Watt-Wise Web: Architecting a Responsive and Energy-Efficient Mobile Web

Guest lecture given at University of Utah on Web browsing

Transcript

1. Watt-Wise Web: Architecting a Responsive and Energy-Efficient Mobile Web Yuhao Zhu http://yuhaozhu.com
4. Snake circa 2000
5. Snake circa 2000 Snake circa 2020
6. Performance
7. Performance Power
8. Measure Power (W) 0 2 4 6 8 Year 2009 2010 2011 2012 2013 2014 2015 Mobile CPU’s Rise to Power [HPCA 2016] 3
9. Measure Power (W) 0 2 4 6 8 Year 2009 2010 2011 2012 2013 2014 2015 0 2 4 6 8 2009 2010 2011 2012 2013 2014 2015 Mobile CPU’s Rise to Power [HPCA 2016] 3
10. Measure Power (W) 0 2 4 6 8 Year 2009 2010 2011 2012 2013 2014 2015 0 2 4 6 8 2009 2010 2011 2012 2013 2014 2015 Mobile CPU’s Rise to Power [HPCA 2016] 3 In pursuit of high performance
11. Measure Power (W) 0 2 4 6 8 Year 2009 2010 2011 2012 2013 2014 2015 0 2 4 6 8 2009 2010 2011 2012 2013 2014 2015 Mobile CPU’s Rise to Power [HPCA 2016] 3 Throttling In pursuit of high performance
12. Measure Power (W) 0 2 4 6 8 Year 2009 2010 2011 2012 2013 2014 2015 0 2 4 6 8 2009 2010 2011 2012 2013 2014 2015 Mobile CPU’s Rise to Power [HPCA 2016] 3 Throttling In pursuit of high performance SoC Thermal Design Power (TDP)
13. 4 Mobile Processor Design “Strategy” Performance Power
14. 4 Mobile Processor Design “Strategy” 2007 (In-order) Performance Power
15. 4 Mobile Processor Design “Strategy” 2007 (In-order) 2011 (Out-of-order) 2012 (Multi-core) 2013 (Asymmetric Multi-core) Performance Power
16. 4 Mobile Processor Design “Strategy” 2007 (In-order) 2011 (Out-of-order) 2012 (Multi-core) 2013 (Asymmetric Multi-core) Performance Power Squeeze 3 decades of desktop CPU techniques into a 6-year span
17. “Improving” Energy Capacity 5 600 smartphone from 2006 to 2014 on http://www.gsmarena.com/makers.php3
18. “Improving” Energy Capacity 5 Screen Size (inches) Battery Capacity (mAh) 600 smartphone from 2006 to 2014 on http://www.gsmarena.com/makers.php3 
       
     
     
     
     
     
     
     
     
     
    
19. “Improving” Energy Capacity 5 Screen Size (inches) Battery Capacity (mAh) 600 smartphone from 2006 to 2014 on http://www.gsmarena.com/makers.php3 
       
     
     
     
     
     
     
     
     
     
    
20. “Improving” Energy Capacity 5 Screen Size (inches) Battery Capacity (mAh) 600 smartphone from 2006 to 2014 on http://www.gsmarena.com/makers.php3 
       
     
     
     
     
     
     
     
     
     
    
