Personalized Diapause: Reducing Radio Energy Consumption of Smartphones by Network-Context Aware Dormancy Predictions

Transcription

1 1 A period of suspended growth accompanied by decreased metabolism in insects Personalized Diapause: Reducing Radio Energy Consumption of Smartphones by -Context Aware Dormancy Predictions Yeseong Kim and Jihong Kim Computer Architecture & Embedded Systems Lab. Department of Computer Science and Engineering Seoul National University 2012 Workshop on Power-Aware Computing and Systems October 7, 2012

2 2 Radio Energy Consumption in Smartphones High radio energy consumption About 30% of the total energy consumption in smartphones (3G ) Radio energy consumption increasing -dependent apps increasing (e.g., SNS apps) Shift to high-energy-demand 4G LTE radio network 3G or 4G LTE Apps Mobile Reducing radio energy consumption is an important design issue of smartphones.

3 Power (mw) 3 Radio Energy Consumption in Tail Time A significant portion of radio energy is consumed during tail time After a packet transmission, a high power level is maintained expecting a subsequent transmission. End of Transmission TAIL (10~20 Seconds) New Transmission No reconnection Time (ms)

4 Wasted tail energy of total radio energy (%) Power 4 Wasted Radio Energy during Tail Time A significant radio energy is wasted during tail time. If there is no transmission End of Transmission Wasted Tail Energy Tail Energy Tail time From our measurement study of 25 smartphone users 50% Time 40% 30% 20% 10% 0% 33% of the total radio energy is wasted in the tail time Reducing wasted radio energy is very important.

5 Power Packet Power Packet 5 Fast Dormancy and Key Challenge The Fast Dormancy feature enables a smartphone radio module to release the radio connection to save the wasted energy during the tail time Invoke fast dormancy Time Key Challenge: How to predict the subsequent transmission? Time

6 Power Packet Power Packet 6 Fast Dormancy and Key Challenge The Fast Dormancy feature enables a smartphone radio module to release the radio connection to save the wasted energy during the tail time Invoke fast dormancy Time Key Challenge: How to predict the subsequent transmission Right prediction: Saving radio energy Invoke fast dormancy Time

7 Power Packet Power Packet 7 Fast Dormancy and Key Challenge The Fast Dormancy feature enables a smartphone radio module to release the radio connection to save the wasted energy during the tail time Invoke fast dormancy Time Key Challenge: How to predict the subsequent transmission Wrong prediction: Additional Reconnection ; Long delay (e.g.,, 2 secs) to smartphone, Signaling overhead to mobile network Time

8 8 Problems of Existing Dormancy Technique Problem 1. Existing dormancy techniques are app-centric. require app-assisted run-time hints on the next transmission e.g., TOP [ICNP 10] Playing No more network access for a while App Developer Streaming No automatic App support Run-Time Hints User System Software

9 9 Problems of Existing Dormancy Technique Problem 2. Existing dormancy techniques are not applicable to most interactive apps. It is very difficult to predict how a user interacts with interactive apps such as google talk and facebook app. Mobile Interactive Apps Not applicable? User App Developer Run-Time Hints System Software

10 10 Contributions We propose Personalized Diapause, a general-purpose automatic predictive dormancy technique for supporting the fast dormancy feature Not depending on app-assisted future network usage hints Applicable to most of apps with general network transmission patterns Personalized Diapause was implemented on Android 2.3 (Gingerbread) Nexus S smartphones Radio energy consumption saving by up to 36% with 10% increase in the radio reconnection over when no fast dormancy feature is used.

