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Modeling Mobile Code Acceleration
in the Cloud
Huber Flores, Xiang Su, Vassilis Kostakos, Jukka Riekki, Eemil Lagerspetz,
Sasu Tarkoma, Pan Hui, Yong Li, Jukka Manner
Huber Flores
huber.flores@helsinki.fi
ICDCS 2017, Atlanta, USA.
Roadmap
• Motivation
• Background
– Mobile offloading
• Problem statement
• SDN mobile code accelerator
– Utility model
• Evaluation
– Real deployment in Amazon EC2
• Conclusions
Motivation
• Factors that influence QoE
– Energy
– Performance
CPU Battery
[IEEE Communications Magazine] Ickin, Selim, et al. "Factors influencing quality of experience of commonly used
mobile applications." IEEE Communications Magazine 50.4 (2012).
Mobile computational offloading
• Opportunistic augmentation of resources
[IEEE Communications Magazine] Flores, H., Hui, P., Tarkoma, S., Li, Y., Srirama, S., & Buyya, R. (2015). Mobile
code offloading: from concept to practice and beyond. IEEE Communications Magazine, 53(3), 80-88.
Mobile offloading models
1
2
3
WHAT IS THE PROBLEM?
Problem statement
Envisioned system
Network communication (3G/LTE)
[PMC] Flores, Huber, et al. "Social-aware hybrid mobile offloading." Pervasive and Mobile Computing
(2016).
SDN mobile code accelerator
SDN-ACCELERATOR
Modeling mobile code acceleration
Levels of code acceleration
Anomaly
SDN mobile code accelerator
EVALUATION
Mobile performance
Mobile performance
Workload management and fidelity
Accuracy
Workload management
Workload management
Application fidelity
Application fidelity
Conclusions
• Modeling code acceleration is a tough
challenge. Segregation of computational
infrastructure is not that obvious.
• Cloud can provision different levels of
code acceleration
– Opportunities for extending mobile hardware
lifespan
• The response time of mobile applications
can be moderated without further
instrumentation by using software define
techniques
QUESTIONS

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Modeling Mobile Code Acceleration in the Cloud