ENDA: Embracing Network Inconsistency for Dynamic Application Offloading
in Mobile Cloud ComputingJiwei Li Kai Bu Xuan Liu Bin Xiao
The Hong Kong Polytechnic University
Presenter: Jiwei Li
Outline
• Background & Problem• Proposed Solution• Preliminary Results• Conclusion
Outline
• Background & Problem• Proposed Solution• Preliminary Results• Conclusion
Mobile Cloud Computing
• Applications– Apple’s iCloud, Dropbox
• Technical problems – MCC architecture & infrastructure– Network connectivity– Energy efficiency
• One important research topic - offloading
Offloading Strategy
• Previous work– MAUI, CloneCloud, Odessa, COMET
Offload to Cloud
Compute-intensive applications
Computed results
High latency (100-300ms)Limited bandwidth (386 Kbs to 3.6 Mbs)High energy consumption
Nearly unlimited resources
Offload to Cloudlet
Compute-intensive applications
Computed results
Low latency (23-50ms)High bandwidth (54 Mbs)
Limited coverage of Wi-Fi (20-100m)Resource constraint
Uninvestigated Issues in Offloading
• Offloading at mobile environments
• Balancing workloads among multiple cloudlets
Our research is focused onoffloading to cloudlets through Wi-Fiat mobile environments.
A B
C
D
A Motivating Example
Re-connection Matters
• Re-connection includes– Scanning– Connecting– Assigning IP and network ID
• Takes long time (1-12s)• Consumes additional power
Reducing re-connection times means increasing energy efficiency.
Our Studied Problems
• How to predict user’s trajectory?• How to select Wi-Fi access points (AP)?• How to balance workload among cloudlets?
Problem Formulation
• Minimize: – Communication overheads during offloading at
mobile environments• Must satisfy requirements:– App-specific network latency– App-specific response time
To put it simply, we aim toselect the most energy-efficient Wi-Fi access point,taking user mobility and server load into account.
Outline
• Background & Problem• Proposed Solution• Preliminary Results• Conclusion
Answering a few questions …
• Is it feasible to deploy cloudlets at large scale?• Bind current public Wi-Fi hotspots with cloudlets.
•How do we overcome resource constraints on cloudlets?• Adopt workload balance management mechanism among
participating cloudlets.
•How do we conquer Wi-Fi’s limited coverage range issue?• Propose mobility-aware Wi-Fi AP selection scheme.
A Real Scenario
ENDA
• Three-tier architecture Design– Cloud– Cloudlet– Smartphone
• Objective:– Make the most energy efficient offloading decision
Clouds
CloudletsSmartphones
VM on cloudlets
Profilers
Wi-Fi
2G/3
G
WAN
User Track Prediction
Wi-Fi AP Distribution and Status
Wi-Fi AP Selector
GPS
Runtime System
Wi-Fi Adapter
FINAL DECISION
OFFLOADING
INPUT INPUTRE
PORT
REPO
RT
APP
INFO
Our work will be focused on
Advantages
• Minimize end-to-end communication overheads
• Exempt smartphones from complex computation of making decisions
• Improve energy efficiency for offloading
Demo Scenario
Predicted user track(will be pruned based onapp info & network conditions)
Effective routes:N1 -> (S, A)N2 -> (S, B)N3 -> (S, D)N4 -> (C, D)
ENDA chooses the most energy-efficient Wi-Fi AP according to the specific predicted track
Start offloading at location S
Outline
• Background & Problem• Proposed Solution• Preliminary Results• Conclusion
GUI-based Simulation
Add routers
Add walking path
Calculate effective path
Simulation Results
Wi-Fi B Wi-Fi A Wi-Fi C
Outline
• Background & Problem• Proposed Solution• Preliminary Results• Conclusion
Conclusion
• ENDA– Difference from previous work– Minimize communication overheads– Potential to apply to real offloading systems
• Future work– Thorough mathematical analysis– Implementation– More complex scenarios
Thank you!
Q&A
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