- Users want their devices to run as long as possible on a single charge of battery. As an app - developer, you should strive to make your app draw the minimum amount of power from the battery - without sacrificing responsiveness, speed or latency for the user. You can take steps to optimize - your app for maximum battery life by following the guidelines in this course. In many cases, - you can minimize battery consumption and still get the same amount of work done by being - more efficient. -
- -- When users notice their battery does not get them through the day, they commonly check their - device's Settings to find the offending app, where they can easily uninstall it. You should - analyze your app to see if it is a leading source of battery drain, and take steps to prevent - excessive battery consumption. -
- -- These lessons show you how to analyze your app for common sources of battery drain and fix them - in your app. -
- -
-
-- Some techniques for reducing the amount of network use can be applied to multiple network - use scenarios. After you complete your analysis and optimization of the primary network - access use case for your app, you should also look at these general-purpose techniques - and see if you can apply them to your app. -
- -- This lesson briefly covers techniques that you can use to lower network use and reduce the - impact your app has on battery life. -
- -- If you can reduce the amount of data sent or received over a network connection, this also - reduces the duration of the connection, which conserves battery. You can: -
- -- Your app can avoid downloading duplicate data by caching. Always cache static resources, - including on-demand downloads such as full size images, and cache them for as long as reasonably - possible. -
- -- For example, you should consider this approach for a networked app that displays data from - user-initiated network requests as the primary content on the screen. When the user opens this - screen the first time, the app should display a splash screen. Subsequent loads should initially - load with the data that was cached from the last network request. The screen reloads with - new data once the network request is complete. -
- -- To learn about caching, watch the video. To implement caching in your app, see Cache Files - Locally. -
- - -- Optimize pre-fetch cache size based on local file system size and current network connectivity. - You can use the connectivity manager to determine what type of networks (Wi-FI, LTE, HSPAP, EDGE, - GPRS) are active and modify your pre-fetching routines to minimize battery load. -
- -- For more information, see - Use - Modifying your Download Patterns Based on the Connectivity Type. -
\ No newline at end of file diff --git a/docs/html/training/app/battery/network/action-app-traffic.jd b/docs/html/training/app/battery/network/action-app-traffic.jd deleted file mode 100644 index 09746e9b0f5d5..0000000000000 --- a/docs/html/training/app/battery/network/action-app-traffic.jd +++ /dev/null @@ -1,135 +0,0 @@ -page.title=Optimizing App-Initiated Network Use -trainingnavtop=true - -@jd:body - -
-
-- Network traffic initiated by your app can usually be significantly optimized, since you can plan - for what network resources it needs and set a schedule for accessing them. By applying careful - scheduling, you can create significant periods of rest for the device radio and, thereby, save - power. There are several Android APIs that can help with network access scheduling, and some of - these functions can coordinate network access for other apps, further optimizing battery - performance. -
- -- This lesson teaches you how to reduce battery consumption by applying techniques for - optimizing app-initiated network traffic. -
- - -- On a mobile device, the process of turning on the radio, making a connection, and keeping the - radio awake uses a large amount of power. For this reason, processing individual requests at - random times can consume significant power and reduce battery life. A more efficient approach is - to queue a set of network requests and process them together. This allows the system to pay the - power cost of turning on the radio just once, and still get all the data requested by an app. -
- - - - -- Using a network access scheduler API for queuing and processing your app data requests can - significantly increase the power efficiency of your app. Schedulers conserve battery power by - grouping requests together for the system to process. They can further improve efficiency by - delaying some requests until other requests wake up the mobile radio, or waiting until the - device is charging. Schedulers defer and batch network requests system-wide, across all apps on - the device, which gives them an optimizing advantage over what any individual app can do. -
- - -- Android provides three different APIs for your app to batch and schedule network requests. For - most operations, these techniques are functionally equivalent. These APIs are listed in the - following table with the most highly recommended first. -
- -| Scheduler | -Requirements | -Implementation Ease | -
|---|---|---|
| - GCM Network Manager | -GCM Network Manager requires that your app use the Google Play services client library, - version 6.1.11 or higher. Use the latest available version. | -Straightforward | -
