Scheduling Periodic Real-Time Tasks with Heterogeneous Reward Requirements

TL;DR

This paper addresses scheduling periodic real-time tasks with heterogeneous reward requirements, proposing conditions for feasibility and designing policies that ensure fairness and optimize reward fulfillment.

Contribution

It introduces a new model for reward-based scheduling, derives feasibility conditions, and develops both offline and online scheduling policies with proven optimality and approximation bounds.

Findings

Feasibility condition for reward-based scheduling is established.

A greedy online policy is proven to be feasibility optimal for equal-period tasks.

An approximation bound is provided for general task periods.

Abstract

We study the problem of scheduling periodic real-time tasks so as to meet their individual minimum reward requirements. A task generates jobs that can be given arbitrary service times before their deadlines. A task then obtains rewards based on the service times received by its jobs. We show that this model is compatible to the imprecise computation models and the increasing reward with increasing service models. In contrast to previous work on these models, which mainly focus on maximize the total reward in the system, we aim to fulfill different reward requirements by different tasks, which offers better fairness and allows fine-grained tradeoff between tasks. We first derive a necessary and sufficient condition for a system, along with reward requirements of tasks, to be feasible. We also obtain an off-line feasibility optimal scheduling policy. We then studies a sufficient condition for a policy to be feasibility optimal or achieves some approximation bound. This condition can serve as a guideline for designing on-line scheduling policy and we obtains a greedy policy based on it. We prove that the on-line policy is feasibility optimal when all tasks have the same periods and also obtain an approximation bound for the policy under general cases.