May 29, 2019

By Hani Tawfiq Khader and Shabbir Majeed Chaudhry, University of Engineering and Technology, Taxila, Pakistan Android is a very popular operating system (OS), and because of its effectiveness, performance and practicality, it is widely used in mobile and portable devices. However, its popularity has also brought it a drawback in the form of poor optimisation since some vendors do not build it but port it from stock, such as Android Google sources. Often, this results in the hardware’s lower performance and poor power efficiency. Optimising the OS Optimising an operating system consist of re-writing or modifying the main source code, whilst taking into consideration each system’s unique set of parameters. With Android, using the source code is not entirely possible in most cases, since many companies that provide Android devices keep their systems closed. Still, most of these systems are built using the main source code. Having prior knowledge of the OS and its modifications helps further optimise it, by providing direct solutions to challenges encountered on any device built on it. This sort of re-optimisation can be carried out without having to decompile the original sources of the Android OS, and also it can be done at the user’s end. Improving the power efficiency of the Android platform is one of the most active research areas in Android system development. Different research studies have been carried out on existing techniques to improve battery efficiency without compromising user experience, including ways of lowering screen brightness, switching pixels, increasing Screen-On Time (SOT), using alternative applications that do same work with less resources, and others. Google introduced a method that provides a better battery efficiency by restricting app access to network- and CPU-intensive services. Also, there’s a method of increasing battery efficiency by determining and monitoring device connectivity status; for example, if not connected to the Internet, the device will not need a scheduled update either. During design, first we need to establish the main areas that affect the system’s optimisation and efficiency, including: User applications and services;Memory;Network optimisation;Kernel optimisation. Lowering the Energy Consumption User applications and services consume power, since they constantly use background processing (CPU, GPU, etc.), synchronisation, data transfer to and from the Internet and communications protocols among others. App hibernation helps increase device efficiency to over 75%; however, it’s best that hibernation entirely stops the app’s background processes, with only a gap or a wakeup timer for device wake-up; this gap can work as a periodic CPU wake-up timer for the app. Table 1 shows battery usage in different operating modes. Table 1. App hibernation efficiency Another way to improve app efficiency is by automating its tasks. We used Tasker for a test case, with which we automated several apps that normally run all the time in the background. The goal was to place these applications in hibernation mode as soon as the user turns off the screen. Once the screen comes back on, the applications start to run again. Fig. 1 Display state After testing the automation of the most-used applications like WhatsApp, Facebook, Twitter, etc., on the display state task for 24 hours, the automation saved over 25% of standby time without compromising user experience. Fig. 2 Hibernation application comparison chart Memory Management Improved memory management also optimises the system, which affects both performance and battery use. We tested something called ‘Swappiness’ on the memory to ascertain how it may increase battery consumption rate, considering the size of the RAM (Random Access Memory). Swappiness is a property of the Linux kernel that changes the balance between swapping out runtime memory, as opposed to dropping pages from the system page cache. Swappiness can be set to values between 0 and 100; a low value means the kernel will avoid swapping as long as possible, whereas a higher value will make the kernel work harder to use swap space. We set Swappiness to 60 to start with, which keeps the swap in an on-state whenever the RAM is 35-40% used. This results in a slower task operation, which decreases efficiency. In a follow-up test, we reduced the Swappiness value to 25 on a 3GB RAM device, which makes the swap work only after 70-75% of RAM is used: chmod 644 /proc/sys/vm/swappiness echo “25” > /proc/sys/vm/swappiness Above, the ‘/proc/sys/vm/swappiness’ parameter sets the value that balances between dropping pages from page cache and the swapping-process memory. Other parts of the memory are ‘dirty_writeback_centisecs’ and ‘dirty_expire_centisecs’, where the first refers to how often the kernel flusher threads will wake up to write dirty data to disk, and the latter is how long data can be in cache before it needs to be written. Default values are five seconds for ‘dirty_writeback_centisecs’ and 30 seconds for ‘dirty_expire_centisecs’. We rearranged the timing by doubling the value of ‘dirty_writeback_centisecs’ to 10 seconds and at the same time