| Job Scheduler | -Job Scheduler does not require Google Play services, but is available only when targeting - Android 5.0 (API level 21) or higher. | -Straightforward | -
| Sync Adapter | -Sync Adapter does not require the Google Play services client library and has been - available since Android 2.0 (API level 5). | -Complex | -
- Note: For scheduled data synchronization, you should always prefer GCM - Network Manager or Job Scheduler over Sync Adapter if your requirements allow it. -
- - -- One of the most serious and unexpected causes of battery drain is when a user travels beyond the - reach of any cell tower or access point. In this situation, the user is typically not using their - device, but they notice the device getting warm, and then see that the battery is low or has run - out. -
- -- In this scenario, the problem is that an app is running a background process that keeps - waking up the mobile radio at regular intervals to search for a cellular signal, but finds none. - Searching for a cell signal is one of the most power-draining operations there is. -
- -- The way to avoid causing this kind of problem for a user with your app is to use a - battery-efficient method for checking connectivity. For app-initiated network requests, use a - scheduler, which automatically uses Connectivity - Manager to check for connectivity before calling into your app. As a result, if there's no - network, the Connectivity Manager conserves battery because it performs the connectivity check - itself, without loading the app to run the check. Battery is further conserved because schedulers - use exponential - backoff to check for connectivity less frequently as time progresses. -
diff --git a/docs/html/training/app/battery/network/action-server-traffic.jd b/docs/html/training/app/battery/network/action-server-traffic.jd deleted file mode 100644 index e5ac971e7289c..0000000000000 --- a/docs/html/training/app/battery/network/action-server-traffic.jd +++ /dev/null @@ -1,81 +0,0 @@ -page.title=Optimizing Server-Initiated Network Use -trainingnavtop=true - - -@jd:body - -
-
-- Network traffic sent by server programs to your app can be challenging to optimize. A - solution to this problem is to periodically poll the server from your app to check for updates. - This approach can waste network connection and power by starting up a device radio, only to - receive an answer that no new data is available. A more efficient approach would be for the - server to notify your app when it has new data, but figuring out how to send a notification from - your server to potentially thousands of devices is no easy feat. -
- -- The Google Cloud Messaging (GCM) - service solves this communication problem by allowing your servers to send notifications to - instances of your app wherever they are installed, enabling greater network efficiency and - lowering power usage. -
- -- This lesson teaches you how to apply the GCM service to reduce network use for server-initiated - actions and reduce battery consumption. -
- - -- Google Cloud Messaging (GCM) is a lightweight mechanism used to transmit brief messages from an - app server to your app. Using GCM, your app server uses a message-passing - mechanism to notify your app that there is new data available. This approach eliminates network - traffic that your app would perform, by not contacting a backend server for new data when there - is none available. -
- -- An example use of GCM is an app that lists speaker sessions at a conference. When sessions are - updated on your server, the server sends a message to your app telling it updates are available. - Your app can then call the server to update the sessions on the device only when there is new - data, rather than periodically contacting the server to find out if there are updates. -
- -- GCM is more efficient than polling for changes on the server. The service eliminates - unnecessary connections where polling would return no updates, and it avoids running periodic - network requests that could cause a device radio to power up. Since GCM can be used by many apps, - using it in your app reduces the total number of network connections needed on a device and - allows the device radio to sleep more often. -
- -- For more information about GCM and how to implement it for your app, see - Google Cloud Messaging. -
- -- Note: When using GCM, your app can pass messages in normal or high priority. - Your server should typically use - normal priority to deliver messages. Using this priority level prevents devices from being - woken up if they are inactive and in a low-power Doze - state. Use high priority messages only if absolutely required. -
diff --git a/docs/html/training/app/battery/network/action-user-traffic.jd b/docs/html/training/app/battery/network/action-user-traffic.jd deleted file mode 100644 index 4d73ff3ebc175..0000000000000 --- a/docs/html/training/app/battery/network/action-user-traffic.jd +++ /dev/null @@ -1,122 +0,0 @@ -page.title=Optimizing User-Initiated Network Use -trainingnavtop=true - -@jd:body - -
-
-- Quick handling of user requests helps ensure a good user experience, especially when it comes to - user actions that require network access. You should prioritize low latency over power - conservation to provide the fastest response when optimizing network use that is a direct result - of user actions. Attaining an optimal network traffic profile for your app, while making sure - that your users get fast responses, can be a bit challenging. -