21. “Improving” Energy Capacity 6
22. “Improving” Energy Capacity 6
23. “Improving” Energy Capacity 6
24. “Improving” Energy Capacity 6
25. “Improving” Energy Capacity 6
26. Mobile Applications 7
27. Mobile Applications 7 ^ Web
28. Mobile Applications 8 ^ Web Monthly Unique Mobile Users Non-Web Web 3.3 M 8.9 M comScore Mobile Metrix, U.S., June 2015
29. Mobile Applications 8 ^ Web Monthly Unique Mobile Users 2 4 6 8 10 12 Jun
 2014 Sep
 2014 Dec
 2014 Mar
 2015 Jun
 2015 Sep
 2015 Dec
 2015 Mar
 2016 Jun
 2016 Million Web Non-Web comScore 2016 U.S. Mobile App Report
30. Is This (Just) a Network Issue? [IEEE MICRO 2015] 9
31. 38 32 26 20 14 8 2 Load time (s) 10 2 3 4 5 6 7 8 100 2 3 4 5 6 7 8 1000 2 Network RTT (ms) 10 Is This (Just) a Network Issue? [IEEE MICRO 2015]
32. 38 32 26 20 14 8 2 Load time (s) 10 2 3 4 5 6 7 8 100 2 3 4 5 6 7 8 1000 2 Network RTT (ms) 10 LTE 3G Adverse 3G 2G Wi-Fi Is This (Just) a Network Issue? [IEEE MICRO 2015]
33. 38 32 26 20 14 8 2 Load time (s) 10 2 3 4 5 6 7 8 100 2 3 4 5 6 7 8 1000 2 Network RTT (ms) 10 LTE 3G Adverse 3G 2G Wi-Fi Is This (Just) a Network Issue? [IEEE MICRO 2015]
34. 38 32 26 20 14 8 2 Load time (s) 10 2 3 4 5 6 7 8 100 2 3 4 5 6 7 8 1000 2 Network RTT (ms) 10 LTE 3G Adverse 3G 2G Wi-Fi Is This (Just) a Network Issue? [IEEE MICRO 2015]
35. 38 32 26 20 14 8 2 Load time (s) 10 2 3 4 5 6 7 8 100 2 3 4 5 6 7 8 1000 2 Network RTT (ms) 10 Compute LTE 3G Adverse 3G 2G Wi-Fi Is This (Just) a Network Issue? [IEEE MICRO 2015]
36. Measured Power (W) 0.0 3.0 6.0 9.0 2009 2010 2011 2012 2013 2014 2015 Screen Radio CPU 11 Compute Is This (Just) a Network Issue? [IEEE MICRO 2015]
37. 12 Compute Is This (Just) a Network Issue? [IEEE MICRO 2015] Measured Power (W) 0.0 3.0 6.0 9.0 2009 2010 2011 2012 2013 2014 2015 Screen Radio CPU
38. Compute 13 Web Browsing from a Compute Perspective
39. 14 Web Browsing from a Compute Perspective Compute
40. Frameworks and Libraries HTML JavaScript CSS Language Runtime Styling Security Local Storage User Input Layout Render Runtime 15 Web Browsing from a Compute Perspective Compute Architecture Application
41. Runtime 16 Cross-Layer Optimizations Architecture Application
42. Runtime 16 Cross-Layer Optimizations Architecture Application WebCore Web-specific Processor Architecture [ISCA 2014] [TOCS 2017]
43. Runtime 16 Cross-Layer Optimizations Architecture Application WebCore Web-specific Processor Architecture GreenWeb Language Support for Quality-of-Experience [PLDI 2016] [ISCA 2014] [TOCS 2017]
44. Runtime 16 Cross-Layer Optimizations Architecture Application WebCore Web-specific Processor Architecture GreenWeb Language Support for Quality-of-Experience [PLDI 2016] [ISCA 2014] [TOCS 2017]
45. Runtime 16 Cross-Layer Optimizations Architecture Application WebCore Web-specific Processor Architecture GreenWeb Language Support for Quality-of-Experience [PLDI 2016] [ISCA 2014] [TOCS 2017]
46. Runtime 16 Cross-Layer Optimizations Architecture Application WebCore Web-specific Processor Architecture WebRT Fast, Energy-Efficient Mobile Web Runtime GreenWeb Language Support for Quality-of-Experience [PLDI 2016] [ISCA 2014] [TOCS 2017] [HPCA 2013] [HPCA 2015] [ISCA 2019]
47. Runtime 16 Cross-Layer Optimizations Architecture Application WebCore Web-specific Processor Architecture WebRT Fast, Energy-Efficient Mobile Web Runtime GreenWeb Language Support for Quality-of-Experience [PLDI 2016] [ISCA 2014] [TOCS 2017] [HPCA 2013] [HPCA 2015] [ISCA 2019]
48. 17 Mobile Applications are Event-Driven Touching Moving Interactions
49. 17 Mobile Applications are Event-Driven Touching Moving Interactions Events
50. 17 Mobile Applications are Event-Driven Touching Moving Interactions Events click touchstart touchmove scroll
51. 17 Mobile Applications are Event-Driven Touching Moving Interactions Events click touchstart touchmove scroll Event Queue
52. 17 Mobile Applications are Event-Driven Touching Moving Interactions Events click touchstart touchmove scroll Event Loop Event Queue CPU
53. 17 Mobile Applications are Event-Driven Touching Moving Interactions Events click touchstart touchmove scroll Event is the atomic unit of execution; optimize latency/energy at the event-level. Event Loop Event Queue CPU
54. ▸ Observation: Events have different latency slacks that enable energy optimizations 17 Mobile Applications are Event-Driven Touching Moving Interactions Events click touchstart touchmove scroll Event Loop Event Queue CPU
55. ▸ Observation: Events have different latency slacks that enable energy optimizations 18 Event-based Web Runtime Optimization
56. ▸ Observation: Events have different latency slacks that enable energy optimizations 18 ▸ Mechanism: Provide just enough energy to meet QoE requirement for different events Event-based Web Runtime Optimization
57. ▸ Observation: Events have different latency slacks that enable energy optimizations 18 ▸ Mechanism: Provide just enough energy to meet QoE requirement for different events ▸ Implementation: Map events to different heterogeneous hardware configurations Event-based Web Runtime Optimization
