Presentation on theme: "Learning with Purpose Android OS Development What Lies Beyond SDK ! Bhanu Kaushik April 16 2013 PhD Student Department of Computer Science, University."— Presentation transcript:
Learning with Purpose Android OS Development What Lies Beyond SDK ! Bhanu Kaushik April PhD Student Department of Computer Science, University of Massachusetts, Lowell, MA.
Learning with Purpose Session I : Android OS – Introduction – Android Architecture – Download and Build – Android Code Organization – Implementation - Demo – SmartParcel – Session I – Summary Session II : Android SDK – All You Need to Know – Samples – Summary Session II Outline
Learning with Purpose Supports Millions of mobile devices in more than 190 countries. It's the largest installed base of any mobile platform. Introduction Well This is Not Necessary !
Learning with Purpose Session I Android OS
Learning with Purpose How to get Android Source code ? What Goes Where in Android ? How to Compile the code ? How can I create apps shipped with OS ? How to modify OS to offer new Services? How to use custom services in Apps ? Using all above, How can I provide solutions to real life problems ? Session I What will we learn !
Learning with Purpose Android Architecture
Learning with Purpose Binder: OpenBinder-based driver to facilitate inter-process communication (IPC) Android Power Management: a light weight power management driver optimized for embedded systems. Low Memory Killer: To kill off processes to free up memory as necessary. Logger: A light weight logging device used to capture system, radio, log data, etc. USB Gadget: Uses the USB function framework. Android/PMEM: Provide contiguous physical memory regions to userspace libraries that interact with the digital signal processor (DSP). Android Alarm: A driver which provides timers that can wake the device up from sleep. Android Architecture Enhancements over Linux 2.6
Learning with Purpose Available at, ml (Link)Link Requirements Linux or MacOS (Only Unix Based Systems) Python (python.org.)python.org.) GNU Make (gnu.org,)gnu.org,) JDK 6 if you wish to build Gingerbread or newer JDK 5 for Froyo or older. You can download both from (java.sun.com.)java.sun.com.) Git 1.7 or newer (git-scm.com.)git-scm.com.) Download and Build Environment Setup
Learning with Purpose Available at, ml (Link)Link Basic Steps Initialize Repo Choose Build Version (Link)Link Sync Repo Go Grab a coffee – This will take a while ! Download and Build Download
Learning with Purpose Instructions available at (Link)Link Basic Steps Setup Environment paths $ source build/envsetup.sh Choose Build Target using lunch $ lunch full-eng $ make –jN (j4, j8…j32) Chose N between 1 and 2 times the number of hardware threads on the computer being used for the build Go Grab a Lunch and a Nap – This will take a Loooong time! Download and Build Build
Learning with Purpose Android Code Organization Lets Dive into the Code
Learning with Purpose /bionic.: where the bionic library is. /build/: is the main make file system. /dalvik/: where the Dalvik VM and dex compiler is. /development/: integration to IDE like eclipse emacs etc. /device/: device specific code lies here. /external/:external libraries go here. Eg. Skia, jpeg, sqlite. /frameworks/:all the android frameworks go here. /hardware/: HAL, hardware abstract layer /prebuilt/: all the prebuilt code. Linux Kernel goes here. /packages/: all default apps shipped with system /out/: final output directory. Android Code Organization What we have seen so far !
Learning with Purpose Implementation - Demo
Learning with Purpose Create App using SDK (or otherwise) Turn off the Build Automatically in Eclipse. Copy the code to /packages/apps/YOUR_APP Create make file Template LOCAL_PATH:= $(call my-dir) include $(CLEAR_VARS) LOCAL_SRC_FILES := $(call my-dir/src/) LOCAL_PACKAGE_NAME := AppCall include $(BUILD_PACKAGE) Expose Application In /build/target/product/core.mk Add, YOUR_APP \ in product packages section Implementation System APP - Steps involved
Learning with Purpose Create service Aidl (Link)Link Add AIDL to Build Add Service Functionality Expose service Use the service Implementation Steps involved
Learning with Purpose Session I SmartParcel: A Collaborative Data Sharing Framework for Mobile Operating Systems Bhanu Kaushik ∗, Honggang Zhang †, Xinwen Fu ∗, Benyuan Liu ∗, Jie Wang ∗ ∗ Department of Computer Science, University of Massachusetts, Lowell, MA. † Department of Computer and Information Science, Fordham University, Bronx NY
Learning with Purpose Huge number of Mobile Devices such as Smartphones, Tablets, PDAs, portable media players etc. “About 6.2 billion users around the globe” – Ericsson, These devices support large number of Internet based applications. These Applications work on simple one-to-one client- server data distribution model. Introduction Results in: Increasing concerns about volume of global online digital content generated by these devices. Multi-fold increase in Network traffic originating from these devices “100 PetaByte/Month in 2007 to 700 PetaByte/Month in 2012”-Ericsson, Huge incumbent content availability and maintainability cost.
