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 trade-off between raw performance and energy consumption. ◦ Ignoring the application QoS characteristic. ◦ Raw performance does not directly correspond to application QoS.

The Interplay between QoS, Performance, and Energy.

Contribution Propose eQoS framework for reasoning about the QoS-energy trade-off in mobile Web application. Propose event-based scheduling. Propose QPE ◦ An eQoS metric that quantifies the trade-off between QoS and energy consumption.

eQoS Energy-efficient QoS. A new concept that captures the QoS- energy trade-off. Provides “just enough” performance to meet users’ QoS expectations with minimal energy consumption. ◦ Imperceptibility ◦ Usability

Mobile Web Application Event-driven ◦ Various user interactions, sensor inputs and application internal tasks are translated to one or more applications events. ◦ Each event is registered with an event handler. ◦ FIFO-like event queue.  A software thread continuously monitors the event queue.  dequeues any available event from the head of the queue for processing, one event at a time.

Fundamental Event-level Characteristics 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 I ) and Usability (P U ) Values Low Event-Intensity, High Event-Latency ◦ (P 1, P U ): (1, 10) s Low Event-Intensity, Low Event-Latency ◦ (P 1, P U ): (50, 100) ms  For web browsing, (P 1, P U ): (1, 3) ms High Event-Intensity, Low Event-Latency ◦ (P 1, P U ): (60, 30) FPS

Event-Based Scheduling Scheduling Unit: event-handler

Detector Identifies the P I and P U values for an event handler. ◦ Based on event latency and event intensity information. ◦ High latency: latency > 0.8 s ◦ High intensity: intensity > 3 times per second

QoS Monitor Takes the predictive models, P I and P U values to determine the architecture configuration for executing a handler. Monitors event latencies and intensities on the hardware ◦ Adjusts its prediction and scheduling decisions on the fly. ◦ Feedback-driven optimizations

Model Constructor Builds a performance and energy model for each event handler. Performance model: ◦ Use the highest and the second-highest frequencies to construct the performance model. Energy model: ◦ Profiling and store in a local power profile file.

Evaluation QPE ◦ QoS per energy ◦ QoS Score: utility function between 0~1. (QoS I = 1, QoS U = 0)

Experimental Setup Odroid XU+E development board ◦ Samsung Exynos 5410 SoC ◦ 4 big + 4 little Android ◦ Google’s Chromium Web browser 33.0 Embed all interactions into the benchmarked applications. ◦ Ensuring reproducibility

Model Accuracy Application: Paper.js

Compare with Other Schedulers Four baseline schedulers: ◦ Perf-sched ◦ Interactive-sched ◦ On-demand-sched ◦ Energy-sched Oracle-sched ◦ Has a priori knowledge of all event handler latencies. ◦ Always maximizes the QPE score.

Architecture Configuration Distribution 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 savings over Ondemand-sched and Interactive-sched.  About 0.1% more QoS violation. ◦ EBS reduces 72.0% QoS violation compared to Energy-sched.

Summary of Comparison(Cont.) Usability ◦ EBS achieves 55.4%, 52.9%, and 41.4% energy savings over Perf-sched, Interactive-sched, and Ondemend-sched, respectively, 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 Architecture ◦ beneficial for eQoS optimizations.  Low-latency, low-intensity applications (second group) benefit from having a little core.  Applications in the first and third group benefit from having a big core.

Conclusion Propose eQoS, which serves as a general framework for reasoning about the energy efficiency trade-off in interactive mobile Web applications. Demonstrate a working prototype and conduct real hardware and software measurements. ◦ The event-based scheduling optimizing for eQoS achieves 41.2% energy saving with only 0.4% of perceptible QoS violations.