58. Event-Level Characterization !19
59. Event-Level Characterization !19
60. Event-Level Characterization !19 150 100 50 0 Event Latency (ms) Events
61. Event-Level Characterization !19 150 100 50 0 Event Latency (ms) Events
62. Event-Level Characterization !19 150 100 50 0 Event Latency (ms) Events keyup
63. Event-Level Characterization !19 150 100 50 0 Event Latency (ms) Events keyup
64. Event-Level Characterization !19 150 100 50 0 Event Latency (ms) Events Large Slack keyup
65. Event-Level Characterization !19 150 100 50 0 Event Latency (ms) Events Large Slack change keyup
66. Event-Level Characterization !19 150 100 50 0 Event Latency (ms) Events Large Slack change Small Slack keyup
67. Event-Level Characterization !19 150 100 50 0 Event Latency (ms) Events Large Slack change Small Slack click keyup
68. Event-Level Characterization !19 150 100 50 0 Event Latency (ms) Events Large Slack change Small Slack No Slack click keyup
69. Event-Level Characterization !19 150 100 50 0 Event Latency (ms) Events Large Slack change Small Slack No Slack click keyup ▸ Wide distribution of event latencies. Events exhibit different slacks. ▹ How to exploit event slacks?
70. !20 Event-based Scheduler (EBS)
71. !20 Event-based Scheduler (EBS) ▸ Goal: For each event, find the most energy-efficient hardware configuration that meets the latency target
72. !20 Event-based Scheduler (EBS) Thread Scheduling
73. !20 Event-based Scheduler (EBS) Thread Scheduling
74. !20 Event-based Scheduler (EBS) Thread-based Scheduler Thread Scheduling
75. !20 Event-based Scheduler (EBS) Thread-based Scheduler Thread Scheduling Throughput Fairness
76. !20 Event-based Scheduler (EBS) Thread-based Scheduler Thread Scheduling Throughput Fairness Events-based Scheduling
77. !20 Event-based Scheduler (EBS) Thread-based Scheduler Thread Scheduling Throughput Fairness Events-based Scheduling Event Queue
78. !20 Event-based Scheduler (EBS) Thread-based Scheduler Thread Scheduling Throughput Fairness Event-based Scheduler Events-based Scheduling Event Queue
79. !20 Event-based Scheduler (EBS) Thread-based Scheduler Thread Scheduling Throughput Fairness Event-based Scheduler Events-based Scheduling Event Latency Event Energy Event Queue
80. !21 Leveraging Heterogeneous Hardware ▸ Offer a large performance-energy trade-off space ▸ Widely used in commodity devices
81. !22 Leveraging Heterogeneous Hardware ▸ Offer a large performance-energy trade-off space ▸ Widely used in commodity devices Energy Consumption Performance Big Core Small Core
82. !22 Leveraging Heterogeneous Hardware ▸ Offer a large performance-energy trade-off space ▸ Widely used in commodity devices Energy Consumption Performance Big Core Small Core Voltage/ Frequency Levels
83. !22 Leveraging Heterogeneous Hardware ▸ Offer a large performance-energy trade-off space ▸ Widely used in commodity devices Energy Consumption Performance Big Core Small Core
84. !22 Leveraging Heterogeneous Hardware ▸ Offer a large performance-energy trade-off space ▸ Widely used in commodity devices Energy Consumption Performance Big Core Small Core
85. !23 Leveraging Heterogeneous Hardware ▸ Offer a large performance-energy trade-off space Memory Operation CPU Operation Tmemory Ndependent f Event Latency Xie, et al., Compile-Time Dynamic Voltage Scaling Settings: Opportunities and Limits, PLDI’03
86. !23 Leveraging Heterogeneous Hardware ▸ Offer a large performance-energy trade-off space Memory Operation CPU Operation Tmemory Ndependent f Event Latency Xie, et al., Compile-Time Dynamic Voltage Scaling Settings: Opportunities and Limits, PLDI’03 Event Latency =
87. !23 Leveraging Heterogeneous Hardware ▸ Offer a large performance-energy trade-off space Memory Operation CPU Operation Tmemory Ndependent f Event Latency Xie, et al., Compile-Time Dynamic Voltage Scaling Settings: Opportunities and Limits, PLDI’03 Event Latency = Tmemory +
88. !23 Leveraging Heterogeneous Hardware ▸ Offer a large performance-energy trade-off space Memory Operation CPU Operation Tmemory Ndependent f Event Latency Xie, et al., Compile-Time Dynamic Voltage Scaling Settings: Opportunities and Limits, PLDI’03 Event Latency = Tmemory + Ndependent / f
89. !23 Leveraging Heterogeneous Hardware ▸ Offer a large performance-energy trade-off space Memory Operation CPU Operation Tmemory Ndependent f Event Latency Xie, et al., Compile-Time Dynamic Voltage Scaling Settings: Opportunities and Limits, PLDI’03 Event Latency = Tmemory + Ndependent / f
90. Evaluation Methodology ▸ Implemented inside Google Chromium Web browser ▸ Representative hardware platform ▹ Exynos 5410 SoC (A15 + A7) ▸UI-level record and replay for reproducibility. ▹ Mosaic [ISPASS 2015] https://github.com/Matthalp/mosaic 24
91. 25 Evaluation Results 0 0.2 0.4 0.6 0.8 1 1.2 Norm. Energy 0 0.2 0.4 0.6 0.8 1 Perf
92. 26 Evaluation Results 0 0.2 0.4 0.6 0.8 1 1.2 Norm. Energy 0 0.2 0.4 0.6 0.8 1 Perf 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 Android