Learning with Purpose Major challenges faced by mobile Internet users Carrier enforced limited data plans, Unavailability of hardware (3G or LTE), Unavailability of access points, Service outages and Network and server overloads. Results in: Unavailability of application data to the users High service maintainability cost, to both the service providers and hosting servers. Motivation and Related Work Motivation
Learning with Purpose Proposed Solutions for data offloading Large Scale Alvarion, “Mobile data offloading for 3G and LTE networks.” Cisco, “Architecture for mobile data offload over Wi-Fi access networks.” Small Scale Han et. al. “Mobile data offloading through opportunistic communications and social participation” Lee et. al., “Mobile data offloading: How much can wifi deliver?” Unaddressed Issues: Entail huge changes in both, state of the art software and hardware technologies Do not take into account the heterogeneity of application data. Motivation and Related Work Related Work : Data Offloading
Learning with Purpose Delay-Tolerant Networks (DTN) Target the interoperability between and among challenged networks Familiar Strangers Coined by Stanley Milgram in 1972, “Individuals that regularly observe and exhibit some common patterns in their daily activities”. SmartParcel uses the idea of opportunistic connectivity and in-network storage and retransmission from DTN architecture to ensure data delivery among the nodes in a “Familiar Strangers” network set up. Motivation and Related Work Related Work: Opportunistic Data delivery and Familiar Strangers
Learning with Purpose Our Goal is to develop framework of a Mobile data offloading and Service Assurance scheme by encouraging collaborative data sharing among spatio-temporally co-existing mobile devices. Problem Definition SmartParcel Fig. 1 : Proposed SmartParcel Approach
Learning with Purpose Service Discovery Manager Data Transfer Manager Service Cache Manager Dynamic Cache Static Cache Network Interface Manager Service APIs Central Control Manager Architecture Components Fig. 2 : SmartParcel Service Architecture.
Learning with Purpose Service Discovery Manager: Identifies the available candidates for data transfer by broadcasting a “SYN” message periodically “SYN” packet contains meta-data about applications registered to SmartParcel. The meta-data is organized as a key value pair, i.e., (“ApplicationId, TimeStamp”). At receiver, based on the meta-data information it sets up a one-to-one connection Data Transfer Manager: Manages the data transfer. Can manage concurrent connections to multiple devices. To reduce the network overhead, sends data for multiple applications as one chunk. Architecture Component Details
Learning with Purpose Service Cache Manager: Service cache to store the application specific (heterogeneous ) data. Dynamic Cache In-memory cache for storing the applications meta-data information. Implemented as Hash Map with (Application Id, Timestamp) as key-value pairs. Static Cache Static cache for storing the actual application specific data. Maintained as SQLite database. Schema “Ap- plication Id (as string), Data (as blob), Time Stamp” Primary key : Application id and timestamp Flexibility to developer to assign “Time to live” and “Reset-Time” for the application data, end of day by default. Architecture Component Details
Learning with Purpose Central Control Manager: Manage the control from all components of the SmartParcel service. All components work under same instance for synchronous operation. Network Interface Manager: Internal service, responsible for managing network connections. Assists Service Discovery for identifying available devices on different network interfaces (3G, LTE, WiFi, BlueTooth etc.). Service APIs: Subscribe or unsubscribe to service Update app data Settings Sharing statistics etc. Architecture Component Details
Learning with Purpose Android SDK New set of permissions. SMP_ALL, SMP_BLUETOOTH, SMP_WIFI, SMP_NFC and SMP_BT_WIFI. Architecture Android and SmartParcel Group NameBlueToothWiFiNFCDISk-IO SMP_ALL √√√√ SMP_BLUETOOTH √ ×× √ SMP_WIFI × √ × √ SMP_NFC ×× √√ SMP_BT_WIFI √√ × √ Table 1 : Resources used in different permissions Fig. 3 : Integration of SmartParcel in Android framework Android OS Integrated in the “System Server” module. System Server is launched by Zygote. Zygote forks the SmartParcel service as a system service. Ensures system level privileges and independence from the application “context”.
