Dynamic DPU Offloading and Computational Resource Management in Heterogeneous Systems

Abstract

DPU offloading has emerged as a promising way to enhance data processing efficiency and free up host CPU resources. However, unsuitable offloading may overwhelm the hardware and hurt overall system performance. It is still unclear how to make full use of the shared hardware resources and select optimal execution units for each tenant application. In this paper, we propose DORM, a dynamic DPU offloading and resource management architecture for multi-tenant cloud environments with CPU-DPU heterogeneous platforms. The primary goal of DORM is to minimize host resource consumption and maximize request processing efficiency. By establishing a joint optimization model for offloading decision and resource allocation, we abstract the problem into a mixed integer programming mathematical model. To simplify the complexity of model-solving, we decompose the model into two subproblems: a 0-1 integer programming model for offloading decision-making and a convex optimization problem for fine-grained resource allocation. Besides, DORM presents an orchestrator agent to detect load changes and dynamically adjust the scheduling strategy. Experimental results demonstrate that DORM significantly improves resource efficiency, reducing host CPU core usage by up to 83.3%, increasing per-core throughput by up to 4.61x, and lowering the latency by up to 58.5% compared to baseline systems.