93. 0 0.2 0.4 0.6 0.8 1 1.2 Norm. Energy 0 0.2 0.4 0.6 0.8 1 Perf 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 Android 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 EBS 27 Evaluation Results 29.2% Energy Savings
94. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 Android 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 EBS 0 0.2 0.4 0.6 0.8 1 1.2 Norm. Energy 0 0.2 0.4 0.6 0.8 1 Perf 28 Evaluation Results Norm. QoS Violation 0 0.2 0.4 0.6 0.8 1 1.2 Norm. Energy 0 0.2 0.4 0.6 0.8 1
95. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 Android 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 EBS 0 0.2 0.4 0.6 0.8 1 1.2 Norm. Energy 0 0.2 0.4 0.6 0.8 1 Perf Norm. QoS Violation 0 0.2 0.4 0.6 0.8 1 1.2 Norm. Energy 0 0.2 0.4 0.6 0.8 1 Norm. QoS Violation 0 0.2 0.4 0.6 0.8 1 1.2 Norm. Energy 0 0.2 0.4 0.6 0.8 1 29 Evaluation Results
96. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 Android 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 EBS 0 0.2 0.4 0.6 0.8 1 1.2 Norm. Energy 0 0.2 0.4 0.6 0.8 1 Perf Norm. QoS Violation 0 0.2 0.4 0.6 0.8 1 1.2 Norm. Energy 0 0.2 0.4 0.6 0.8 1 Norm. QoS Violation 0 0.2 0.4 0.6 0.8 1 1.2 Norm. Energy 0 0.2 0.4 0.6 0.8 1 Norm. QoS Violation 0 0.2 0.4 0.6 0.8 1 1.2 Norm. Energy 0 0.2 0.4 0.6 0.8 1 30 Evaluation Results 0.8% More QoS violations
97. Rethink Event-based Scheduling 31 Event-based Scheduler Event Latency Event Energy Event Queue
98. Rethink Event-based Scheduling 31 Event-based Scheduler Event Latency Event Energy Pending Events Event Queue
99. Rethink Event-based Scheduling 31 Event-based Scheduler Event Latency Event Energy Pending Events onclick GC task Timer event Event Queue Time Event Queue Pending Events
100. Rethink Event-based Scheduling 31 Event-based Scheduler Event Latency Event Energy Reactive Strategy Pending Events onclick GC task Timer event Event Queue Time Event Queue Pending Events
101. Rethink Event-based Scheduling 31 Event-based Scheduler Event Latency Event Energy Reactive Strategy Pending Events onclick GC task Timer event ontouchmove onsubmit Event Queue Time Event Queue Pending Events
102. Rethink Event-based Scheduling 31 Event-based Scheduler Event Latency Event Energy Proactive Strategy Pending & Speculative Events onclick GC task Timer event ontouchmove onsubmit Event Queue Time Event Queue Pending Events Speculative Events
103. Inefficiency of Current Schedulers 32
104. Inefficiency of Current Schedulers 32 Time input 1 input 2 QoS Deadline 1 QoS Deadline 2
105. Inefficiency of Current Schedulers 32 Time input 1 input 2 QoS Deadline 1 QoS Deadline 2 E1 OS
106. Inefficiency of Current Schedulers 32 Time input 1 input 2 QoS Deadline 1 QoS Deadline 2 E1 slack OS
107. Inefficiency of Current Schedulers 32 Time input 1 input 2 QoS Deadline 1 QoS Deadline 2 E1 E2 slack OS
108. Inefficiency of Current Schedulers 33 Time input 1 input 2 QoS Deadline 1 QoS Deadline 2 E1 E2 EBS OS
109. Inefficiency of Current Schedulers 33 Time input 1 input 2 QoS Deadline 1 QoS Deadline 2 E1 E2 E1 EBS OS
110. Inefficiency of Current Schedulers 33 Time input 1 input 2 QoS Deadline 1 QoS Deadline 2 E1 E2 E1 E2 EBS OS
111. Inefficiency of Current Schedulers 33 Time input 1 input 2 QoS Deadline 1 QoS Deadline 2 E1 E2 E1 E2 ? EBS OS
112. Inefficiency of Current Schedulers 34 Time Oracle input 1 input 2 QoS Deadline 1 QoS Deadline 2 E1 E2 E1 E2 EBS OS
113. Inefficiency of Current Schedulers 34 Time Oracle input 1 input 2 QoS Deadline 1 QoS Deadline 2 E1 E2 E1 E2 E1 E2 EBS OS
114. Inefficiency of Current Schedulers 35 Time input 1 input 2 QoS Deadline 1 QoS Deadline 2 E1 E2 Schedule across both pending and future events Oracle
115. PES: Proactive Event Scheduler 36
116. PES: Proactive Event Scheduler 36 Prediction Web Program Analysis Machine Learning
117. PES: Proactive Event Scheduler 36 Prediction Web Program Analysis Machine Learning Scheduling Constrained Optimization
118. PES: Proactive Event Scheduler 36 Prediction Web Program Analysis Machine Learning Scheduling Constrained Optimization
119. Event Sequence Learning 37 Prediction Model
120. Event Sequence Learning 37 Time E1 E2 E3 Event Sequence Prediction Model
121. Event Sequence Learning 37 Time E1 E2 E3 Event Sequence Prediction Model
122. Event Sequence Learning 37 Time E1 E2 E3 Event Sequence E4 Prediction Model
123. Event Sequence Learning 37 Time E1 E2 E3 Event Sequence E4 Prediction Model
124. Recurrent Prediction 38 Time E1 E2 E3 E4 Event Sequence Prediction Model
125. Recurrent Prediction 38 Time E1 E2 E3 E4 Event Sequence Prediction Model
126. Recurrent Prediction 38 Time E1 E2 E3 E4 Event Sequence E5 Prediction Model
127. Recurrent Prediction 38 Time E1 E2 E3 E4 Event Sequence E5 Prediction Model
128. Recurrent Prediction 38 Time E1 E2 E3 E4 Event Sequence E5 … Prediction Model
129. Prediction Model 39 Prediction Model
130. Prediction Model 40 Prediction Model The distance of click The number of scrolls The number of navigations Features encoding past interactions …
131. Prediction Model 41 Features encoding past interactions ln( p 1 − p ) = x β Prediction Model
