This document proposes MapCloud, a framework for optimal service allocation in mobile cloud computing. It uses a 2-tier cloud architecture with local and public clouds to balance factors like delay, cost, and scalability. MapCloud models mobile applications and quality of service using location-time workflows. It introduces an algorithm called MuSIC that allocates services for single-user or group applications in a way that optimizes factors like power consumption and delay based on user mobility patterns. The document outlines MapCloud's features and provides diagrams of its architecture and workflow.

1. 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.

2. Prologue
2
MapCloud:
Optimal Service Allocation for
Mobile Users
Mobile Users
Criteria: Low
price, Low delay,
proximity
Mobile User
Behavior: Mobility
patterns, …
XaaS:
Computation,
Storage,
Bandwidth,…

3. 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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4. Location-Time Workflow
4
t1 t2 t4t3 tN
l2
l1
l3
ln
W1
Wk+1
Wk
Wj+1
Wj
Location-Time Workflow

5. 5
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

6. 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
service
usage pattern and
save it
As location-Time
workflow .
Recommended
Web Services
with their URLs.
User Web Service
Usage 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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7. MapCloud Prototype Snapshots
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8. 8

9. 9

10. 10

11. 11

12. 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.
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