on optimal and fair service allocation in mobile cloud computing

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On Optimal and Fair Service Allocation in Mobile Cloud Computing (short version) Reza Rahimi SCHOOL OF INFORMATION AND COMPUTER SCIENCE, University of California, Irvine, CA.

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Page 1: On Optimal and Fair Service Allocation in Mobile Cloud Computing

On Optimal and Fair Service Allocation in Mobile Cloud

Computing(short version)

Reza RahimiSCHOOL OF INFORMATION AND

COMPUTER SCIENCE, University of California,

Irvine, CA.

Page 2: On Optimal and Fair Service Allocation in Mobile Cloud Computing

Prologue

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MapCloud:

Optimal Service Allocation for Mobile Users

Mobile Users Criteria: Low

price, Low delay, proximity

Mobile User Behavior: Mobility

patterns, …

XaaS: Computation,

Storage, Bandwidth,…

Page 3: On Optimal and Fair Service Allocation in Mobile Cloud Computing

MapCloud Features

• It uses 2-Tiere Cloud architecture as an efficient platform for mobile cloud computing.

• Proposing Location-Time Workflow as an efficient Framework for modeling mobile applications and their QoS (power consumption, delay and price ) based on cloud.

• Proposing an efficient service allocation algorithm called MuSIC for different classes of mobile application like single user application or group-based and collaborative application.

• Finally, it provides an Abstraction and Generic framework for efficient service allocation in mobile cloud computing!

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Page 4: On Optimal and Fair Service Allocation in Mobile Cloud Computing

Location-Time Workflow

4

t1 t2 t4t3 tN

l2

l1

l3

ln

W1

Wk+1

Wk

Wj+1

Wj

Location-Time Workflow

• It could be formally defined as:,….,

Page 5: On Optimal and Fair Service Allocation in Mobile Cloud Computing

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Tier 2: Local Cloud

(+) Low Delay, Low Power,

Almost Free (-) Not Scalable and

Elastic

Tier 1: Public Cloud (+) Scalable and Elastic

(-) Price, Delay

Wi-Fi Access Point

3G Access Point

RTT: ~290ms

RTT: ~80ms

2-Tier Cloud Architecture

Page 6: On Optimal and Fair Service Allocation in Mobile Cloud Computing

Mobile User

Logger DB and QoS Analyzer

Location-Time

Analytics

QoS-Aware Service

Scheduler

2-Tier QoS-Aware Cloud Registry

2-Tier Cloud Service

Pool

MapCloud Sequence Diagram

Extract user web serviceusage pattern and save it As location-Time workflow .

Recommended Web Services

with their URLs.

User Web ServiceUsage Log with Experienced QoS .

Run MuSIC or Use MuSIC Result on previous collected data from mobile users to find best service allocation.

It analyzes user experienced QoS and updates cloud registry, Not necessarily in this time period, could be run independently!

User Logs like:Web Service Usage, Experienced QoS like:delay, power consumption

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MapCloud Prototype Snapshots

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References

1. M. Reza. Rahimi, Nalini Venkatasubramanian, Athanasios Vasilakos, "MuSIC: On Mobility-Aware Optimal Service Allocation in Mobile Cloud Computing", In the IEEE 6th International Conference on Cloud Computing, (Cloud 2013), July 2013, Silicon Valley, CA, USA.

2. M. Reza. Rahimi, Nalini Venkatasubramanian, Sharad Mehrotra and Athanasios Vasilakos, "MAPCloud: Mobile Applications on an Elastic and Scalable 2-Tier Cloud Architecture", In the 5th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2012), Nov 2012, USA.

3. M. Reza. Rahimi, Nalini Venkatasubramania "Exploiting an Elastic 2-Tiered Cloud Architecture for Rich Mobile Applications", poster in the IEEE/ACM 13th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM 2012), June 2012, USA.

4. M. Reza. Rahimi, Nalini Venkatasubramania "Cloud Based Framework for Rich Content Mobile Applications", poster in the IEEE/ACM 11th International Symposium on Cluster, Cloud and Grid Computing (CCGRID2011), Newport Beach, May 2011, USA.

5. Shivajit Mohapatra, M. Reza. Rahimi, Nalini Venkatasubranian "Power-Aware Middleware for Mobile Applications", Chapter 10 of the Handbook of Energy-Aware and Green Computing, ISBN: 978-1-4398-5040-4, Chapman & Hall/CRC, 2011. 12