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Adaptive Code Offloading for Mobile
Cloud Applications
Exploiting Fuzzy Sets and Evidence-based Learning
Huber Flores
huber@ut.ee
MCS'13, Taipei, Taiwan 1
Outline
• Background
• Problem statement
• Proposed solution
• Conclusions
MCS'13, Taipei, Taiwan 2
Background
• Mobile cloud computing
– Augmented functionality
– Extended battery life
– Increased performance
• Task delegation
– Mobile Cloud Middleware (Flores et al., 2011)
• Zompopo (Srirama et al., 2011)
• Code offloading
– Small data size is transmitted which requires intensive
computational processing (Kumar et al., 2010)
– Online/Offline
MCS'13, Taipei, Taiwan 3
Background
MCS'13, Taipei, Taiwan 4
Background
• MAUI (.Net)
– Code annotations (Cuervo et al., 2010)
• CloneCloud
– Code profilers (Chun et al., 2011)
• Other frameworks
– Own framework (Similar to MAUI, but Java-based)
MCS'13, Taipei, Taiwan 5
What is the problem?
Besides, scalability…
MCS'13, Taipei, Taiwan 6
Problem statement
• Code offloading may also fail?
– Runtime analysis should be encouraged (Ra et al.,
2011)
– Some code cannot be profiled (e.g. REST)
– Should it be a local decision of global inference
• Cloud infrastructure
MCS'13, Taipei, Taiwan 7
Problem statement
• Is Mobile Cloud taking full advantage of Cloud
Computing?
• Code offloading for next generation mobile
devices?
e.g., Samsung Galaxy S, S2, S3, S4….
(How to optimize the offloading decision process?)
MCS'13, Taipei, Taiwan 8
Proposed solution
• Offloading from a different perspective
– “Offloading is a global learning process rather than
local decision process“
• How it can learn?
– Analysis of code offloading traces which are generated
by the massive amount of devices that connect to
cloud
“EMCO: Evidence-based mobile code offloading“
MCS'13, Taipei, Taiwan 9
Evidence-based mobile code offloading
MCS'13, Taipei, Taiwan 10
Evidence-based mobile code offloading
MCS'13, Taipei, Taiwan 11
Evidence-based mobile code offloading
• Crisp Sets
– Mobile parameters
– Cloud parameters
– Others
• Linguistic variables
– bandwith
• Fuzzy Sets
– speed_slow,
– speed_normal
– speed_high
• Variable to control
– Offloading
• Rules
– if speed_high AND data_small then remote
– If speed_low AND data_medium the local
MCS'13, Taipei, Taiwan 12
Preliminary results
• Use cases
– Mobile components scheduling
– Back-end allocation (Clone)
– Data-intensive REST
– Others
MCS'13, Taipei, Taiwan 13
Preliminary results
Bandwidth = speed_high
Data size = data_small
Instance load = cpu_highOffload = 90%
Not to offload = 65%
MCS'13, Taipei, Taiwan 14
Preliminary results
Bandwidth = speed_high
Data size = data_small
Instance load = cpu_normalOffload = 90%
Offload = 83%
MCS'13, Taipei, Taiwan 15
Preliminary results
Bandwidth = speed_high
Data size = data_medium
Instance load = cpu_normal
Not to offload = 70%
Not to offload = 75%
Instance type = micro
Instance cores = 1
Battery level = low
MCS'13, Taipei, Taiwan 16
Preliminary results
Bandwidth = speed_high
Data size = data_medium
Instance load = cpu_normal
Offload = 75%
Instance type = medium
Instance cores = 2Not to offload = 82%
Battery level = low
MCS'13, Taipei, Taiwan 17
Preliminary results
MCS'13, Taipei, Taiwan 18
Conclusions and future research
• Cloud analysis may periodically empower
mobile components with knowledge. (Cloud is
expert and handset asks for its expertise)
– e.g. code offloading
• Offloading as a learning process may grant the
cloud with self-adaptive behavior.
• We are exploring some other strategies for the
analysis of code offloading traces.
