Mobile cloud computing is one of the main ways to augment the resource-constrained mobile devices to run rich mobile applications through the offloading technique, which leverages resources and services from remote server in the cloud. However, an efficient and intelligent use of cloud resources is required due to changing environment conditions and application variability usage. In order to help address this issue we present CoSMOS—Context-Sensitive Model for Offloading System—a context-aware and self-adaptive offloading decision support model for mobile cloud computing systems, based on self-aware and self-expressive systems. It employs decision-taking estimation based on application’s time execution and energy consumption to decide efficiently when and which application components should be offloaded in order to improve system’s execution. Our experiments show that the model is capable of inferring appropriate decisions with acceptable performance in a range of environment conditions.
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The authors would like to thank the Group of Computer Networks, Software Engineering and Systems (GREat) for the all the support offered during this work’s design and development stages, and for the MpOS framework and BenchImage mobile application used on this project. The authors would also like to thank the support provided by Brazilian Higher Education Funding Council (CAPES).
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Nakahara, F.A., Beder, D.M. A context-aware and self-adaptive offloading decision support model for mobile cloud computing system. J Ambient Intell Human Comput 9, 1561–1572 (2018) doi:10.1007/s12652-018-0790-7
- Mobile cloud computing
- Decision support
- Dynamic offloading