MC-ADAPT: Adaptive Task Dropping under Task-level Mode Switch - - PowerPoint PPT Presentation

MC-ADAPT: Adaptive Task Dropping under Task-level Mode Switch

Jaewoo Lee

Department of Computer and Information Science University of Pennsylvania

Adaptive MC scheduling

• Trends in MC scheduling
• Earlier MC work drops all low-criticality tasks (LO-

tasks) at mode switch

• Recent MC scheduling work provides the degraded

service for LO-task after mode switch

• We consider to drop less LO-tasks
• Our problem
• How to minimize the dropping of LO-tasks?

engine logging MP3 window engine logging MP3 window

Traditional MC scheduling Adaptive MC scheduling An example

• f automotive

systems MC-ADAPT

Approach

• Selective task dropping under a

fine-grained criticality mode

• Existing work adopted system-level

mode switch

• We adopt task-level mode switch
• Drop LO-tasks selectively

L L

HI-task 1

H H L L L H

HI-task 2

MC-ADAPT

High-criticality task

Challenges

• How to drop LO-tasks under task-level mode

switch?

• Although different runtime scenarios need different task

drop, design-time algorithms prepare offline drop decisions for a limited cases due to space complexity

• Need a more flexible task drop decision
• How to evaluate the quality of task dropping

solution?

• Hard to evaluate runtime performance (existing work
• ften evaluates it with random simulation)
• Need a formal characterization (in terms of the speedup

factor)

MC-ADAPT

Task Drop by an Online Test

• Idea: drop LO-tasks by a simple online schedulability test
• Algorithm EDF-AD (Adaptive task Dropping):
• Schedule a HI-task with VD (=

in its LO-mode

• VD 1) reserve time for HI-WCET, 2) bound the demand of LO-tasks at drop
• Drop LO-tasks selectively by EDF-AD online test
• EDF-AD online schedulability test:

: active LO-tasks : LO-mode HI-tasks : dropped LO-tasks : HI-mode HI-tasks

• /
• /

(

· )/

(

· )/

VD coefficient (0<x≤1)

• (VD)

Demand before t* + (runtime util. after t*) MC-ADAPT To be schedulabie, demand in the considered interval ≤ interval length (

)

≤

t* (the rel. time of the last mode switch job)

(4,1,1,L) (6,1,2,H) τ1 τ3 (4,1,1,L) τ2 (6,1,3,H) τ4

active

LO

active dropped

VD:3 VD:3

6 12

Task Drop by an Online Test

x= 0.5

LOHI

By online test, drop τ1

• + ·

+

• +

≤ 1

job (virtual) deadline job release

MC-ADAPT

Schedulability

• EDF-VD [Baruah12]: VD-based scheduling algo. under system-level mode

switch

• EDF-AD: the proposed scheduling algo. under task-level mode switch
• EDF-AD-E: enhanced EDF-AD algo. handling a corner case of EDF-AD

deceasing the offline schedulability MC-ADAPT

The Deadline Miss Ratio of LO-tasks

• Simulation: DMR varying utilization bound
• The probability of mode switch: 0.4
• Each random system is schedulable by EDF-VD
• Result: EDF-AD-E shows up to 42.5% lower DMR than

EDF-VD

The lower value is good MC-ADAPT

Speedup Factor for Task Drop

• How to apply the speedup factor to evaluate the quality of

task dropping?

• Idea: the speedup factor for the task dropping problem
• The minimum speedup s.t. algo. A performs the same as OPT for

any feasible task set and any mode switch sequence

Schedule a given MC task set with a given mode-switch sequences by dropping the minimal # of LO-tasks

• Algo. A sped up by α

OPT

HI-task 1 LO-task LO-task LO-task HI-task 1 LO-task LO-task LO-task

An example task set and its example mode switch sequence (suppose that algo. A has a speedup factor of α ) the min. speedup α (α ≥1) s.t. algo. A performs the same as the optimal algo. for a problem MC-ADAPT

HI-task 2 HI-task 2

The optimal scheduling algo. with the optimal task dropping

Discussion

• Scheduling algorithm for better speedup factor?
• MC-ADAPT (based on EDF-VD) has a speedup factor of

1.618 for task drop

• Speedup factor for task drop is no smaller than 1.333 since the

task dropping problem is a generalization of MC scheduling problem

• To improve the speedup factor, I conjecture that we need

individual VD assignment approach

• Global VD assignment approaches drop more tasks due to its

inefficiency under task level mode switch

• 1.6 is the best speedup factor that current MC-ADAPT can

achieve

• Need online schedulability test (for task dropping) based on

the individual VD assignment

• [Ekberg12] based on demand analysis has pseudopolynomial

complexity, which cannot used in runtime decision

Thank you

Question?

Evaluation: Experimental Setup

• Random task set generation according to [Baruah12]
• Goal
• Evaluate acceptance ratio, the higher the better
• Evaluate Deadline Miss Ratio (DMR), the lower the better
• For each setting, we generate 5,000 random systems and

run 10,000 time units

• Scheduling algorithms
• EDF-VD [Baruah12]: VD-based scheduling algo. under

system-level mode switch

• EDF-AD: the proposed scheduling algo. under task-level

mode switch

MC-ADAPT

Offline Analysis

• Offline schedulability test is derived from the online

schedulability test:

• Schedulability anomaly
• Some task set is schedulable by EDF-VD but not by EDF-AD
• The reason why the anomaly happens
• There are HI-tasks s.t.

/ ≥ although / ≤

• VD coefficient is derived to satisfy the latter inequality

EDF-VD [Baruah12] EDF-AD

When the system starts After mode switch

• +
• +
• +

EDF-AD-E

• The resolution of the schedulability anomaly
• HI-mode-preferred tasks: HI-tasks s.t.
• Do not execute the HI-mode-preferred tasks in LO-mode
• EDF-AD-E scheduling algo.
• Set the initial mode of HI-mode-preferred tasks to HI
• Other rules are the same as EDF-AD
• Offline schedulability analysis

(Enhanced)

EDF-VD EDF-AD EDF-AD-E When the system starts After mode- switch

• +
• ·

+∑

max (

• ,

)

• ≤ 1

·

+ ≤ 1

·

+∑

min (

• ,

)

• ≤ 1