MCS'13, Taipei, Taiwan 19
THANK YOU FOR LISTENING…
MCS'13, Taipei, Taiwan 20

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Adaptive Code Offloading for Mobile Cloud Applications

  • 1. Adaptive Code Offloading for Mobile Cloud Applications Exploiting Fuzzy Sets and Evidence-based Learning Huber Flores huber@ut.ee MCS'13, Taipei, Taiwan 1
  • 2. Outline • Background • Problem statement • Proposed solution • Conclusions MCS'13, Taipei, Taiwan 2
  • 3. Background • Mobile cloud computing – Augmented functionality – Extended battery life – Increased performance • Task delegation – Mobile Cloud Middleware (Flores et al., 2011) • Zompopo (Srirama et al., 2011) • Code offloading – Small data size is transmitted which requires intensive computational processing (Kumar et al., 2010) – Online/Offline MCS'13, Taipei, Taiwan 3
  • 5. Background • MAUI (.Net) – Code annotations (Cuervo et al., 2010) • CloneCloud – Code profilers (Chun et al., 2011) • Other frameworks – Own framework (Similar to MAUI, but Java-based) MCS'13, Taipei, Taiwan 5
  • 6. What is the problem? Besides, scalability… MCS'13, Taipei, Taiwan 6
  • 7. Problem statement • Code offloading may also fail? – Runtime analysis should be encouraged (Ra et al., 2011) – Some code cannot be profiled (e.g. REST) – Should it be a local decision of global inference • Cloud infrastructure MCS'13, Taipei, Taiwan 7
  • 8. Problem statement • Is Mobile Cloud taking full advantage of Cloud Computing? • Code offloading for next generation mobile devices? e.g., Samsung Galaxy S, S2, S3, S4…. (How to optimize the offloading decision process?) MCS'13, Taipei, Taiwan 8
  • 9. Proposed solution • Offloading from a different perspective – “Offloading is a global learning process rather than local decision process“ • How it can learn? – Analysis of code offloading traces which are generated by the massive amount of devices that connect to cloud “EMCO: Evidence-based mobile code offloading“ MCS'13, Taipei, Taiwan 9
  • 10. Evidence-based mobile code offloading MCS'13, Taipei, Taiwan 10
  • 11. Evidence-based mobile code offloading MCS'13, Taipei, Taiwan 11
  • 12. Evidence-based mobile code offloading • Crisp Sets – Mobile parameters – Cloud parameters – Others • Linguistic variables – bandwith • Fuzzy Sets – speed_slow, – speed_normal – speed_high • Variable to control – Offloading • Rules – if speed_high AND data_small then remote – If speed_low AND data_medium the local MCS'13, Taipei, Taiwan 12
  • 13. Preliminary results • Use cases – Mobile components scheduling – Back-end allocation (Clone) – Data-intensive REST – Others MCS'13, Taipei, Taiwan 13
  • 14. Preliminary results Bandwidth = speed_high Data size = data_small Instance load = cpu_highOffload = 90% Not to offload = 65% MCS'13, Taipei, Taiwan 14
  • 15. Preliminary results Bandwidth = speed_high Data size = data_small Instance load = cpu_normalOffload = 90% Offload = 83% MCS'13, Taipei, Taiwan 15
  • 16. Preliminary results Bandwidth = speed_high Data size = data_medium Instance load = cpu_normal Not to offload = 70% Not to offload = 75% Instance type = micro Instance cores = 1 Battery level = low MCS'13, Taipei, Taiwan 16
  • 17. Preliminary results Bandwidth = speed_high Data size = data_medium Instance load = cpu_normal Offload = 75% Instance type = medium Instance cores = 2Not to offload = 82% Battery level = low MCS'13, Taipei, Taiwan 17
  • 19. Conclusions and future research • Cloud analysis may periodically empower mobile components with knowledge. (Cloud is expert and handset asks for its expertise) – e.g. code offloading • Offloading as a learning process may grant the cloud with self-adaptive behavior. • We are exploring some other strategies for the analysis of code offloading traces. MCS'13, Taipei, Taiwan 19
  • 20. THANK YOU FOR LISTENING… MCS'13, Taipei, Taiwan 20