CORD: Co-design of Resource Allocation and Deadline Decomposition with Generative Profiling

TL;DR

This paper introduces a generative profiling approach for modeling resource-dependent execution times of DAG-based real-time tasks on multicore systems, enabling more efficient resource allocation and improved schedulability.

Contribution

It presents a novel generative resource profiling algorithm and a co-design method for resource allocation and deadline decomposition tailored for multicore real-time systems.

Findings

Generative profiling achieves high accuracy with limited data.

The co-allocation method significantly improves schedulability.

The approach effectively models time-varying resource use in DAG tasks.

Abstract

As multicore hardware is becoming increasingly common in real-time systems, traditional scheduling techniques that assume a single worst-case execution time for a task are no longer adequate, since they ignore the impact of shared resources on execution time. When tasks execute concurrently on different cores, their execution times often vary substantially with their allocated budgets of shared resources, such as cache and memory bandwidth. Even under a specific resource allocation, the resource use pattern of a task also changes with time during a job execution. It is therefore important to consider the relationship between multicore resources and execution time in task modeling and scheduling algorithm design. In this paper, we propose a much more precise execution model for DAG-based real-time tasks that captures the time-varying resource use characteristics of a task under different budgets of shared resources. We present a generative resource profiling algorithm that efficiently predicts, from limited measurement data, the resource profile of a task at any time during its execution under a given resource budget. The generative profiles can then be used to construct the execution models for tasks, using which one can make informed resource allocation decisions. We further introduce a multicore resource allocation and deadline decomposition co-design technique for DAG-based tasks that leverages the generated execution models to jointly allocate resources and deadlines to subtasks, to maximize resource efficiency and schedulability. Our evaluation results show that our generative profiling algorithm achieves high accuracy while being efficient, and that our co-allocation technique substantially improves schedulability compared to a state-of-the-art deadline decomposition method.