slides - Computer Science - University of Massachusetts Lowell

Report
SmartParcel: A Collaborative Data
Sharing Framework for Mobile
Operating Systems
Bhanu Kaushik∗
Xinwen Fu∗
Honggang Zhang†
Benyuan Liu∗
∗Department
of Computer Science,
University of Massachusetts, Lowell, MA.
†Department
of Computer and Information Science,
Fordham University, Bronx NY
Learning with Purpose
Jie Wang∗
Outline
Introduction
Motivation and Related Work
Problem Definition
Architecture
Simulation Setup
Results
Conclusions and Future Work
Learning with Purpose
Outline
Introduction
Motivation and Related Work
Problem Definition
Architecture
Simulation Setup
Results
Conclusions and Future Work
Learning with Purpose
Introduction
Huge number of Mobile Devices such as Smartphones,
Tablets, PDAs, portable media players etc.
“About 6.2 billion users around the globe” – Ericsson,
2012.
These devices support large number of Internet based
applications.
These Applications work on simple one-to-one clientserver data distribution model.
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, 2012.
•
Learning with Purpose
Huge incumbent content availability and
maintainability cost.
Outline
Introduction
Motivation and Related Work
Problem Definition
Architecture
Simulation Setup
Results
Conclusions and Future Work
Learning with Purpose
Motivation and Related Work
Motivation
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.
Learning with Purpose
Motivation and Related Work
Related Work : Data Offloading
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.
Learning with Purpose
Motivation and Related Work
Related Work: Opportunistic Data delivery and Familiar
Strangers
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.
Learning with Purpose
Outline
Introduction
Motivation and Related Work
Problem Definition
Architecture
Simulation Setup
Results
Conclusions and Future Work
Learning with Purpose
Problem Definition
SmartParcel
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.
Fig. 1 : Proposed SmartParcel Approach
Learning with Purpose
Outline
Introduction
Motivation and Related Work
Problem Definition
Architecture
Simulation Setup
Results
Conclusions and Future Work
Learning with Purpose
Architecture
Components
Service Discovery Manager
Data Transfer Manager
Service Cache Manager
• Dynamic Cache
• Static Cache
Network Interface Manager
Service APIs
Central Control Manager
Fig. 2 : SmartParcel Service Architecture.
Learning with Purpose
Architecture
Component Details
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.
Learning with Purpose
Architecture
Component Details
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.
Learning with Purpose
Architecture
Component Details
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.
Learning with Purpose
Architecture
Android and SmartParcel
Group Name
BlueTooth
WiFi
NFC
DISk-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 SDK
•
•
New set of permissions.
SMP_ALL, SMP_BLUETOOTH, SMP_WIFI,
SMP_NFC and SMP_BT_WIFI.
Android OS
•
•
•
•
Learning with Purpose
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”.
Outline
Introduction
Motivation and Related Work
Problem Definition
Architecture
Simulation Setup
Results
Conclusions and Future Work
Learning with Purpose
Simulation Setup
Data Set
MIT Reality Mining Data Set
• 100 unique devices, 500,000 hours, 9
months
• We use the Bluetooth encounters data.
Table 2 : Data Set Description
Encounters
Activity
Maximum
65
901
Minimum
2
4
Mean
4
243
Std. Dev.
8.67
133
Fig 5 : Distribution of Device Encounters.
Learning with Purpose
Fig 4 : Hourly Variation of Device Encounters.
Fig 6 : Distribution of Active Devices Per Day.
Simulation Setup
Setup Parameters
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)
Learning with Purpose
Outline
Introduction
Motivation and Related Work
Problem Definition
Architecture
Simulation Setup
Results
Conclusions and Future Work
Learning with Purpose
Results
Effect of user’s social activity level
User Participation Probability
(UPP) = 100%
Data Refresh Rate (DRR) =1
Refresh interval
Fig 6 : Effect of ASC on DAR over the Day, when ASC = 1
Fig 8 : Effect of ASC on DAR , when ASC =1 to 75 devices.
Learning with Purpose
Fig 7 : Effect of ASC on DAR over the Day, when ASC = 30
Results
Effect of Data Refresh Rate (DRR)
User Participation Probability (UPP) = 100%
Data Refresh Rate (DRR) = 2 Refresh intervals,
12:00am -11:59am and 12:00pm-11:59pm
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.
Learning with Purpose
Fig 10 : Variation of Data Availability Ratio (DAR) with Data Refresh
Rate (DRR) when DRR = 2 and Refresh Interval 12:00pm - 11:59pm.
Results
Effect of Data Refresh Rate (DRR)
User Participation Probability
(UPP) = 100%
Data Refresh Rate (DRR) = 3
Refresh intervals.
Fig 12 : Refresh Interval 08:00am-03:59pm.
Fig 11 : Refresh Interval 12:00am-07:59am.
Learning with Purpose
Fig 13 : Refresh Interval 04:00pm-11:59pm.
Results
Effect of Selfishness
User Participation Probability (UPP) = 10%, 20%, 50% and 90%.
Data Refresh Rate (DRR) = 1 Refresh interval
Allowed Server Connections(ASC) = 1 to 90 devices.
Fig 14 : Variation of Data Availability Ratio with User Participation
Probability(UPP) and Allowed Server Connections(ASC).
Learning with Purpose
(*Median of 1000 Simulation runs)
Outline
Introduction
Motivation and Related Work
Problem Definition
Architecture
Simulation Setup
Results
Conclusions and Future Work
Learning with Purpose
Conclusions and Future Work
“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.
Learning with Purpose
Thank You !
Questions ?
Learning with Purpose

similar documents