11 11 Outline Introduction Overview of Personalized Diapause Key Steps of Personalized Diapause Extraction of Context Estimation of Transmission Trend Predictive Dormancy Analysis Experimental Results Conclusion

12 12 Key Idea of Personalized Diapause Activity Apps Sending a message Downloading a song Checking new s... Transmissions Transmissions Transmissions Transmissions Time

13 13 Key Idea of Personalized Diapause Activity Apps Sending a message Downloading a song Checking new s... Transmissions Context 1 Transmissions Context 2 Transmissions Context 3 Transmissions Context 4 Context Block Context Block Context Block Context 1 Context 3 Context 2 Context 4 Time

14 14 Key Idea of Personalized Diapause Personalized Usage Characteristics Activity Apps Transmission Trend Sending a message Downloading a song Checking new s... Transmission Trend Transmissions Context 1 Transmissions Context 2 Transmissions Context 3 Transmissions Context 4 Context Block Context Block Context Block Context 1 Context 3 Context 2 Context 4 Time

15 15 Overview of Personalized Diapause Key steps of Personalized Diapause Extracting semantically equivalent network activities + Estimating transmission trends of network activities Predictive dormancy analysis Context Block Context 1 Context 3 Transmission Trend Predictive Dormancy

16 16 Outline Introduction Overview of Personalized Diapause Key Steps of Personalized Diapause Extraction of Context Estimation of Transmission Trend Predictive Dormancy Analysis Experimental Results Conclusion

17 ~ Transmission 17 Step 1. Extraction of Context Transmissions are transferred due to network activities. Context Context Context Context Time Apps Downloading a song Downloading a song Fetching new s Sending an Activity

18 ~ Transmission 18 Step 1. Extraction of Context Transmissions are transferred due to network activities. Context Context Context Context Time Apps Downloading a song Downloading a song Fetching new s Sending an System Software Execution Path A Execution Path A Execution Path B Execution Path C The network activities can be systematically distinguished from their execution paths.

19 19 Identifying Equivalent Context Available from Dalvik VM connect, write, read, send, recv Socket API recv() recv() Transmission Time Downloading a song Context 1 Downloading a song Context 2

20 20 Identifying Equivalent Context Same execution paths Application Function Call Stack selectsong() makebuffer() downloadsong() selectsong() makebuffer() downloadsong() Socket API recv() recv() Transmission Time Context 1 Context 2 Context Block (NCB)

21 21 Context Block (NCB) contexts in the same Context Block are assumed to perform same network activity. Context 1 Context 2 Context 4 Downloading a song NCB 1 Context 3 Context 5 Context 6 Sending a message NCB 2 NCB 3 NCB 4 NCB 5 User s network context blocks Basic unit of monitoring transmission trend in the tail time

22 22 Key steps of Personalized Diapause Step 1. Step 2. Extracting semantically equivalent network activities + Estimating transmission trends of network activities Step 3. Predictive dormancy analysis Context Block Context 1 Context 3 Transmission Trend Predictive Dormancy

23 23 Personalized Transmission Trend Claim 1. Different users differently behave even for same network activity. Talk via messenger apps Mr. Every10Seconds Prof. EveryHour A transmission is likely to occur in the tail time A transmission is unlikely to occur in the tail time Must consider personalized transmission trends.

24 24 Transmission Trend of Activities Claim 2. Different Activities have different transmission trends. Checking system update Browsing a web page Sending a message Checking new s Transmission Trend Transmission Trend Transmission Trend Transmission Trend Must consider different network activity characteristics.

25 Transmission 25 Validation Study of Smartphone Usage Subject: 25 active smartphone users Aged 20~40 College students, graduate students, bankers, kindergarten teachers Study Period: during two weeks Method: using a modified Dalvik VM For logging network contexts with call stack information Context Whether/When did a next transmission occur in the tail time?? Tail time Time

26 User 1 User 2 User 3 User 4 User 5 User 6 User 7 User 8 User 9 User 10 User 11 User 12 User 13 User 14 User 15 User 16 User 17 User 18 User 19 User 20 User 21 User 22 User 23 User 24 User 25 Rate of occurrence of a next transmission in the tail time (%) 26 Personalized Usage Tendency Strong personalized network usage tendency Mr. Every10Seconds Prof. EveryHour Large energy wasted 0

27 Rate of occurrence of a next transmission (%) 27 Skewed Transmission Distribution User s behavior for transmissions is quite skewed Week 1 Week Most of the first transmissions in the tail happen within the first 6 seconds Time of occurrence of a next transmission (sec) These persistent right-skewed distribution can be exploited to apply the fast dormancy feature.