decreased the ‘dirty_expire_centisecs’ to five seconds: if [ -e /proc/sys/vm/dirty_expire_centisecs ]; then echo “500” > /proc/sys/vm/dirty_expire_centisecs fi if [ -e /proc/sys/vm/dirty_writeback_centisecs ]; then echo “1000” > /proc/sys/vm/dirty_writeback_centisecs fi ‘/proc/sys/vm/dirty_expire_centisecs’ and '/proc/sys/vm/dirty_writeback_centisecs’ are stock kernel parameters that set the value of both ‘dirty_expire_centisecs’ and ‘dirty_writeback_centisecs’, respectively. Setting these improves RAM management, which enhances the memory task performance and eliminates any delays in task processing. At the same time, battery performance was improved by 15-20%. Fig. 3 Memory effects on a full battery cycle Network Optimisation Wi-Fi is one of the most common reasons for low battery performance. The constant usage of the Wi-Fi service relies on Wi-Fi wakelocks, which keep the devices awake for app access, preventing it from going into deep sleep, and hence causing rapid battery-drain. It’s most effective to run the Wi-Fi service when needed, which will reduce the wakelock service by over 80%. We can modify the SQlite database of the device settings parameter ‘Android ID’ located in the ‘/data/data/com.android.providers.settings/databases/settings.db’ by using the Bash (Unix shell) language: sqlite=/system/xbin/sqlite3 wifi_idle_wait=60000 RETURN_VALUE=$($sqlite /data/data/com.android.providers.settings/databases/settings.db “select value from secure where name=‘wifi_idle_ms’”) if [ $RETURN_VALUE=‘‘ ]; then $sqlite /data/data/com.android.providers.settings/databases/settings.db “insert into secure (name, value) values (‘wifi_idle_ms’, $wifi_idle_wait )” else $sqlite /data/data/com.android.providers.settings/databases/settings.db “update secure set value=$wifi_idle_wait where name=‘wifi_idle_ms’” Fi ‘Wifi_idle_ms’ is a parameter that controls Wi-Fi idle time for the default sleep policy from the Android Open Source Project. This script allows to set the value of ‘wifi_idle_ms’ to a specific time. Once the screen is turned off, Wi-Fi remains connected for 60 seconds before it turns off; 60 seconds is the value we assigned to the variable ‘wifi_idle_wait’. Once the screen is on, Wi-Fi automatically reconnects. This resolves most wakelock issues related to Wi-Fi. As an alternative solution to network connection loss, when the screen is off, the device can automatically connect to data connection of the SIM card, turning off Wi-Fi. We conducted a worst-case scenario test on a device running on the Android platform 5.1.1, connected to a public Wi-Fi access point, a public university campus where many people use the service, which lowers the connection quality. We conducted the test over two days; see Table 2. Day Standby Time Screen on Time 1 6h 1.3h 2 24h 6.8h Efficiency Difference 300% 423% Table 2. Test case: University campus Wi-Fi sleep efficiency The first day the device was connected to the Wi-Fi access point with standard settings, and the second, the device was connected to the access point using settings with the Bash script. The ‘wlan_rx wakelock’ lowered its operation from 85% of the time to 8%, in one battery cycle. Fig. 4 Wi-Fi connected at the university campus with standard Android 5.1.1 settings Fig. 5 Wi-Fi connected at the university campus with settings using Bash script In Figure 4, ‘wlan_rx_wake’ was awake 85% of the time during the last five hours (after unplugging the charger), leaving the battery with 54% capacity. In Figure 5, after using the script to put the Wi-Fi to sleep (60 seconds after turning the screen off), the ‘wakelock_rx_wake’ was awake for only 50 minutes and 49 seconds, which is 8% of the time, leaving the battery with 80% capacity after 10 hours and 55 minutes following unplugging the charger. We repeated the test in a residential house with Wi-Fi access point; see Table 3. Day Standby Time Screen on Time 1 17h 3h 2 38h 3.2h Efficiency Difference 55.26% 6.25% Table 3. Test case: Wi-Fi sleep efficiency in a residential home The standby time was enhanced by 55.26%, while screen usage was almost the same, in a full battery cycle, due to the absence of Wi-Fi connectivity in the background when the screen was off or in standby. Fig. 6 Wi-Fi test at different locations Bluetooth Power Consumption The Bluetooth service is also one of the most common power users. The service usually handles data transfer or pairing with another device. Since most users forget to toggle off the Bluetooth service after using it, this results in a significant battery drain. One of the solutions we propose is to have the Bluetooth service turned off automatically when not paired with a device for a specific period of time. To implement it, we created a Tasker profile that checks if the...

May 29, 2019

Würth Elektronik presented its first ever ExCEllence in Senior Design Showcase at the University of Texas in Dallas

Optimising the Android platform for power efficiency

Würth Elektronik presented its first ever ExCEllence in Senior Design Showcase at the University of Texas in Dallas

Optimising the Android platform for power efficiency

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