- -- This lesson teaches you how to optimize network use for user-initiated - actions and reduce battery consumption. -
- - -- Pre-fetching data is an effective way to reduce the number of independent data transfer sessions - that your app runs. With pre-fetching, when the user performs an action in your app, the app - anticipates data that will most likely be needed for the next user actions and fetches that data - in bulk. Battery power consumption is reduced because the pre-fetched data does not require - separate requests that each incur the overhead of waking up the mobile radio. -
- -- Tip: To explore whether your app might benefit from pre-fetching, review your - app's network traffic and look for situations where a specific series of user actions almost - always results in multiple network requests over the course of the task. For instance, an app - that incrementally downloads article content as a user views it might be able to pre-fetch one or - more articles in categories the user is known to view. -
- -- Watch the video on effective pre-fetching below which describes what pre-fetching is, where to - use it, and how much data to pre-fetch. For more details, see Optimizing - Downloads for Efficient Network Access. -
- - -- Searching for a cell signal is one of the most power-draining operations on a mobile - device. Your app should always check for connectivity before sending a user-initiated network - request. If you use a scheduling service, the service does this automatically for you. Fo - - (Schedulers do this automatically for you.) -
- -- A best practice for user-initiated traffic is to first check for a connection using Connectivity Manager, and if there - is no connection, schedule the network request for when the - connection is made. Schedulers will use techniques such as exponential backoff to save battery, - where each time the attempt to connect fails, the scheduler doubles the delay before the next - retry. -
- -- Note: To check for connectivity for app-initiated traffic, see Optimizing App-Initiated Network Use. -
- - -- In general, it's more efficient to reuse existing network connections than to initiate new ones. - Reusing connections also allows the network to more intelligently react to congestion and related - network data issues. For more information on reducing the number of connections used by your app, - see - Optimizing Downloads for Efficient Network Access. -
diff --git a/docs/html/training/app/battery/network/analyze-data.jd b/docs/html/training/app/battery/network/analyze-data.jd deleted file mode 100644 index 03efa05f6c486..0000000000000 --- a/docs/html/training/app/battery/network/analyze-data.jd +++ /dev/null @@ -1,225 +0,0 @@ -page.title=Analyzing Network Traffic Data -trainingnavtop=true - -@jd:body - -
-
-- Gathering data about the network access behavior of your app is the first step toward optimizing - your app's use of network hardware, and making your app more power efficient. After you have - tagged your app code with traffic identifiers, run tests, and collected data, it is time to - analyze that data. -
- -- This lesson teaches you how to look at the network traffic data you have collected from your app - and directs you to actions for improving your app's networking performance and reducing power - consumption. -
- - -- Efficient use of network resources by an app is characterized by significant periods where - the network hardware is not in use. - - On mobile devices, there is a significant cost associated with starting up the radio - to send or receive data, and with keeping the mobile radio active for long periods. If your app - is accessing the network efficiently, you should see that its communications over the network are - tightly grouped together, well spaced with periods where the app is making no connection requests. -
- -- Figure 1 shows suboptimal network traffic from app, as measured by the Network Traffic tool. The - app is making frequent network requests. This traffic has few periods of - rest where the radio could switch to a standby, low-power mode. The network access behavior of - this app is likely to keep the radio on for extended periods of time, which is - battery-inefficient. -
- -
-- Figure 1. Battery-inefficient network activity measured from an app. -
- -- Figure 2 shows an optimal network traffic pattern. The app sends network requests in bursts, - separated by long periods of no traffic where the radio can switch to standby. This chart shows - the same amount of work being done as Figure 1, but the requests have been shifted and grouped to - allow the radio to be in standby most of the time. -
- -
-- Figure 2. Battery-efficient network activity measured from an app. -
- -- If the network traffic for your app looks similar to the graph in Figure 2, you are in good - shape! Congratulations! You may want to pursue further networking efficiency by checking out the - techniques described in Optimizing General Network - Use -
- -- If the network traffic for your app looks more like the graph in Figure 1, it's time to take a - harder look at how your app accesses the network. You should start by analyzing what types of - network traffic your app is generating. -
