Adaptive offloading systems achieve context specific
optimization on mobile and pervasive devices by offloading
computational components to a resource copious remote server
or cloud. However, with the recent advancement in
computational capacity of mobile and pervasive devices,
adaptive offloading could facilitate the formation of ad-hoc
cloud-like environments using collections of mobile and
pervasive devices, with reduced reliance on centralized
infrastructure. Therefore, in this paper, we formulate a
decision-making strategy for global adaptive offloading that
distributes application components to community-based clouds
formed from multiple collaborating peers. The goal was to
extend the collaboration and application lifetime by optimizing
the Time to Failure (TTF) of devices due to energy depletion,
while meeting application specific performance constraints.
Specifically, a max-min technique was used to maximise the
minimum TTF in order to balance energy consumption across
collaborating devices. The efficacy, performance and
scalability of the formulated model were evaluated with the
proposed algorithm producing an optimal solution to the
specified model, using integer linear programming, in
affordable time and energy for a range of application and
collaboration sizes.
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