An Improved Scheduling Algorithm Based on Jellyfish Search for Measurement Data Process System

Parallel computing of real-time data is an effective solution to the problem of timely analysis with the rapid increase of real-time data in today's power grid. Currently, most data parallel processing methods do not consider the characteristics of the real-time power measurement data to improve efficiency. This paper proposes an improved jellyfish search method for data process task scheduling, which considers time window segmentation based on data arrival characteristic. The method proposed in this paper can improve the processing speed of realtime power measurement data with resources reduction.