Crowdsensing the opportunistic context of mobile devices

huber.flores@helsinki.fi

Helsinki, Finland.

EVIDENCE-AWARE MOBILE COMPUTATIONAL

OFFLOADING Huber Flores, Pan Hui, Petteri Nurmi, Eemil Lagerspetz, Sasu Tarkoma, Jukka

Manner, Vassilis Kostakos, Yong Li and Xiang Su

Outline

• Background

– Mobile code offloading

• Motivation

• Problem statement

• Evidence-aware mobile computational offloading

• Implications for the edge

• Conclusions

Helsinki, Finland.

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Background

• Opportunistic augmentation of resources

Helsinki, Finland.

[IEEE Communications] 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.

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Motivation

• The offloading outcome is diverse due to many parameters – Latency – Code profiling – Device workload – Server processing the task – Type of device – Etc.

• Initial idea…. – Can we do better?

• …. Tuning parameters

Helsinki, Finland.

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Problem?

• It is not that easy

– For a single device

• Is it possible to crowdsource the problem?

Helsinki, Finland.

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Crowdsensing characterization

Helsinki, Finland.

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Crowdsensing characterization

Helsinki, Finland.

[IEEE Communications] 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.

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Crowdsensing characterization

Helsinki, Finland.

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Crowdsensing characterization

Helsinki, Finland.

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Crowdsensing characterization

Helsinki, Finland.

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Crowdsensing characterization

Helsinki, Finland.

[ICDCS] Flores, Huber, et al. “Modeling Mobile Code Acceleration in the Cloud" , Proceeding of ICDCS 2017, Atlanta, USA, June 5-8, 2017.

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Crowdsensing support

• LAPSI

Helsinki, Finland.

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EMCO framework

Helsinki, Finland.

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Crowd evaluation

Helsinki, Finland.

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Crowd evaluation

Helsinki, Finland.

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Off-the-shelf applications

Helsinki, Finland.

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Implications

• Combine cloud/edge provisioning – Dynamic allocation of resources

• Self-organizing systems (end points at the edge) – Offloading

– Sensing

– Networking

– Storage

– And so on

Helsinki, Finland.

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Implications

• Edge infrastructure

Helsinki, Finland.

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Implications

• Edge infrastructure

Helsinki, Finland.

Task

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Summary

• Context characterization is really important, but it is not a task a single device can perform

• We demonstrate how context adaptation improves offloading

• A methodology for context reconstruction from passive data (datasets)

Helsinki, Finland.

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QUESTIONS

Helsinki, Finland.

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