Adaptive deployment in ad-hoc systems using emergent component ensembles: vision paper

Mobile cloud computing in the context of ad-hoc clouds brings new challenges when offloading computation from mobile devices. The management of application deployment needs to ensure that the offloading provides users with the expected benefits, but it suddenly needs to cope with a highly dynamic environment which lacks a central authority and in which computational nodes appear and disappear. We propose an approach to the management of ad-hoc systems in such dynamic environment using component ensembles that connect mobile devices with more powerful computation nodes. Our approach aims to address the challenges of scalability and robustness of such systems without the need for central authority, relying instead on simple patterns that lead to reasonable adaptation decisions based on limited and imprecise information.

[1]  Petr Tuma,et al.  Capturing performance assumptions using stochastic performance logic , 2012, ICPE '12.

[2]  Petr Tuma,et al.  Performance Awareness in Component Systems: Vision Paper , 2012, 2012 IEEE 36th Annual Computer Software and Applications Conference Workshops.

[3]  Cheng Wang,et al.  A Survey of Job Scheduling in Grids , 2007, APWeb/WAIM.

[4]  Ke Xu,et al.  A Survey of Research on Mobile Cloud Computing , 2011, 2011 10th IEEE/ACIS International Conference on Computer and Information Science.

[5]  Mohsen Sharifi,et al.  A Survey and Taxonomy of Cyber Foraging of Mobile Devices , 2012, IEEE Communications Surveys & Tutorials.

[6]  Michal Kit,et al.  Towards Dependable Emergent Ensembles of Components: The DEECo Component Model , 2012, 2012 Joint Working IEEE/IFIP Conference on Software Architecture and European Conference on Software Architecture.

[7]  Chi-Sheng Shih,et al.  Executing mobile applications on the cloud: Framework and issues , 2012, Comput. Math. Appl..

[8]  Alvaro A. A. Fernandes,et al.  An Approach to Ad hoc Cloud Computing , 2010, ArXiv.

[9]  Byung-Gon Chun,et al.  Dynamically partitioning applications between weak devices and clouds , 2010, MCS '10.