Fine-Grained Power Modeling for Smartphones Using System Call - PowerPoint PPT Presentation

Fine-Grained Power Modeling for Smartphones Using System Call Tracing Based on paper and presentation by: Abhinav Pathak, Y. Charlie Hu, Ming Zhang Paramvir Bahl, Yi-Min Wang Damian Rodziewicz

Fine-Grained Power Modeling for Smartphones Using System Call Tracing Smartphone Capabilities

Fine-Grained Power Modeling for Smartphones Using System Call Tracing Smartphone Capabilities Camera

Fine-Grained Power Modeling for Smartphones Using System Call Tracing Smartphone Capabilities Camera GPS

Fine-Grained Power Modeling for Smartphones Using System Call Tracing Smartphone Capabilities Camera GPS Wi-Fi

Fine-Grained Power Modeling for Smartphones Using System Call Tracing Smartphone Capabilities Camera GPS Wi-Fi Games and applications

Fine-Grained Power Modeling for Smartphones Using System Call Tracing Smartphone Capabilities Camera GPS Wi-Fi Games and applications

Fine-Grained Power Modeling for Smartphones Using System Call Tracing Smartphone Constraints ● Energy – one of the most critical issues in smartphones. ● Smartphone capabilities are growing rapidly. ● Battery capacity has only doubled through 10 years. "According to unpublished research by the Boston Consulting Group, the amount of energy that a battery can store (its energy density) is growing by 8% a year . Mobile-device power consumption, meanwhile, is growing at more than three times this rate, as backlit colour screens, high-speed wireless networks and more powerful microprocessors draw ever-larger amounts of power."

Fine-Grained Power Modeling for Smartphones Using System Call Tracing Key issue How can we measure energy consumption in our applications? ● Power meter ● Online power models for mobile devices ● Utilization-based power model ● System call tracing power model

Fine-Grained Power Modeling for Smartphones Using System Call Tracing Measuring Energy Consumption Power meter ● High cost (~700$) ● Requires performing surgery on your phone ● Only whole energy usage ● Stationary – cannot move with ● Very accurate (every 200 ms exact power usage)

Fine-Grained Power Modeling for Smartphones Using System Call Tracing Online power model Źródło: http://eurosys2011.cs.uni-salzburg.at/pdf/eurosys2011-pathak-slides.pdf

Fine-Grained Power Modeling for Smartphones Using System Call Tracing Utilization-based approach State-of-art creating model Energy usage Ep = Energy used by 1 second of processor En = Energy used by 1 packet of data sent Ed = Energy used by writing/reading 1 byte … same for GPS, camera, and so on ...

Fine-Grained Power Modeling for Smartphones Using System Call Tracing Utilization-based approach State-of-art predicting Read data every second (something like proc in Linux): %Up = % of used Processor Nn = number of packets sent Nd = number of bytes read / written Result = %Up * Ep + Nn * En + Nd * En

Fine-Grained Power Modeling for Smartphones Using System Call Tracing Utilization-based approach State-of-art wrong assumptions ● Active utilization is the only trigger of power state change ● File open/close, socket open/close also consume energy ● Several components have tail energy states (even few seconds) ● Linear estimation ● Sending 100 packets does not cost 2 as much energy as sending 50 ● Quantitative utilization ● Cannot measure energy consumption of camera / GPS ● The interval can be too large to collect data, or the sampling can become too costly, if done at fine granularity.

Fine-Grained Power Modeling for Smartphones Using System Call Tracing Utilization-based approach Examples of power usage Źródło: http://eurosys2011.cs.uni-salzburg.at/pdf/eurosys2011-pathak.pdf

Fine-Grained Power Modeling for Smartphones Using System Call Tracing System-call-based approach Observations ● System calls provide the only means via which applications gain access to the hardware (I/O) components. ● Names and parameters give clear indication of components and level of utilization. (Encapsulation of utilization-based approach) ● System call can be naturally related back to the calling subroutine and the hosting thread and the process.

Fine-Grained Power Modeling for Smartphones Using System Call Tracing System-call-based approach Problems ● Tail energy states – How much do they consume? How long do they last? ● Device drivers are closed source (no source / no information given). ● Power consumption does not add linearly. ● Energy does not scale linearly.

Fine-Grained Power Modeling for Smartphones Using System Call Tracing System-call-based approach Problems – non linear additivity

Fine-Grained Power Modeling for Smartphones Using System Call Tracing System-call-based approach Problems – non linear scalability

Fine-Grained Power Modeling for Smartphones Using System Call Tracing System-call-based approach Idea ● Use system calls as triggers in power modelling. ● Reverse engineer power logic in device drivers. ● Use Finite-State-Machine (FSM) to model the power states and transitions between them in components. ● Nodes: Power States (Either productive state or tail state) ● Edges: Transitions between states ● Can be system call, timeout, other condition. ● We assume that these drivers implement very simple logic ● We use Linear Regression to connect workload with power usage in states.

Fine-Grained Power Modeling for Smartphones Using System Call Tracing System-call-based approach Creating FSM ● Create an FSM for one system call. ● Model all system calls for one component. ● Shuffle the system calls in a C program, look for new power states. ● Model one huge FSM for the phone from the components.

Fine-Grained Power Modeling for Smartphones Using System Call Tracing System-call-based approach Finite State Machine – step 1

Fine-Grained Power Modeling for Smartphones Using System Call Tracing System-call-based approach Creating FSM – step 2 - Wi-Fi

Fine-Grained Power Modeling for Smartphones Using System Call Tracing System-call-based approach Creating FSM – step 3

Fine-Grained Power Modeling for Smartphones Using System Call Tracing System-call-based approach Combinatorial issue ● Usualy one component has about 2 productive states and 1 tail state. ● It is done manually at this time. ● Applications are run, FSM models are created. ● States are binded together manually. ● Soon the process will be automated.

Fine-Grained Power Modeling for Smartphones Using System Call Tracing System-call-based approach Evaluation – CPU, Disk, Wi-Fi

Fine-Grained Power Modeling for Smartphones Using System Call Tracing System-call-based approach Evaluation – Entire Phones

Fine-Grained Power Modeling for Smartphones Using System Call Tracing System-call-based approach Implementation ● Tracing system calls in whole phone, to predict energy usage. ● Windows Mobile 6 ● CeLog – logging mechanism for CPU, memory, TLB, interrupt. ● Wrappers that log system calls in libraries. ● Thunking – system call made to invalid address – prefetch abort trap. ● Android ● Three levels – kernel, Dalvik Virtual Machine, framework. ● SystemTap – logging system calls in kernel. ● Logging library from Android to log framework calls.

Fine-Grained Power Modeling for Smartphones Using System Call Tracing System-call-based approach Implementation - Eprofiler ● Currently being worked on. ● Mannualy annotating system calls in the source code to log the entry and exit points.

Fine-Grained Power Modeling for Smartphones Using System Call Tracing System-call-based approach Estimation

Fine-Grained Power Modeling for Smartphones Using System Call Tracing System-call-based approach Estimation

Fine-Grained Power Modeling for Smartphones Using System Call Tracing System-call-based approach What is next? ● Implementing eproof ● Releasing modified Android image to public ● Releasing tools for Android to predict energy usage ● Detailed classification of power behaviour of different OSes and handsets

Fine-Grained Power Modeling for Smartphones Using System Call Tracing Thank You