- - -- When you look at the network traffic generated by your app, you need to understand the source of - the traffic, so you can optimize it appropriately. Frequent network activity generated by your - app may be entirely appropriate if it is responding to user actions, but completely inappropriate - if you app is not in the foreground or if the device in a pocket or purse. This section discusses - how to analyze the types of network traffic being generated by your app and directs you to - actions you can take to improve performance. -
- -- In the previous lesson, you tagged your app code for different traffic types and used the Network - Traffic tool to collect data on your app and produce a graph of activity, as shown in Figure 3. -
-
-- Figure 3. Network traffic tagged for the three categories: user, app, and - server. -
- -- The Network Traffic tool colors traffic based on the tags you created in the previous lesson. The - colors are based on the traffic type constants you defined in - your app code. Refer back to your app code to confirm which constants represent user, app, or - server-initiated traffic. -
- -- The following sections discuss how to look at network traffic types and provides recommendations - on how to optimize traffic. -
- - -- Network activity initiated by the user may be efficiently grouped together while a user is - performing a specific activity with your app, or spread out unevenly as users request additional - information your app does not have. Your goal in analyzing user-initiated network traffic is to - look for patterns of frequent network use over time and attempt to create, or increase the size - of, periods where the network is not accessed. -
- -- The unpredictability of user requests makes it challenging to optimize this type of network use - in your app. In addition, users expect fast responses when they are actively using an app, so - delaying requests for efficiency can lead to poor user experiences. In general, you should - prioritize a quick response to the user over efficient use of the network while a user is - directly interacting with your app. -
- -- Here are some approaches for optimizing user-initiated network traffic: -
- -- Caution: Beware of network activity grouping bias in your user activity test - data! If you ran a set of user scenarios with your network testing plan, the graph of - user-initiated network access may be unrealistically grouped together, potentially causing you to - optimize for user behavior that does not actually occur. Make sure your user network test - scenarios reflect realistic use of your app. -
- - -- Network activity initiated by your app code is typically an area where you can have a significant - impact on the efficient use of network bandwidth. In analyzing the network activity of your app, - look for periods of inactivity and determine if they can be increased. If you see patterns of - consistent network access from your app, look for ways to space out these accesses to allow the - device radio to switch into low power mode. -
- -- Here are some approaches for optimizing app-initiated network traffic: -
- -- Network activity initiated by servers communicating with your app is also typically an area where - you can have a significant impact on the efficient use of network bandwidth. In analyzing the - network activity from server connections, look for periods of inactivity and determine if they - can be increased. If you see patterns of consistent network activity from servers, look for ways - to space out this activity to allow the device radio to switch into low power mode. -
- -- Here is an approach for optimizing app-initiated network traffic: -
- -
-
-- The network traffic generated by an app can have a significant impact on the battery life of the - device where it is running. In order to optimize that traffic, you need to both measure it and - identify its source. Network requests can come directly from a user action, requests from your own - app code, or from a server communicating with your app. -
- -- The Network Traffic tool (part of the - DDMS tools) enables you to view how and when your app transfers data over a network. -
- -- This lesson shows you how to measure and categorize network requests by tagging your source code, - then shows you how to deploy, test and visualize your apps's network traffic. -
- - -- Apps use the networking hardware on a device for various reasons. In order to properly optimize - your app's use of networking resources, you must understand how frequently your app is using the - network and for what reasons. For performance analysis purposes, you should break down use of - network hardware into these categories: -
- -- This procedure shows you how to tag your app's source code with constants to categorize traffic - as one of these three request types. The Network Traffic tool represents each type of traffic - with a different color, so you can visualize and optimize each traffic stream separately. - The technique described here reports network traffic based on the execution of threads in your - app which you identify as a user, app or server source. -
- --public static final int USER_INITIATED = 0x1000; -public static final int APP_INITIATED = 0x2000; -public static final int SERVER_INITIATED =0x3000; --
extends GcmTaskService|extends JobService|extends
- AbstractThreadedSyncAdapter|HttpUrlConnection|Volley|Glide|HttpClient
- *.java.
