Handling Large-Scale SAR Image Data on Network-based Compute systems using Divisible load paradigm

Processing Synthetic Aperture Radar (SAR) image data on compute clusters is a challenging problem. One of the ways to handle such computationally intensive tasks is to design efficient load distribution strategies for a given compute infrastructure. In this paper, we adopt the Divisible Load paradigm to handle SAR image processing by using Multi-instalment scheduling (MIS) strategies that have conclusively proven to minimize the idle times. In this paper, we design, analyse, and evaluate a practically viable load distribution strategy, referred to as Multi-Instalment Scheduling with Results Retrieval (MIS-RR), by considering the communication and computational overheads. In addition, we consider the results retrieval phase as a part of our solution procedure. This makes the formulation complete as it takes into account of all real-world influencing parameters in modelling and this is a first-work-of-its-kind contribution in employing a periodic MIS strategy that includes results retrieval phase together with system overhead parameters. We present a detailed theoretical analysis followed by a rigorous performance evaluation that captures the behaviour of the strategy. We evaluate our strategy based on a number of influencing parameters such as network scalability, number of instalments, overheads and then attempt to identify the maximal number of processors to use for a given size of the load. This latter result is of practical importance for system administrators in optimizing the resources to be deployed for a given load.