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