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Slim VM: optimistic partial program loading for connected embedded Java virtual machines
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ACM International Conference Proceeding Series; Vol. 347 archive
Proceedings of the 6th international symposium on Principles and practice of programming in Java table of contents
Modena, Italy
SESSION: Optimization and run-time performance II table of contents
Pages 117-126  
Year of Publication: 2008
ISBN:978-1-60558-223-8
Authors
Gregor Wagner  University of California, Irvine Irvine, CA
Andreas Gal  University of California, Irvine Irvine, CA
Michael Franz  University of California, Irvine Irvine, CA
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Embedded systems are inherently limited in terms of local storage capacity. This limitation conflicts with the demands of modern virtual machine platforms, which require large amounts of library code to be present on each client device. These conflicting requirements are often resolved by providing specialized embedded versions of the standard libraries, but even these stripped down libraries consume significant resources.

We present a solution for "always connected" mobile devices that is based on a zero footprint paradigm. In our approach, all code resides on a remote host. Only those parts of applications and libraries that are likely to be needed are transferred to the mobile device. Since it is difficult to predict statically which library parts will be needed at runtime, we combine static analysis and lazy code loading to transfer code with a high likelihood of execution ahead of time while other code (such as exception code) remains on the remote host and is provided only on demand. This allows us to perform not only dead code elimination, but also aggressive elimination of cold (or potentially unused) code.

The granularity of our approach is flexible all the way down to individual basic blocks. Our method achieves total code size reductions of up to 95%.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Gregor Wagner: colleagues
Andreas Gal: colleagues
Michael Franz: colleagues