This document presents ENDA, a proposed solution for embracing network inconsistency in mobile cloud computing. ENDA is a three-tier architecture that aims to make the most energy efficient offloading decisions for smartphones by selecting the optimal Wi-Fi access point based on predicted user trajectory, workload balancing among cloudlets, and minimizing communication overhead. Preliminary results from a GUI-based simulation show ENDA's ability to choose the most energy efficient Wi-Fi network path according to a user's predicted movement. The solution seeks to address issues with current offloading approaches and overcome constraints of limited cloudlet resources and Wi-Fi coverage.

1. ENDA: Embracing Network Inconsistency for
Dynamic Application Offloading
in Mobile Cloud Computing
Jiwei Li Kai Bu Xuan Liu Bin Xiao
The Hong Kong Polytechnic University
Presenter: Jiwei Li

2. Outline
• Background & Problem
• Proposed Solution
• Preliminary Results
• Conclusion

3. Outline
• Background & Problem
• Proposed Solution
• Preliminary Results
• Conclusion

4. Mobile Cloud Computing
• Applications
– Apple’s iCloud, Dropbox
• Technical problems
– MCC architecture & infrastructure
– Network connectivity
– Energy efficiency
• One important research topic - offloading

5. Offloading Strategy
• Previous work
– MAUI, CloneCloud, Odessa, COMET

6. 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

7. 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

8. Uninvestigated Issues in Offloading
• Offloading at mobile environments
• Balancing workloads among multiple cloudlets
Our research is focused on
offloading to cloudlets through Wi-Fi
at mobile environments.

9. A B
C
D
A Motivating Example

10. 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.

11. Our Studied Problems
• How to predict user’s trajectory?
• How to select Wi-Fi access points (AP)?
• How to balance workload among cloudlets?

12. 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 to
select the most energy-efficient Wi-Fi access point,
taking user mobility and server load into account.

13. Outline
• Background & Problem
• Proposed Solution
• Preliminary Results
• Conclusion

14. 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.

15. A Real Scenario

16. ENDA
• Three-tier architecture Design
– Cloud
– Cloudlet
– Smartphone
• Objective:
– Make the most energy efficient offloading
decision

17. Clouds
CloudletsSmartphones
VM on cloudlets
Profilers
Wi-Fi
2G/3G
WAN
User Track
Prediction
Wi-Fi AP
Distribution
and Status
Wi-Fi AP
Selector
GPS
Runtime System
Wi-Fi
Adapter
FINAL DECISION
OFFLOADING
INPUT INPUTREPORT
REPORT
APPINFO
Our work will be focused on

18. Advantages
• Minimize end-to-end communication
overheads
• Exempt smartphones from complex
computation of making decisions
• Improve energy efficiency for offloading

19. Demo Scenario
Predicted user track
(will be pruned based on
app 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

20. Outline
• Background & Problem
• Proposed Solution
• Preliminary Results
• Conclusion

21. GUI-based Simulation
Add routers
Add walking path
Calculate effective path

22. Simulation Results
Wi-Fi B Wi-Fi A Wi-Fi C

23. Outline
• Background & Problem
• Proposed Solution
• Preliminary Results
• Conclusion

24. 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

25. Thank you!

26. Q&A