Controlling Energy Profile of RT Multiprocessor Systems by Anticipating Workload at Runtime

Transcription

1 Controlling Energy Profile of RT Multiprocessor Systems by Anticipating Workload at Runtime Khurram Bhatti, Muhammad Farooq, Cécile Belleudy, Michel Auguin LEAT Laboratory, University of Nice-Sophia Antipolis, France Research work supported by French national project PHERMA (ANR-06-ARFU06-003) Higher Education Commission (HEC) of Pakistan SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 1

2 Motivation Whatever their origin, all (autonomous) devices share the same Achilles heel: The Battery! Courtesy: Sameera. P SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 2

3 Outline Context Real time Systems & energy-efficient scheduling Dynamic Power Management (DPM) strategies AsDPM Strategy By definition System model and notations ti Working Principle Experiments Simulation environment Results SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 3

4 Context Real time systems Functional correctness depends on Production of desired results Timing of these results Classification Soft real time Hard real time Real time scheduler Real time systems Dynamic Power Management (DPM) Issues in DPM strategies Responsible for providing deadline guarantees Place & execute tasks Knows application characteristics & processing capacity Energy-efficient i scheduling of application DVFS (Dynamic Voltage & Frequency Scaling) DPM (Dynamic Power Management) SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 4

5 Context Real time systems Dynamic Power Management (DPM) Issues in DPM strategies Dynamic Power Management (DPM) Dynamic reconfiguration of system Provide functionality and performance with minimum active components General Assumptions Non-uniform workload Prediction in fluctuations Embodiment Component, System, Network level Realization Timeout policy, hard-wired controller, software routines etc. SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 5

6 Context Real time systems Dynamic Power Management (DPM) Issues in DPM strategies Dynamic Power Management (DPM) Inherent idleness in application s behavior Low-power states P C T Ti Idle d t P C d Ti Mode j penalties t PSM of Intel s Xscale Processor PXA270 SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 6

7 Context Real time systems Dynamic Power Management (DPM) Issues in DPM strategies Issues in DPM strategies State transitions are costly Temporal penalty Energy penalty Inefficiencies in DPM strategies come from the inaccurate prediction Duration of the idle period Arrival of the next request for an idle component What if we do not predict? SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 7

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9 AsDPM Strategy By Definition By definition System model Working principle Non-predictive strategy Arrival of idle time Duration of idle time Not based on a timeout policy No assumptions on probabilistic distribution of idle intervals Based on admission control of tasks Works in conjunction with a global scheduler Extracts fragmented idleness from Application schedule SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 9

10 AsDPM Strategy System Model By definition System model Working principle Application model r i Asynchronous, Periodic & independent tasks B i Preemptive & fully migrating t Architecture Model c Multiprocessor (SMP) platform Identical processors DVFS & DPM support k-power states per processor Notations SSE= Standard Scheduling Event mact = No. of active processors at any time instance AET i C i T i / L L i ui = Ci Ti u sum ( τ ) def = u τ τ i i d i SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 10

11 AsDPM Strategy Working Principle Admission control of tasks Ready tasks Queues TQ: Task Queue ReTQ: Ready Task Queue RuTQ: Running Task Queue DeTQ: Deferred Task Queue By definition System model Working principle TQ ReTQ RuTQ DeTQ τ i τ i + 1 τ n τ i τ i + 1 τ i + 2 j [ i ] 1[ τi 1] [ τ ] π τ π π j+ + j+ 2 i+ 2 τ i+ k π j SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 11

12 AsDPM Strategy By definition System model Working principle Working Principle Fragmented idleness Idle intervals from some k-processors are extracted and clustered on some other (m-k) processors to increase their idle interval length. π 1 SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 12

13 AsDPM Strategy Working Principle By definition System model Working principle Simple Laxity Runtime parameter showing urgency of a task to execution Anticipated Laxity Runtime parameter showing urgency of a task to execution in the presence of all higher priority ready tasks Laxity Bottom Test (LBT): A negative value (<0) for anticipated laxity means this task cannot meet its deadline if executed after all higher priority ready tasks SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 13

14 AsDPM Strategy By definition System model Working principle Working Principle Laxity Bottom Test t10 t12 t16 SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 14

15 AsDPM Strategy By definition System model Working principle Working Principle SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 15

16 AsDPM Strategy By definition System model Working principle Working Principle Task deferring (demo) ReTQ RuTQ ReTQ empty? finish LBT exec LBT LBT T1 T T2 T3 x (on P2) LBT T4 X (on P1) LBT T5 P1 P P2 DeTQ P1 P2 P1 SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 16

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18 Simulation environment Example configuration Simulation traces Simulation results Simulation environment STORM (Simulation TOol for Real-time Multiprocessor scheduling) Integrates multiprocessor architectures with network & memory components Flexible to add customized scheduling policies Supports power & energy estimations Freeware for academic use SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 18

19 Simulation environment Example configuration Simulation traces Simulation results Example Configuration Application task set Six tasks (n=6) Hyper-period = 1200ms Quadruplet values Architecture t u sum def ( τ ) = u = 2.20 τ τ Multiprocessor SMP architecture composed of Intel XScale Processors (PXA270) Scheduling policies Earliest Deadline First (EDF) Least tlaxity First t(llf) State transitions Between S1 (Running) and S4 (Sleep) i i PXA270 energy states* *ValuesatFref=624 MHz and Vdd=1.55Volts SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 19

20 Simulation environment Example configuration Simulation traces Simulation results STORM s simulation traces EDF Schedule EDF-AsDPM Schedule π 1 π 1 π 2 π 2 π π 3 3 π 1 π 1 π 2 π 2 π π 3 3 SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 20

21 Simulation environment Example configuration Simulation traces Simulation results Simulation results Earliest Deadline First (EDF) Energy consumption: 10.40% with WCET 15.86% with AET Performance with WCET of tasks State transitions Performance with AET of tasks 74.85% with WCET 74.53% with AET Occupancy of processors SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 21

22 Simulation environment Example configuration Simulation traces Simulation results Simulation results Least Laxity First (LLF) Energy consumption: 9.70% with WCET 17.58% with AET Performance with WCET of tasks State transitions 59.76% with WCET Performance with AET of tasks 66.35% with AET Occupancy of processors SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 22

23 A non-predictive DPM strategy for multiprocessor systems Can work in conjunction of any yglobal scheduler Optimizes on energy By reducing the no. of processing elements while respect timing constraints By reducing the no. of stat transitions of processing elements By always eliminating the least charged processors (if necessary) which implies less cost of task migration because of lesser cache occupancy Intelligence for suitable mode selection future work! Integration of fasdpm in a complete energy-efficient i scheduling framework with DVFS support future work! SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 23

24 Thanks Questions? SympA 13, Sept 9 11, Toulouse Energy-efficient schedulling 24