Learning with Purpose MIT Reality Mining Data Set 100 unique devices, 500,000 hours, 9 months We use the Bluetooth encounters data. Simulation Setup EncountersActivity Maximum65901 Minimum24 Mean4243 Std. Dev Fig 6 : Hourly Variation of Device Encounters. Fig 5 : Distribution of Device Encounters. Fig 4 : Distribution of Active Devices Per Day. Table 2 : Data Set Description Data Set
Learning with Purpose Data Refresh Rate (DRR) : The frequency with which the data is being refreshed. Allowed Server Connections (ASC) : Number of devices allowed to get data from server on each day. User Participation Probability (UPP) : The Probability of user acting selfish, i.e., limiting its participation by only receiving data and not sending data We measure the Data Availability Ratio (DAR) Simulation Setup Setup Parameters
Learning with Purpose User Participation Probability (UPP) = 100% Data Refresh Rate (DRR) =1 Refresh interval Results Effect of user’s social activity level Fig 6 : Effect of ASC on DAR over the Day, when ASC = 1 Fig 7 : Effect of ASC on DAR over the Day, when ASC = 30 Fig 8 : Effect of ASC on DAR, when ASC =1 to 75 devices.
Learning with Purpose User Participation Probability (UPP) = 100% Data Refresh Rate (DRR) = 2 Refresh intervals, 12:00am -11:59am and 12:00am-07:59am Results Effect of Data Refresh Rate (DRR) Fig 9 : Variation of Data Availability Ratio (DAR) with Data Refresh Rate (DRR) when DRR = 2 and Refresh Interval 12:00 am - 11:59 am. Fig 10 : Variation of Data Availability Ratio (DAR) with Data Refresh Rate (DRR) when DRR = 2 and Refresh Interval 12:00pm - 11:59pm.
Learning with Purpose User Participation Probability (UPP) = 100% Data Refresh Rate (DRR) = 3 Refresh intervals. Results Effect of Data Refresh Rate (DRR) Fig 11 : Refresh Interval 12:00am-07:59am. Fig 12 : Refresh Interval 08:00am-03:59pm. Fig 13 : Refresh Interval 04:00pm-11:59pm.
Learning with Purpose User Participation Probability (UPP) = 10%, 20%, 50% and 90%. Data Refresh Rate (DRR) = 1 Refresh interval Allowed Server Connections(ASC) = 1 to 90 devices. Results Effect of Selfishness Fig 14 : Variation of Data Availability Ratio with User Participation Probability(UPP) and Allowed Server Connections(ASC). * (*Median of 1000 Simulation runs)
Learning with Purpose “SmartParcel” - A novel approach for Data sharing among co-existing and co-located devices is presented. “One for all”, multiple incentive system for application developers, Internet service providers and application data providers (eg. cloud services) with collateral benefits for the consumer itself. We discussed the Design and implementation “SmartParcel” in Android. Implementation in android framework dictates the feasibility of the architecture. Flexibility of design ensures integration in almost every existing mobile operating system. In the future, we intend to investigate the scalability and performance issues encountered on real devices. Conclusions and Future Work
Learning with Purpose Android Source code Download Code Organization in Android- What Goes Where. Compiling the code. Creating Apps shipped with OS Modifying OS to offer new Services Using custom services in Apps Demo of Sample project - SmartParcel Session I : Summary
Learning with Purpose Session II Android SDK
Learning with Purpose Getting the right tools ! Basic Android app Services in Apps AsyncTask -Performance Enhancement Sound Recoder demo Session II What will we learn !
Learning with Purpose Implementation - Demo
Learning with Purpose Basic Android app Services in Apps AsyncTask -Performance Enhancement Sound Recorder demo Session II Summary !