Event-Based Scheduling for Energy-Efficient QoS (eQoS) in Mobile Web Applications

Event-Based Scheduling for Energy-Efficient QoS (eQoS) in Mobile Web Applications

Yuhao Zhu, Matthew Halpern, Vijay Janapa Reddi

Department of Electrical and Computer Engineering, The University of Texas at Austin

HPCA’15

Motivation



Prior art only focused on the trad e-off between raw performance and energy consumption.

◦Ignoring the application QoS characte ristic.

◦Raw performance does not directly cor respond to application QoS.

The Interplay between QoS, Perfor

mance, and Energy.

Contribution



Propose eQoS framework for reasoni ng about the QoS-energy trade-off in mobile Web application.



Propose event-based scheduling.



Propose QPE

◦An eQoS metric that quantifies the tr ade-off between QoS and energy consum ption.

eQoS



Energy-efficient QoS.



A new concept that captures the Qo S-energy trade-off.



Provides “just enough” performan ce to meet users’ QoS expectation s with minimal energy consumption.

◦Imperceptibility

◦Usability

Mobile Web Application

 Event-driven

◦Various user interactions, sensor inputs and application internal tasks are transl ated to one or more applications events.

◦Each event is registered with an event ha ndler.

◦FIFO-like event queue.

 A software thread continuously monitors the ev ent queue.

 dequeues any available event from the head of the queue for processing, one event at a time.

Fundamental Event-level Character istics



Event intensity

◦The frequency of events triggered per second.



Event latency

◦The event execution time.

◦The responsiveness to an event.

Event characteristics

Workload Description

Event Imperceptibility (P

) and Usability (P

) Values



Low Event-Intensity, High Event-La tency

◦(P₁, P_U): (1, 10) s



Low Event-Intensity, Low Event-Lat ency

◦(P₁, P_U): (50, 100) ms

 For web browsing, (P₁, P_U): (1, 3) ms



High Event-Intensity, Low Event-La tency

◦(P₁, P_U): (60, 30) FPS

Event-Based Scheduling

Scheduling Unit: event-

handler

Detector



Identifies the P

and P

values fo r an event handler.

◦Based on event latency and event inte nsity information.

◦High latency: latency > 0.8 s

◦High intensity: intensity > 3 times p er second

QoS Monitor



Takes the predictive models, P

an d P

values to determine the archi tecture configuration for executin g a handler.



Monitors event latencies and inten sities on the hardware

◦Adjusts its prediction and scheduling decisions on the fly.

◦Feedback-driven optimizations

Model Constructor



Builds a performance and energy mode l for each event handler.



Performance model:

◦Use the highest and the second-highest f requencies to construct the performance model .



Energy model:

◦Profiling and store in a local power pro file file.

f N

T

/ time

Execution  

Evaluation



QPE

◦QoS per energy

◦QoS Score: utility function between 0

~1.

(QoS_I = 1, QoS_U = 0)

n Consumptio Energy

Score

QPE  QoS

Experimental Setup



Odroid XU+E development board

◦Samsung Exynos 5410 SoC

◦4 big + 4 little



Android 4.2.2

◦Google’s Chromium Web browser 33.0



Embed all interactions into the be nchmarked applications.

◦Ensuring reproducibility

Model Accuracy

Application:

Paper.js

Compare with Other Schedul ers



Four baseline schedulers:

◦Perf-sched

◦Interactive-sched

◦On-demand-sched

◦Energy-sched



Oracle-sched

◦Has a priori knowledge of all event h andler latencies.

◦Always maximizes the QPE score.

Architecture Configuration Dis

tribution for Imperceptibility

Summary of Comparison



Imperceptibility

◦EBS consumes 0.4% more QoS violation than Perf-sched, but saves on average 41.2% power.

◦EBS achieves 22.9% and 37.9% energy s avings over Ondemand-sched and Intera ctive-sched.

 About 0.1% more QoS violation.

◦EBS reduces 72.0% QoS violation compa red to Energy-sched.

Summary of Comparison(Con t.)



Usability

◦EBS achieves 55.4%, 52.9%, and 41.4%

energy savings over Perf-sched, Inter active-sched, and Ondemend-sched, res pectively, with nearly equivalent QoS violations (< 0.1%).

◦Compared to Energy-sched, EBS reduces the QoS violation by about 50%.

Case Study

 EDP vs QPE

 Big.Little Architectu re

◦ beneficial for eQoS op timizations.

 Low-latency, low-intensit y applications (second gr oup) benefit from having a little core.

 Applications in the first and third group benefit f rom having a big core.

Conclusion



Propose eQoS, which serves as a gene ral framework for reasoning about th e energy efficiency trade-off in int eractive mobile Web applications.



Demonstrate a working prototype and conduct real hardware and software m easurements.

◦The event-based scheduling optimizing fo r eQoS achieves 41.2% energy saving with only 0.4% of perceptible QoS violations.