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Timecard: Controlling User-Perceived Delays in Server-Based Mobile Applications Lenin Ravindranath, Jitu Padhye, Ratul Mahajan, Hari Balakrishnan
Timecard:Controlling User-Perceived Delays in
Server-Based Mobile Applications
Lenin Ravindranath, Jitu Padhye, Ratul Mahajan, Hari Balakrishnan
CloudServices
MobileApps
CloudServices
• Users are impatient– 100ms delay can cost substantial drop in revenue at Amazon– Similar observations from Google and Bing
Response Time Matters
Control Variability in Response Times
Request Response
• Fixed deadlines• Trade-off quality for response time– More time to compute, better quality results– Flexible Services
• For e.g., search
Deadline
Server processing
The elephant is outside the room
Request Response
Server processing
The elephant is outside the room
User click
Server processing
App App
ServerRequest Response
Reading sensors Link quality(3G, HSPA+, LTE, Wifi)
Response size
Phone model
Parsing andRendering
Uplink Downlink
User perceived delay
Highly variable
Radio State
The elephant is outside the room
User click
Server processing
App App
ServerRequest Response
Reading sensors Link quality(3G, HSPA+, LTE, Wifi)
Response size
Phone model
Parsing andRendering
Uplink Downlink
User perceived delay
Highly variable
Radio State
• Unaware of end-to-end delays• Clients with non-trivial external delays– Poor end-to-end response times
• Client with low external delays– Do not produce the best quality result
The elephant is outside the room
User click
Server processing
App App
ServerRequest Response
Reading sensors Link quality(3G, HSPA+, LTE, Wifi)
Response size
Phone model
Parsing andRendering
Uplink Downlink
User perceived delay
Highly variable
Radio State
Servers should adapt to external delays and control end-to-end delay variability
TimecardGetElapsedTime(); PredictRemainingTime
(responseSize);
Time elapsed since user request
Predicted downlink + app processing delay
App App
Server
Timecard
App App
Server
Desired end-to-end delay
App
GetElapsedTime(); PredictRemainingTime(responseSize);
Adapt Processing Time
Timecard
App
Server
Desired end-to-end delay
AppApp
GetElapsedTime(); PredictRemainingTime(responseSize);
Adapt Response
Timecard
App
Server
Desired end-to-end delay
App
GetElapsedTime(); PredictRemainingTime(responseSize);
TimecardGetElapsedTime(); PredictRemainingTime
(responseSize);
App App
Server
UI Thread
Background Thread
Background Thread
Server Threads
UI dispatcher
Thread start
GPS start
Web requestGPS callback
Event handler
Spawn workersRequest handler
Send response
Web callback
App App
Server
Challenges
UI Thread
Background Thread
Background ThreadApp App
Server
Challenges
Transaction
GetElapsedTime(); PredictRemainingTime(responseSize);
No single reference clock
Automatically collect data and learn
• Online Transaction Tracking• Time Synchronization• Remaining Time Predictor– Downlink delay– App processing delay
Timecard
• Periodic time sync probes from app to server– Find drift and offset between clocks
• Use server clock as reference clock– Client maps local time to server time
Synchronizing Time
• Efficient technique for probing– Aware of the radio state and traffic– Minimal extra delays– Energy efficient
Predicting Remaining Time
App App
Server
• Downlink time• App processing time
PredictRemainingTime(responseSize);
Predicting Downlink Time
Data size
• 1 KB to 40 KB of data• RTT matters than throughput– Predict RTT
• TCP window state matters– Multiple RTTs– Estimate TCP Window &
number of RTTs
• Complicated by middleboxes
Middlebox
Read TCP state
EstimateTCP state
Predicting Downlink Time
Data size
• Model downlink delay– Recent RTT– Bytes already transferred– Response size– Network provider– Client OS
Middlebox EstimateTCP state
• Error in cellular network– median 17 ms– 90th percentile 86ms
Predicting App Processing Time
• Parsing and rendering depends on data size• Model processing time as a function of– Response data size– Phone model
4.6% (8ms) median error, 10% in 90th percentile
Timecard Implementation
App App
Server
Transaction Tracking
Transaction Tracking
Logging
Predictor
Time Sync
Data Collection
Automatic Instrumentation• WP Apps• ASP .NET services APIs
Timecard can help control end-to-end delay
• Modified two services (and two apps) to use timecard
Mobile Ads Service
• 0.1% runtime overhead
• Less than 1% memory overhead– Garbage collection in idle time
• Less than 1% network overhead
• Negligible battery overhead
Timecard Overhead
TimecardGetElapsedTime(); PredictRemainingTime
(responseSize);
App App
Server
Desired end-to-end delay
Adapt Processing Time
Adapt Response
TimecardGetElapsedTime(); PredictRemainingTime
(responseSize);
App App
Server
Request Prioritization
Desired end-to-end delay
TimecardGetElapsedTime(); PredictRemainingTime
(responseSize);
App App
Server
Adapt Resources Used
Desired end-to-end delay
Backup
UI Thread
Background Thread
Background Thread
Server Threads
App App
Server
Transaction Tracking
TC Transaction Context
- Timestamps, transaction/device/network information
TC
TC
TC
TC
TC
TC
TC
TC
GetTransaction().GetStartTime();
