The document proposes an adaptive code offloading framework called EMCO that uses evidence-based learning. It aims to optimize the code offloading decision process by treating offloading as a global learning process based on analysis of offloading traces from many mobile devices, rather than a local decision. The framework uses fuzzy sets and linguistic variables to represent parameters like bandwidth, and rules to determine whether to offload code based on these variables and cloud/mobile conditions. Preliminary results show the percentage of times code is offloaded or not for different combinations of bandwidth, data size, instance load, and other factors. The conclusions discuss how periodic cloud analysis can empower mobile devices with knowledge gained from traces to allow self-adaptive off

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
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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
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4. Background
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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)
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6. What is the problem?
Besides, scalability…
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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
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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?)
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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“
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10. Evidence-based mobile code offloading
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11. Evidence-based mobile code offloading
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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
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13. Preliminary results
• Use cases
– Mobile components scheduling
– Back-end allocation (Clone)
– Data-intensive REST
– Others
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14. Preliminary results
Bandwidth = speed_high
Data size = data_small
Instance load = cpu_highOffload = 90%
Not to offload = 65%
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15. Preliminary results
Bandwidth = speed_high
Data size = data_small
Instance load = cpu_normalOffload = 90%
Offload = 83%
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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
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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
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18. Preliminary results
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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.
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20. THANK YOU FOR LISTENING…
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