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Learning with Purpose SmartParcel: A Collaborative Data Sharing Framework for Mobile Operating Systems Bhanu Kaushik ∗ Honggang Zhang † Xinwen Fu ∗ Benyuan.

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Presentation on theme: "Learning with Purpose SmartParcel: A Collaborative Data Sharing Framework for Mobile Operating Systems Bhanu Kaushik ∗ Honggang Zhang † Xinwen Fu ∗ Benyuan."— Presentation transcript:

1 Learning with Purpose 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

2 Learning with Purpose Introduction Motivation and Related Work Problem Definition Architecture Simulation Setup Results Conclusions and Future Work Outline

3 Learning with Purpose Introduction Motivation and Related Work Problem Definition Architecture Simulation Setup Results Conclusions and Future Work Outline

4 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.

5 Learning with Purpose Introduction Motivation and Related Work Problem Definition Architecture Simulation Setup Results Conclusions and Future Work Outline

6 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

7 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

8 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

9 Learning with Purpose Introduction Motivation and Related Work Problem Definition Architecture Simulation Setup Results Conclusions and Future Work Outline

10 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

11 Learning with Purpose Introduction Motivation and Related Work Problem Definition Architecture Simulation Setup Results Conclusions and Future Work Outline

12 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.

13 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

14 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 “Application 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

15 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

16 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”.

17 Learning with Purpose Introduction Motivation and Related Work Problem Definition Architecture Simulation Setup Results Conclusions and Future Work Outline

18 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 4 : Hourly Variation of Device Encounters. Fig 5 : Distribution of Device Encounters.Fig 6 : Distribution of Active Devices Per Day. Table 2 : Data Set Description Data Set

19 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

20 Learning with Purpose Introduction Motivation and Related Work Problem Definition Architecture Simulation Setup Results Conclusions and Future Work Outline

21 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.

22 Learning with Purpose User Participation Probability (UPP) = 100% Data Refresh Rate (DRR) = 2 Refresh intervals, 12:00am -11:59am and 12:00pm-11:59pm 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.

23 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.

24 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)

25 Learning with Purpose Introduction Motivation and Related Work Problem Definition Architecture Simulation Setup Results Conclusions and Future Work Outline

26 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

27 Learning with Purpose Thank You ! Questions ?


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