132. Prediction Model 42 Features encoding past interactions Click ScrollUp ScrollDown ZoomIn ZoomOut … Prediction Model 0.10 0.12 0.58 0.07 0.02
133. Prediction Model 42 Features encoding past interactions Click ScrollUp ScrollDown ZoomIn ZoomOut … Prediction Model ✔
134. Prediction Model 42 Features encoding past interactions Click ScrollUp ScrollDown ZoomIn ZoomOut … Prediction Model All Event Types
135. Prediction Model 42 Features encoding past interactions Click ScrollUp ScrollDown ZoomIn ZoomOut … Prediction Model Filter Unlikely Events!
136. 43 Program State Analysis
137. 43 Program State Analysis
138. 43 <collapsible> <href> <html> <body> <div> … <div> <div> <href> <href> … Program State Analysis DOM Tree
139. 43 <collapsible> <href> <html> <body> <div> … <div> <div> <href> <href> … Program State Analysis DOM Tree Viewport
140. 43 <collapsible> <href> <html> <body> <div> … <div> <div> <href> <href> … Viewport Program State Analysis DOM Tree Viewport
141. 43 <collapsible> <href> <html> <body> <div> … <div> <div> <href> <href> … Viewport Program State Analysis DOM Tree Viewport
142. 43 <collapsible> <href> <html> <body> <div> … <div> <div> <href> <href> … Viewport Program State Analysis DOM Tree Viewport
143. 43 <collapsible> <href> <html> <body> <div> … <div> <div> <href> <href> … Viewport Program State Analysis DOM Tree Viewport
144. Integrating Program States 44 Features encoding past interactions Click ScrollUp ScrollDown ZoomIn ZoomOut … Prediction Model
145. Integrating Program States 45 Features encoding past interactions Click ScrollUp ScrollDown ZoomIn ZoomOut … Current
 application states + + Prediction Model
146. Overview of the Predictor 46 Click ScrollUp ScrollDown ZoomIn ZoomOut … + Prediction Model ~10 us Features encoding past interactions Current
 application states +
147. Overview of the Predictor 46 Click ScrollUp ScrollDown ZoomIn ZoomOut … + Prediction Model Stop until the cumulative confidence of the predicted event sequence is below a threshold ~10 us Features encoding past interactions Current
 application states +
148. PES: Proactive Event Scheduler 47 Prediction Web Program Analysis Machine Learning Scheduling Constrained Optimization
149. Constrained Optimization Formulation 48 E1 E2 E3 Time ▸ Goal: minimize total energy while meeting deadlines
150. Objective: Constrained Optimization Formulation 48 E1 E2 E3 Time ▸ Goal: minimize total energy while meeting deadlines N ∑ i Min. Energy (i)
151. Objective: Constrained Optimization Formulation 48 E1 E2 E3 Time △Texe 1 △Texe 2 △Texe 3 ▸ Goal: minimize total energy while meeting deadlines N ∑ i △Texe (i) x Min.
152. Objective: Constrained Optimization Formulation 48 E1 E2 E3 Time △Texe 1 △Texe 2 △Texe 3 ▸ Goal: minimize total energy while meeting deadlines N ∑ i △Texe (i) x Power (i) Min.
153. Objective: Constraints: Constrained Optimization Formulation 48 E1 E2 E3 Time △Texe 1 △Texe 2 △Texe 3 ▸ Goal: minimize total energy while meeting deadlines N ∑ i △Texe (i) x Power (i) Min.
154. Objective: Constraints: Constrained Optimization Formulation 48 E1 E2 E3 Time △Texe 1 △Texe 2 △Texe 3 ▸ Goal: minimize total energy while meeting deadlines Order: ≤ Tend (i) Tstart (i+1) N ∑ i △Texe (i) x Power (i) Min.
155. Objective: Constraints: Constrained Optimization Formulation 48 E1 E2 E3 Time Tstart 1 Tstart 2 Tstart 3 TQoS 1 TQoS 2 TQoS 3 △Texe 1 △Texe 2 △Texe 3 ▸ Goal: minimize total energy while meeting deadlines Order: ≤ Tend (i) Tstart (i+1) Deadline: ≤ Tstart (i) △Texe (i) TQoS (i) + N ∑ i △Texe (i) x Power (i) Min.
156. Objective: Constraints: Constrained Optimization Formulation 48 E1 E2 E3 Time Tstart 1 Tstart 2 Tstart 3 TQoS 1 TQoS 2 TQoS 3 △Texe 1 △Texe 2 △Texe 3 ▸ Goal: minimize total energy while meeting deadlines Order: ≤ Tend (i) Tstart (i+1) Deadline: ≤ Tstart (i) △Texe (i) TQoS (i) + Scheduling knobs: Big/little + DVFS for each event. N ∑ i △Texe (i) x Power (i) Min.
157. Each Event: Objective: Constraints: Problem Formulation 49 ▸ Scheduling Problem → Constrained Optimization. N ∑ i △Texe (i) x Power (i) Min. △Texe (i) = Order: ≤ Tend (i) Tstart (i+1) Deadline: ≤ Tstart (i) △Texe (i) TQoS (i) +
158. Each Event: Objective: Constraints: Problem Formulation 49 ▸ Scheduling Problem → Constrained Optimization. N ∑ i △Texe (i) x Power (i) Min. △Texe (i) = Tmemory + Order: ≤ Tend (i) Tstart (i+1) Deadline: ≤ Tstart (i) △Texe (i) TQoS (i) +
159. Each Event: Objective: Constraints: Problem Formulation 49 ▸ Scheduling Problem → Constrained Optimization. N ∑ i △Texe (i) x Power (i) Min. Tcpu △Texe (i) = Tmemory + Order: ≤ Tend (i) Tstart (i+1) Deadline: ≤ Tstart (i) △Texe (i) TQoS (i) +
160. Each Event: Objective: Constraints: Problem Formulation 49 ▸ Scheduling Problem → Constrained Optimization. N ∑ i △Texe (i) x Power (i) Min. △Texe (i) = Tmemory + Ncycles / f (i) Order: ≤ Tend (i) Tstart (i+1) Deadline: ≤ Tstart (i) △Texe (i) TQoS (i) +
