This chapter starts with the introduction of the layered protocol model and indicate how the energy conserving mechanism can be applied to each layer. Since the major contributions included in this thesis, i.e. energy efficient routing and adaptive deliv- ery, mainly concern Network Layer and Applications Layer, the state-of-the-art of the energy efficient solutions on these two layers are classified and explained with details. Besides, the major cross layer proposals are also described. Nowadays energy effi- ciency of mobile devices regarding both hardware and software solutions is of great importance. Therefore this research on energy efficiency concerns the heterogene- ity and differentiation of mobile devices. Notably, different types of energy efficient mechanisms on mobile devices are discussed in this chapter. A better understanding of mobile devices and the existing research on different layers plays a crucial role during my work.
Energy-oriented Application-based
System Profiling and
Prototype-based Testing
3.1
Overview
This thesis presents the Energy-oriented Application-based System Profiling (ASP) for mobile devices that: a) constructs a mapping between the work load on each hardware component and the corresponding system power consumption value; b) monitors the work load on the hardware components for each application, in order to construct an application profile table that records the power signature of applications; c) calculates the energy constraint of current context by referencing the above tables and monitoring current status of the device (screen brightness, application type, etc).
As the output of ASP, the energy constraint information is used for Energy Ef- ficient Routing and Energy Efficient Adaptive Delivery, which are presented in the following sections. The routing protocol and the cross layer adaptive delivery protocol are considered users/consumers of ASP.
ASP is performed in three phases: setup, monitoring and update. ASP focuses on those components of the latest mobile devices [9] which are the major energy con- sumers among all the hardware components. These are screen (SCR), graphics pro- cessor (GRA), WLAN interface card (WLAN), such as WiFi for example, cellular network interface module (CELL), such as GSM, UMTS, LTE, etc. and processing chip set (CPU).
The ASP is implemented in a prototype system dedicated to work for smart mobile devices, including smart phones and tablet PCs. The system adopts a pure software application level approach, where no modification on hardware and software network protocol stack or external devices is needed. The testing results are obtained on a Samsung Galaxy Note 10.1 tablet with the ASP prototype system installed.
This proposed model is the first application-aware dedicated energy modelling software for mobile devices, smart phone and tablets, that has its design, online model construction and testing completely performed on the mobile device itself instead of lab settings. The conducted comprehensive real tests are the first to target tablet de- vices. In fact, Samsung Galaxy Note 10.1 is used throughout for the design, software development and all the tests. In addition, this is the first solution to feature compre- hensive application aware modelling. It also features incremental model updating.
First, a regression based energy-oriented system profiling system is built. This system runs a series of tests to record battery discharge while subsystems are loaded at different levels in order to build a machine learning based device energy model. This energy model takes hardware subsystem usage as input and calculates the estimated energy constraint score.
During daily device usage, the profiling system keeps record of the power signature of individual applications by periodically obtaining and recording device parameters. Referencing current application type and the energy model, this system will give the score of current device energy constraint on demand in real time. Notably, the energy
model construction takes incremental training approach that updates the energy model periodically to suit the latest device usage scenarios, including user habit, hardware worn and so on.
The use of ASP imposes overhead to the system. Firstly, it needs periodically sam- pling to collect system utilisation data. Secondly, the complex calculation is needed to train the device energy model and calculate device energy constraints. To reduce overhead, ASP keeps and uses application power signature to reduce the frequency of sampling data from the system. Besides ASP trains its device energy model while device is charging.
The data collection overhead is computed as File Input Output cost * Sampling Rate. The File Input Output cost is hardware related, and consequently real-life mea- surement is needed to quantify the data collection overhead. Similarly real-life mea- surement is also needed to quantify the overhead of model training. In this context, the total overhead was evaluated on the prototype system as illustrated in the subsection Overhead Analysis.
3.2
Contributions and Advantages
ASP include features from several existing energy modelling techniques. As in Sesame [106], the battery interface is used instead of external hardware. Battery interface is a set of Linux API functions which provides all the information regarding battery status, including voltage, current, and capacity, etc. This enables self constructive software based device independent energy modelling. This has two benefits, practicality and device independence. For the incremental model update, the training only happens when the device is charging to minimise the energy consumption on modelling. As PowerBooter [105], I train the model using many variations of loads rather than some typical loads for several existing applications. This effort is to obtain a generalised model that suits many potential new applications.
Compared with the above existing energy modelling techniques, There are new observations made during the field tests. More importantly, ASP addresses some prob- lems which were not addressed so far.
• Many papers explored the construction of a power model for mobile-phones, but as far as known, this is the first model constructed for a tablet device. Although these models are similar in many aspects, such a model is important since it permits the analysis of the energy is being spent in the tablet, and once the model is created, the power estimation can be done with a higher sampling rate and accuracy rate compared to the battery interfaces of the tablet.
• Application power signature is proposed to reduce monitoring overhead. This is because any individual application shows distinctive energy characteristics on different device subsystems.
• The proposed model does not rely on any external power measuring equip- ment/tool to validate the model. This is because external tools are not available in daily device usage, and most likely cannot be used for model updating and further training.
• The energy modelling technique adopts incremental updating in order to adapt the model to the change of battery and user preferences.
• Field tests demonstrate the difference between LCD and OLED in terms of en- ergy characteristics complementing the many works done on OLED, a realistic yet different energy model for LCD, is believed to be used for big screens, par- ticularly tablet screens.
• ASP features a better refined CPU energy model and addresses issues that are not fully understood in the existing works. For instance, the relationship between CPU load and CPU energy usage was not comprehended by existing researches [5]. ASP takes into account that modern mobile CPU features multiple cores and
Figure 3.1: The Architecture of ASP
ASP uses different CPU energy models when CPU activates different number of cores.
the observed that the multi-core multi-frequency feature of modern mobile CPU can explain the non leaner relationship between CPU load and energy usage, which was not comprehended by existing researches [5].