level presents a significant challenge in stream processing systems (SPS) with heterogeneous computing resources. Inefficient query mapping can degrade the performance of the SPS. To address this issue, existing approaches employ static methods, such as mapping all queries to either CPUs or GPUs, or maintaining static mapping tables for queries or operators based on their predetermined device preferences. However, the static mapping scheme fails to provide an optimal solution, as the device preference for different query operators changes dynamically at runtime. In this paper, we propose DynO, a high performance SPS that dynamically maps queries to devices at the operator-level using a tree-based machine learning algorithm. To effectively determine an optimized device mapping plan for query operators, DynO employs a tree-based gradient boosting model to accurately predict the execution time for all potential mapping plan combinations. DynO also introduces a novel turn-based updating scheme to maximize performance in stream processing while training a tree-based gradient boosting model. Additionally, we devise an efficient device mapping scheme to expedite the process of determining the optimal device mapping plan by leveraging a direct acyclic graph (DAG) shortest path algorithm. DynO completely hides any overhead caused by the extra computation needed to find the optimal plan by utilizing prefetching and GPU idle periods. Experimental results using a variety of queries and traffic patterns show that DynO outperforms existing state-of-the-art approaches by ensuring high throughput, low latency, and high efficiency. © 2024 IEEE.

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