Mobile devices have become an integral part of our daily lives. Applications running on these devices may avail storage and compute resources from the cloud(s). Further, a mobile device may also connect to heterogeneous access networks (HANs) such as WiFi and LTE to provide ubiquitous network connectivity to mobile applications. These devices have limited resources (compute, storage and battery) that may lead to service disruptions. In this context, mobile cloud computing enables offloading of computing and storage to the cloud. However, applications running on mobile devices using clouds and HANs are prone to unpredictable cloud workloads, network congestion and handoffs. To run these applications efficiently the mobile device requires the best possible cloud and network resources while roaming in HANs. This paper proposes, develops and validates a novel system called M2C2 which supports mechanisms for: i.) multihoming, ii.) cloud and network probing, and iii.) cloud and network selection. We built a prototype system and performed extensive experimentation to validate our proposed M2C2. Our results analysis shows that the proposed system supports mobility efficiently in mobile cloud computing. Paper can be downloaded from: http://karanmitra.me/wp-content/uploads/2015/02/MitraetalLTUWCNC_Preprint2015.pdf

1. M2C2: A Mobility Management
System for Mobile Cloud Computing
Karan Mitra, Saguna, Christer Åhlund and Daniel Granlund
Luleå University of Technology
Sweden
karan.mitra@ltu.se
https://karanmitra.me
28 May 2015

2. Contents
• Introduction
• Challenges
• M2C2: A Mobility Management System for
Mobile Cloud Computing
• Results
• Conclusion and Future Work

3. Introduction
• Cloud Computing
– Shared pool of virtual resources (CPU, storage and
network)
– No long-term contracts, pay-as-you-go model
• Internet/Cloud/… of Things
– Billions of objects (devices, sensors, Web services, etc.)
connected to the Internet
• Massive amounts of data
– Accelerated by cloud computing
• Data storage, processing and visualization
• Mobile Computing
– Data consumption and production

4. Challenges
• End user mobile devices and sensors
– Limited compute, storage and battery capacity
– Network: intermittent connectivity, throughput, delay &
jitter
– Variability: both mobile networks and clouds
• Mobile Cloud Computing (MCC)
– Offload computation and storage to the cloud
– Mobility
Smart healthcare Emergency management

5. M2C2: A Mobility Management System for
Mobile Cloud Computing
• Aim: to select the best cloud and the best
network while users roam in heterogeneous
access networks
• Proposed and developed M2C2
– Multihoming: being able to connect to several access
networks together (e.g., WiFi and LTE)
– Cloud and network probing mechanisms
– Cloud and network selection mechanisms

6. • Comprise several components:
– Anchor Point
• Cloud and network awareness
– Cloud Probing Service
– Cloud Ranking Service
• Cloud probing and ranking: RESTful Webservices
– Home Agent
• Network path probing via M-MIP tunnel
– Mobile Node
• Network selection using Relative Network Load metric
M2C2: Mobility Management in Mobile
Cloud Computing

7. M2C2: Mobility Management in Mobile
Cloud Computing
M2C2: system architecture

8. One Application Scenario
K. Mitra, Saguna and C. Ahlund, “A mobile cloud computing system for emergency management,” Cloud
Computing, IEEE, vol. 1, no. 4, pp. 30–38, 2014.

9. • Cloud Service Selection via Cloud Ranking
Service
– Simple Additive Weighting (SAW)
• Network Selection
– Relative Network Load metric
M2C2: Mobility Management in Mobile
Cloud Computing

10. M2C2: Mobility Management in Mobile
Cloud Computing

11. Results Analysis
• Prototype implementation and experimentation
– Activity recognition application
– Significant software engineering effort!
• Experiment 1: local clouds vs. public clouds
– Computation should be offloaded to local clouds using
WiFi
0 200 400 600 800 1000 1200
0
0.1
0.2
0.3
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0.5
0.6
0.7
0.8
0.9
1
Latency (milliseconds)
Cumulativeprobability
Case A: Local cloud and 3G network
Case B: Local cloud and WiFi network
Case C: Public cloud and 3G network
Case D: Public cloud and WiFi network

12. Results Analysis
• Experiment 2: Cloud and Network Selection
– Cloud selection

13. Results Analysis
• Experiment 3: Impact of mobility
– Mobile node roaming in WiFi and 3G networks
– Seamless handoffs with no packet loss
– Activity recognition continued successfully
• Variation in latency based on access network

14. Conclusion and Future Work
• Proposed, developed and validated M2C2
– A novel system for mobility management in mobile
cloud computing
• Multihoming
• Cloud and network probing
• Cloud and network selection
Future Work:
• Extend and validate M2C2 for a smart city
environment:
– Power consumption on mobile devices
– Extend the metrics
– Real-world case studies

15. Thank you for your attention!
Questions?