This paper investigates sequential cropping under limited computational resources. The paper analyses state-of-the-art competitive algorithms such as Needleman-Wunsch, Smith-Waterman and BLAST, and the application of these algorithms to distributed computing environments, including fog computing (FC). Methods for accelerating the algorithms are also discussed, including parallel data processing, hardware acceleration using graphics processing units (GPU) and programmable gate arrays (FPGA), and optimising data communication. Particular attention is paid to the adaptation of the Smith-Waterman algorithm for execution on low-power devices and the parallelisation of the algorithm on computing nodes. The proposed methodology increases the efficiency of analysing large amounts of genetic data, reduces the network load and ensures efficient combat.