161. Each Event: Objective: Constraints: Problem Formulation 49 ▸ Scheduling Problem → Constrained Optimization. N ∑ i △Texe (i) x Power (i) Min. △Texe (i) = Tmemory + Constants Ncycles / f (i) Order: ≤ Tend (i) Tstart (i+1) Deadline: ≤ Tstart (i) △Texe (i) TQoS (i) +
162. Each Event: Objective: Constraints: Problem Formulation 49 ▸ Scheduling Problem → Constrained Optimization. N ∑ i △Texe (i) x Min. △Texe (i) = Tmemory + Constants Ncycles / f (i) Order: ≤ Tend (i) Tstart (i+1) Deadline: ≤ Tstart (i) △Texe (i) TQoS (i) + Pmap (i) Pmap (i) = Freq2Power (f(i))
163. Each Event: Objective: Constraints: Problem Formulation 49 ▸ Scheduling Problem → Constrained Optimization. N ∑ i △Texe (i) x Min. △Texe (i) = Tmemory + Constants Offline profile Ncycles / f (i) Order: ≤ Tend (i) Tstart (i+1) Deadline: ≤ Tstart (i) △Texe (i) TQoS (i) + Pmap (i) Pmap (i) = Freq2Power (f(i))
164. Each Event: Objective: Constraints: Problem Formulation 50 ▸ Scheduling Problem → Constrained Optimization. N ∑ i △Texe (i) x Pmap (i) Min. Order: ≤ Tend (i) Tstart (i+1) Deadline: ≤ Tstart (i) △Texe (i) TQoS (i) +
165. Each Event: Objective: Constraints: Problem Formulation 50 ▸ Scheduling Problem → Constrained Optimization. N ∑ i △Texe (i) x Pmap (i) Min. Order: ≤ Tend (i) Tstart (i+1) Deadline: ≤ Tstart (i) △Texe (i) TQoS (i) + { Ncycles / f (i, j) } △Texe (i) =Tmemory +M ∑ j=0 * ⍺ (i, j) ⍺ (i, j) in {0,1}
166. Each Event: Objective: Constraints: Problem Formulation 50 ▸ Scheduling Problem → Constrained Optimization. N ∑ i △Texe (i) x Pmap (i) Min. Order: ≤ Tend (i) Tstart (i+1) Deadline: ≤ Tstart (i) △Texe (i) TQoS (i) + { Ncycles / f (i, j) } △Texe (i) =Tmemory +M ∑ j=0 * ⍺ (i, j) ⍺ (i, j) in {0,1} Pmap (i) = Freq2Power ( f(i, j) ) * ⍺ (i, j) M ∑ j=0
167. Each Event: Objective: Constraints: Problem Formulation 50 ▸ Scheduling Problem → Constrained Optimization. N ∑ i △Texe (i) x Pmap (i) Min. Order: ≤ Tend (i) Tstart (i+1) Deadline: ≤ Tstart (i) △Texe (i) TQoS (i) + { Ncycles / f (i, j) } △Texe (i) =Tmemory +M ∑ j=0 * ⍺ (i, j) ⍺ (i, j) in {0,1} Pmap (i) = Freq2Power ( f(i, j) ) * ⍺ (i, j) M ∑ j=0 1 = ⍺ (i, j) With the constraint: M ∑ j=0
168. Each Event: Objective: Constraints: Problem Formulation 50 ▸ Scheduling Problem → Constrained Optimization. N ∑ i △Texe (i) x Pmap (i) Min. Order: ≤ Tend (i) Tstart (i+1) Deadline: ≤ Tstart (i) △Texe (i) TQoS (i) + { Ncycles / f (i, j) } △Texe (i) =Tmemory +M ∑ j=0 * ⍺ (i, j) ⍺ (i, j) in {0,1} Pmap (i) = Freq2Power ( f(i, j) ) * ⍺ (i, j) M ∑ j=0 1 = ⍺ (i, j) With the constraint: M ∑ j=0 Only one DVFS setting is chosen for each event
169. Each Event: Objective: Constraints: Problem Formulation 50 ▸ Scheduling Problem → Constrained Optimization. N ∑ i △Texe (i) x Pmap (i) Min. Order: ≤ Tend (i) Tstart (i+1) Deadline: ≤ Tstart (i) △Texe (i) TQoS (i) + { Ncycles / f (i, j) } △Texe (i) =Tmemory +M ∑ j=0 * ⍺ (i, j) Pmap (i) = Freq2Power ( f(i, j) ) * ⍺ (i, j) M ∑ j=0 1 = ⍺ (i, j) With the constraint: M ∑ j=0
170. Each Event: Objective: Constraints: Problem Formulation 50 ▸ Scheduling Problem → Constrained Optimization. N ∑ i △Texe (i) x Pmap (i) Min. Order: ≤ Tend (i) Tstart (i+1) Deadline: ≤ Tstart (i) △Texe (i) TQoS (i) + { Ncycles / f (i, j) } △Texe (i) =Tmemory +M ∑ j=0 * ⍺ (i, j) Integer Linear Programing! Pmap (i) = Freq2Power ( f(i, j) ) * ⍺ (i, j) M ∑ j=0 1 = ⍺ (i, j) With the constraint: M ∑ j=0
171. Overhead: Each Event: Objective: Constraints: Problem Formulation 50 ▸ Scheduling Problem → Constrained Optimization. N ∑ i △Texe (i) x Pmap (i) Min. Order: ≤ Tend (i) Tstart (i+1) Deadline: ≤ Tstart (i) △Texe (i) TQoS (i) + { Ncycles / f (i, j) } △Texe (i) =Tmemory +M ∑ j=0 * ⍺ (i, j) Integer Linear Programing! Pmap (i) = Freq2Power ( f(i, j) ) * ⍺ (i, j) M ∑ j=0 1 = ⍺ (i, j) With the constraint: M ∑ j=0
172. Overhead: 10 DVFS configurations Each Event: Objective: Constraints: Problem Formulation 50 ▸ Scheduling Problem → Constrained Optimization. N ∑ i △Texe (i) x Pmap (i) Min. Order: ≤ Tend (i) Tstart (i+1) Deadline: ≤ Tstart (i) △Texe (i) TQoS (i) + { Ncycles / f (i, j) } △Texe (i) =Tmemory +M ∑ j=0 * ⍺ (i, j) Integer Linear Programing! Pmap (i) = Freq2Power ( f(i, j) ) * ⍺ (i, j) M ∑ j=0 1 = ⍺ (i, j) With the constraint: M ∑ j=0
173. Overhead: 10 DVFS configurations 8 events look ahead Each Event: Objective: Constraints: Problem Formulation 50 ▸ Scheduling Problem → Constrained Optimization. N ∑ i △Texe (i) x Pmap (i) Min. Order: ≤ Tend (i) Tstart (i+1) Deadline: ≤ Tstart (i) △Texe (i) TQoS (i) + { Ncycles / f (i, j) } △Texe (i) =Tmemory +M ∑ j=0 * ⍺ (i, j) Integer Linear Programing! Pmap (i) = Freq2Power ( f(i, j) ) * ⍺ (i, j) M ∑ j=0 1 = ⍺ (i, j) With the constraint: M ∑ j=0
174. Overhead: 80 variables in ILP Each Event: Objective: Constraints: Problem Formulation 50 ▸ Scheduling Problem → Constrained Optimization. N ∑ i △Texe (i) x Pmap (i) Min. Order: ≤ Tend (i) Tstart (i+1) Deadline: ≤ Tstart (i) △Texe (i) TQoS (i) + { Ncycles / f (i, j) } △Texe (i) =Tmemory +M ∑ j=0 * ⍺ (i, j) Integer Linear Programing! Pmap (i) = Freq2Power ( f(i, j) ) * ⍺ (i, j) M ∑ j=0 1 = ⍺ (i, j) With the constraint: M ∑ j=0