-if (BuildConfig.NETWORK-TEST && Build.VERSION.SDK_INT >= 14) {
- try {
- TrafficStats.setThreadStatsTag(USER_INITIATED);
- // make network request using HttpClient.execute()
- } finally {
- TrafficStats.clearThreadStatsTag();
- }
-}
-
-
-
- Note: Ensure the tagging does not get into your production code by making
- inclusion of this conditional, based on the build type used to generate the APK.
- In the example above, the BuildConfig.NETWORK-TEST field identifies this
- APK as a test version.
-
- Note: This technique for tagging network traffic from your app depends on - how the APIs you are using access and manage network sockets. Some networking libraries may - not allow the {@link android.net.TrafficStats} utilities to tag traffic from your app. -
- -- For more information about tagging and tracking network traffic with the Network Traffic tool, - see Detailed Network Usage - in DDMS. -
- - -
- When you run performance tests, your APK should be as close as possible to the production
- build. In order to achieve this for your network testing, create a network-test
- build type, rather than using debug build type.
-
build.gradle file as shown in the following code example:
-
-
-android {
- ...
- buildTypes {
- debug {
- // debuggable true is default for the debug buildType
- }
- network-test {
- debuggable true
- }
- }
- ...
-}
-
- - To deploy the APK generated by the {@code network-test} build type configured in the previous - proceedure: -
- -network-test for the app module.
- network-test from the list.
- - The Network Traffic tool in Android Studio helps you see how your app uses network resources - while it is running. When running the tool with your app to test performance, you should have a - testing plan that exercises the primary functionality of the app, simulating a user interacting - with your app. You should also include idle time in your test plan, simulating periods when - users are not directly interacting with your app. -
- -- To improve the repeatability of your testing, you should start with a known initial state for - your app by clearing app data. The following procedure shows you how to stop your app and clear - all app data including previously cached data and networking data. This step puts your - app back to a state where it must re-cache all previously cached data. Do not skip this step. -
- -- To start the Network Traffic tool and visualize the network requests: -
- -- Note: You may be prompted to Allow USB Debugging on your - device. Select OK to allow debugging to proceed. -
--adb shell pm clear package.name.of.app --
- Use of tagging for network traffic helps you visually distinguish each request category by - producing a different color for each network traffic in the Network Traffic tool, as shown in - Figure 1. -
- -
-- Figure 1. Network traffic tagged for the three categories. -
diff --git a/docs/html/training/app/battery/network/index.jd b/docs/html/training/app/battery/network/index.jd deleted file mode 100644 index e0220653a24e2..0000000000000 --- a/docs/html/training/app/battery/network/index.jd +++ /dev/null @@ -1,87 +0,0 @@ -page.title=Reducing Network Battery Drain -page.article=true - -page.tags=battery -page.metaDescription=Learn how to optimize your app to reduce battery drain and use network resources efficiently. - -@jd:body - - - - -- Requests that your app makes to the network are a major cause of battery drain because they turn - on the power-hungry mobile or Wi-Fi radios. Beyond the energy needed to send and receive packets, - these radios expend extra power just turning on and keeping awake. Something as simple as a - network request every 15 seconds can keep the mobile radio on continuously and quickly use up - battery power. -
- -- This lesson shows you how to visualize network traffic and you how to tag and categorize your - app's source code to color-code your network requests according to how they are initiated, then - profile the network traffic. From there, each category identifies areas of your app that you can - make more battery-efficient. -
- - -