28 Rate of occurrence of a next transmission in the tail time (%) Transmission Characteristics for Different Activities Per User Different transmission characteristics for different network activities The transmission trend of each NCB persists over long time Checking system update Browsing a web page Sending a message Week 1 Week 2 Fetching new s Exploiting these persistent transmission trends over different NCBs,we can estimate transmissions in the tail time. 28

29 Step 2. Estimating Transmission Trend of Activity We estimate when/whether a transmission will occur in the tail time based on the skewed transmission distribution of each NCB. Context 1 A user s network context blocks Context 2 Context 4 Transmission Trend 29 Context 3 NCB 1 Context 5 Context 6 Transmission Trend NCB 2 Transmission Trend NCB 3 NCB 4 NCB 5 Transmission Trend Transmission Trend

30 30 Key steps of Personalized Diapause Step 1. Step 2. Extracting semantically equivalent network activities + Estimating transmission trends of network activities Step 3. Predictive dormancy analysis Context Block Context 1 Context 3 Transmission Trend Predictive Dormancy

31 Power 31 Step 3. Predictive Dormancy Analysis To determine when to invoke the fast dormancy feature Considering cost-benefit tradeoff Invoke fast dormancy at t i If a transmission occurs at t j (p j ) Tail Energy Canceled Benefit Over head Gain (G i ) = Expected Energy Benefit (B i ) Energy Cost (C j ) Time Probability (p j ) - ( ) Intuitively, choosing the best moment (t i ) to invoke fast dormancy Consider t k 's only where the probability of retransmissions after t k is less than a given upper bound threshold (See our paper for a detail description of the decision procedure) j

32 32 Architectural Overview of Personalized Diapause The key steps are implemented as additional modules to the Dalvik VM and Android framework. Call Stack Tracer Tail Time Power Model Application Dalvik VM Android Framework Personalized Activity Predictor Dormancy Granter Personalized Activity Predictor Context Block Extractor Immediate-Successor Trainer Cost-Benefit Analysis Engine

33 33 Outline Introduction Overview of Personalized Diapause Key Steps of Personalized Diapause Extraction of Context Estimation of Transmission Trend Predictive Dormancy Analysis Experimental Results Conclusion

34 34 Experimental Environment Implemented the Personalized Diapause (PD) technique on Nexus S Android reference smartphones Running Android 2.3 (Gingerbread) To Dalvik VM and Android framework Using the collected network transmission logs from 25 users A custom log replayer tool reproduced network contexts logs. A 3G energy simulator was used for energy consumption comparison. Log Replayer User Log Transmission & Fast dormancy Log 3G Energy Simulator Nexus S (Target Device)

35 Energy saving (%) 35 Impact of PD on Energy Consumption Saving Energy saving of Personalized Diapause No-fast-dormancy support as a baseline Reconnection increase limit 10% 15% 20% On average 60 23% energy saving with 10% of reconnection increase User 1 User 2 User 3 User 4 Mean (25 users)

36 Energy Saving (%) 36 Impact of NCB Classification Technique Comparison with Per-user PD Assuming that all network contexts are classified to a single NCB Per-NCB Per-user vs Per-user PD Per-user PD Very poor energy saving User 1 User 2 User 3 User 4 Mean (25 users) The fine-grained NCB separation based on semantic differences is important in achieving a high energy efficiency.

37 37 Conclusions We presented a general-purpose automatic predictive dormancy technique, Personalized Diapause. Optimizing the radio energy consumption of smartphones with the fast dormancy feature Personalized Diapause takes advantages of personalized network context usage in deciding when to release a radio connection. Based on an automatic extraction technique of meaningful network activities Future work Extend for other types of system optimizations using other useful information available from the network context.

38 Thank you 38