175. Overhead: Runtime overhead: 10ms 80 variables in ILP Each Event: Objective: Constraints: Problem Formulation 50 ▸ Scheduling Problem → Constrained Optimization. N ∑ i △Texe (i) x Pmap (i) Min. Order: ≤ Tend (i) Tstart (i+1) Deadline: ≤ Tstart (i) △Texe (i) TQoS (i) + { Ncycles / f (i, j) } △Texe (i) =Tmemory +M ∑ j=0 * ⍺ (i, j) Integer Linear Programing! Pmap (i) = Freq2Power ( f(i, j) ) * ⍺ (i, j) M ∑ j=0 1 = ⍺ (i, j) With the constraint: M ∑ j=0 Dynamic programing
176. Putting Things Together 51 Web Application Rendering Engine Time
177. PES Putting Things Together 51 Web Application Rendering Engine Time
178. PES Putting Things Together 51 Web Application Rendering Engine Predictor Events Time Prediction
179. PES Putting Things Together 51 Web Application Rendering Engine Predictor Events Scheduler Predictions Time Prediction
180. PES Putting Things Together 51 Web Application Rendering Engine Predictor Events Speculative Schedules Scheduler Predictions Time Prediction Schedule
181. PES Putting Things Together 51 Web Application Rendering Engine Predictor Events Speculative Schedules Scheduler Predictions Time Prediction Schedule F1 Pending Frame Buffer F2 F3
182. PES Putting Things Together 51 Web Application Rendering Engine Predictor Controller Events Speculative Schedules Pending Frames Scheduler Predictions Time Prediction Schedule F1 Pending Frame Buffer F2 F3
183. PES Putting Things Together 51 Web Application Rendering Engine Predictor Controller Events Speculative Schedules Pending Frames Scheduler Predictions Time Prediction Schedule F1 Pending Frame Buffer F2 F3
184. PES Putting Things Together 51 Web Application Rendering Engine Predictor Controller Events Speculative Schedules Pending Frames Scheduler Predictions Commit Time Prediction Schedule F1 Pending Frame Buffer F2 F3 ✔ ✔
185. PES Putting Things Together 51 Web Application Rendering Engine Predictor Controller Events Speculative Schedules Pending Frames Recover Scheduler Predictions Commit Time Prediction Schedule F1 Pending Frame Buffer F2 F3 ✔ ✔ ✘
186. PES Putting Things Together 51 Web Application Rendering Engine Predictor Controller Events Speculative Schedules Pending Frames Recover Scheduler Predictions Commit Time Prediction Schedule F1 Pending Frame Buffer F2 F3 ✔ ✔ ✘ F3 ✔
187. Evaluation 52 52 ▸Traces ▹Training: 10 users, more than 100 traces. ▹Testing: 36 traces, 3 for each Web application. ▸Baseline Mechanisms ▹Interactive governor (Interactive) — Android default ▹EBS: a state-of-the-art reactive event-based scheduler ▹Oracle: optimal scheduler ▸Metrics ▹Energy consumption ▹QoS violation
188. High Prediction Accuracy, Low Mis-Prediction Penalty 53 Prediction Accuracy (%) 60 70 80 90 100 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter
189. High Prediction Accuracy, Low Mis-Prediction Penalty 53 Prediction Accuracy (%) 60 70 80 90 100 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter Mis-prediction Waste (ms) 0 10 20 30 40 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter
190. High Prediction Accuracy, Low Mis-Prediction Penalty 53 Prediction Accuracy (%) 60 70 80 90 100 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter Mis-prediction Waste (ms) 0 10 20 30 40 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter
191. High Prediction Accuracy, Low Mis-Prediction Penalty 53 Prediction Accuracy (%) 60 70 80 90 100 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter Mis-prediction Waste (ms) 0 10 20 30 40 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter 92% Prediction Accuracy and 20 ms of Mis-Prediction Waste
192. Experimental Result 54
193. Experimental Result 54 Norn. Energy 0 0.25 0.5 0.75 1 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter Interactive EBS PES Oracle
194. Experimental Result 54 Norn. Energy 0 0.25 0.5 0.75 1 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter Interactive EBS PES Oracle
195. Experimental Result 54 Norn. Energy 0 0.25 0.5 0.75 1 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter Interactive EBS PES Oracle
196. Experimental Result 54 Norn. Energy 0 0.25 0.5 0.75 1 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter Interactive EBS PES Oracle
197. Experimental Result 54 Norn. Energy 0 0.25 0.5 0.75 1 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter Interactive EBS PES Oracle
198. Experimental Result 54 Norn. Energy 0 0.25 0.5 0.75 1 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter Interactive EBS PES Oracle QoS Violation 0 0.15 0.3 0.45 0.6 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter Interactive EBS PES Oracle
199. Experimental Result 54 Norn. Energy 0 0.25 0.5 0.75 1 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter Interactive EBS PES Oracle QoS Violation 0 0.15 0.3 0.45 0.6 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter Interactive EBS PES Oracle
200. Experimental Result 54 Norn. Energy 0 0.25 0.5 0.75 1 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter Interactive EBS PES Oracle QoS Violation 0 0.15 0.3 0.45 0.6 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter Interactive EBS PES Oracle
