On Handling Large-Scale Polynomial Multiplications in Compute Cloud Environments using Divisible Load Paradigm

Large-scale polynomial product computations often used in aerospace applications such as satellite image processing and sensor networks data processing always pose considerable challenge when processed on networked computing systems. With non-zero communication and computation time delays of the links and processors on a networked infrastructure, the computation becomes all the more challenging. In this research, we attempt to investigate the use of a divisible load paradigm to design efficient strategies to minimize the overall processing time for performing large-scale polynomial product computations in compute cloud environments. We consider a compute cloud system with the resource allocator distributing the entire load to a set of virtual CPU instances (VCI) and the VCIs propagating back the processed results to resource allocator for postprocessing. We consider heterogeneous networks in our analysis and we derive fundamental recursive equations and a closed-form solution for the load fractions to be assigned to each VCI. Our analysis also attempts to eliminate any redundant VCI-link pairs by carefully considering the overheads associated with load distribution and processing. Finally, we quantify the performance of the strategies via rigorous simulation studies.