Dynamic Budget Management with Service Guarantees for Mixed-Criticality Systems

Abstract

Many existing studies on mixed-criticality (MC) scheduling assume that low-criticality budgets for high-criticality applications are known apriori. These budgets are primarily used as guidance to determine when the scheduler should switch the system mode from low to high. Based on this key observation, in this paper we propose a dynamic MC scheduling model under which low-criticality budgets for individual high-criticality applications are determined at runtime as opposed to being fixed offline. To ensure sufficient budget for high-criticality applications at all times, we use offline schedulability analysis to determine a system-wide total low-criticality budget allocation for all the high-criticality applications combined. This total budget is used as guidance in our model to determine the need for a modeswitch. The runtime strategy then distributes this total budget among the various applications depending on their execution requirement and with the objective of postponing mode-switch as much as possible. We show that this runtime strategy is able to postpone mode-switches for a longer time than any strategy that uses a fixed low-criticality budget allocation for each application. Finally, since we are able to control the total budget allocation for high-criticality applications before mode-switch, we also propose techniques to determine these budgets considering system-wide objectives such as schedulability and service guarantee for lowcriticality applications.

Combining Task-level and System-level Scheduling Modes for Mixed Criticality Systems

“…In order to guarantee resources for the HC tasks, many solutions employ a very pessimistic approach that completely discards all the LC tasks upon mode transition [15], [4], [16]. There are some works to delay the dropping of LC tasks by postponing the mode switch instant [38], [20], [23], [33]. Santy et al [39] and Bate et al [7] proposed some techniques to minimize the duration for which the system is in mode HI so that to reduce the non-service duration of LC tasks.…”

“…In this context, a plethora of studies has been carried out to improve the service offered to the LC tasks [3], [20], [28], [41], [40], [43], [5], [31], [36], [30], [18], [19], [25]. These approaches can be classified into four major categories:…”

“…2) Imprecise Computation/Reduced Execution. LC tasks are executed with reduced execution budget when the system is in mode HI [3], [5], [31], [20], [36], [30]. 3) Selective Degradation.…”

Robust Mixed-Criticality Systems

“…Another form of controlled degradation is proposed by Gu and Easwarn [23]. They allow HI-criticality tasks to share a budget, and thereby postpone the time when LO-criticality tasks need to be dropped.…”

Multi-core mixed-criticality real-time scheduling

“…This seriously impacts the performance which may not be suitable for many practical systems that require minimum service guarantees for these tasks [37,50]. To overcome this problem, several techniques have been proposed in the past for single-core [36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54] and multi-core MC systems [55][56][57][58]. These approaches can broadly be classified as follows.…”