201. Experimental Result 54 Norn. Energy 0 0.25 0.5 0.75 1 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter Interactive EBS PES Oracle QoS Violation 0 0.15 0.3 0.45 0.6 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter Interactive EBS PES Oracle
202. Experimental Result 54 Norn. Energy 0 0.25 0.5 0.75 1 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter Interactive EBS PES Oracle QoS Violation 0 0.15 0.3 0.45 0.6 163 msn slashdot youtube google amazon ebay sina espn bbc cnn twitter Interactive EBS PES Oracle 61% less QoS Violation and 26% of Energy Reduction
203. Event-driven Processing is a Fundamental and Prevalent Paradigm Edge Computing Database Crowdsourced Sensor Network Mobile Applications Virtual Reality Internet-of-things Cloud Computing
204. What’s Next?
205. “Microkernel”-based Browser ▸ A web app/website exercises only a small portion of the Web specification. There are “hot” tags, CSS properties, etc. ▸ Design a minimalistic browser core, and load the rest only when requested by a specific webpage. 57 https://www.advancedwebranking.com/html/ https://maqentaer.com/devopera-static-backup/http/dev.opera.com/articles/view/mama-css-syntax/index.html
206. User-Centric Language (Extensions) ▸ Web performance is a comprehensive user-centric objective that can be not captured by one single metric. 58
207. User-Centric Language (Extensions) ▸ Web performance is a comprehensive user-centric objective that can be not captured by one single metric. ▹ Old days: time when unload is fired, doesn’t corresponds to UX 58
208. User-Centric Language (Extensions) ▸ Web performance is a comprehensive user-centric objective that can be not captured by one single metric. ▹ Old days: time when unload is fired, doesn’t corresponds to UX ▹ Now: First Meaningful Paint (FMP), Time to Interactive (TTI), etc. 58
209. User-Centric Language (Extensions) ▸ Web performance is a comprehensive user-centric objective that can be not captured by one single metric. ▹ Old days: time when unload is fired, doesn’t corresponds to UX ▹ Now: First Meaningful Paint (FMP), Time to Interactive (TTI), etc. ▸ Still, focus on performance analysis and inspection without giving developers directly control over user-perceived performance. 58
210. User-Centric Language (Extensions) ▸ Web performance is a comprehensive user-centric objective that can be not captured by one single metric. ▹ Old days: time when unload is fired, doesn’t corresponds to UX ▹ Now: First Meaningful Paint (FMP), Time to Interactive (TTI), etc. ▸ Still, focus on performance analysis and inspection without giving developers directly control over user-perceived performance. ▹ Developers are given a set of high-level, coarse-grained guidelines 58
211. User-Centric Language (Extensions) ▸ Web performance is a comprehensive user-centric objective that can be not captured by one single metric. ▹ Old days: time when unload is fired, doesn’t corresponds to UX ▹ Now: First Meaningful Paint (FMP), Time to Interactive (TTI), etc. ▸ Still, focus on performance analysis and inspection without giving developers directly control over user-perceived performance. ▹ Developers are given a set of high-level, coarse-grained guidelines ▸ Proposal: empower developers to directly express their requirements of user-centric performance goals (up to the browser to deliver). 58
212. User-Centric Language (Extensions) ▸ Web performance is a comprehensive user-centric objective that can be not captured by one single metric. ▹ Old days: time when unload is fired, doesn’t corresponds to UX ▹ Now: First Meaningful Paint (FMP), Time to Interactive (TTI), etc. ▸ Still, focus on performance analysis and inspection without giving developers directly control over user-perceived performance. ▹ Developers are given a set of high-level, coarse-grained guidelines ▸ Proposal: empower developers to directly express their requirements of user-centric performance goals (up to the browser to deliver). ▹ Paint order: developers identify “hero” elements and specify the order in which hero elements are painted. div#hero {paint-order:1} 58
213. User-Centric Language (Extensions) ▸ Web performance is a comprehensive user-centric objective that can be not captured by one single metric. ▹ Old days: time when unload is fired, doesn’t corresponds to UX ▹ Now: First Meaningful Paint (FMP), Time to Interactive (TTI), etc. ▸ Still, focus on performance analysis and inspection without giving developers directly control over user-perceived performance. ▹ Developers are given a set of high-level, coarse-grained guidelines ▸ Proposal: empower developers to directly express their requirements of user-centric performance goals (up to the browser to deliver). ▹ Paint order: developers identify “hero” elements and specify the order in which hero elements are painted. div#hero {paint-order:1} ▹ Interactive state: developers specify the kind of interaction that should ideally be granted when a particular element is painted. div#menu{istate:touchstart,50} 58