Adaptive Offloading Inference for Delivering Applications in Pervasive Computing Environments

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Adaptive Offloading Inference for Delivering Applications in Pervasive Computing Environments (2003)

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by Xiaohui Gu , Klara Nahrstedt , Alan Messer , Dejan Milojicic , Ira Greenberg , Ira Greenberg , Hewlett Packard Laboratories

Venue:	Proc. of IEEE International Conference on Pervasive Computing and Communications (PerCom 2003), Dallas-Fort
Citations:	26 - 2 self

BibTeX

@INPROCEEDINGS{Gu03adaptiveoffloading,
    author = {Xiaohui Gu and Klara Nahrstedt and Alan Messer and Dejan Milojicic and Ira Greenberg and Ira Greenberg and Hewlett Packard Laboratories},
    title = {Adaptive Offloading Inference for Delivering Applications in Pervasive Computing Environments},
    booktitle = {Proc. of IEEE International Conference on Pervasive Computing and Communications (PerCom 2003), Dallas-Fort},
    year = {2003},
    pages = {107}
}

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Abstract

Pervasive computing allows a user to access an application on heterogeneous devices continuously and consistently. However, it is challenging to deliver complex applications on resource-constrained mobile devices, such as cell phones and PDAs. Different approaches, such as application-based or system-based adaptations, have been proposed to address the problem. However, existing solutions often require degrading application fidelity. We believe that this problem can be overcome by dynamically partitioning the application and offloading part of the application execution to a powerful nearby surrogate. This will enable pervasive application delivery to be realized without significant fidelity degradation or expensive application rewriting. Because pervasive computing environments are highly dynamic, the runtime offloading system needs to adapt to both application execution patterns and resource fluctuations. Using the Fuzzy Control model, we have developed an offloading inference engine to adaptively solve two key decision-making problems during runtime offloading: (1) timely triggering of adaptive offloading, and (2) intelligent selection of an application partitioning policy. Extensive trace-driven evaluations show the effectiveness of the offloading inference engine.

Keyphrases

pervasive computing environment adaptive offloading inference inference engine different approach complex application runtime offloading pervasive computing resource-constrained mobile device extensive trace-driven evaluation application partitioning policy powerful nearby surrogate application execution pattern intelligent selection significant fidelity degradation adaptive offloading pervasive application delivery system-based adaptation cell phone fuzzy control model heterogeneous device application execution expensive application rewriting application fidelity key decision-making problem resource fluctuation

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