A Novel Segmented Random Search based Batch Scheduling Algorithm in Fog Computing

In fog computing, batch scheduling is an important and challenging task aiming at reducing the response latency. Response time includes the scheduling time, execution time and other factors such as network latency. However, the majority of the existing batch scheduling algorithms primarily focus on minimizing the execution time of tasks for IoT devices, ignoring the algorithms' scheduling time. This contributes to suboptimal overall response time, which is a critical quality-of-service (QoS) indicator and significantly impacts performance. Optimizing both execution and scheduling time is therefore crucial for achieving minimum response time, improving QoS, and alleviating load-balancing issues. This work introduces a novel approach for the load-balancing difference variable and uses dimensionality reduction techniques for dynamic task organization. The main contribution comprises two distinctive algorithms: the Pre-allocation Minimum Completion Time (PMT) algorithm and the Segmented Random Search Fog Computing Batch Scheduling Algorithm (SRS). These algorithms are designed to address the inherent characteristics of the problem. To evaluate the performance and applicability of batch scheduling algorithms, we establish a model incorporating compliance and application performance indicators. We introduce the Logical Recursive Indirect Comparison Analysis method to assess and evaluate batch scheduling algorithms. Simulation results demonstrate the exceptional effectiveness of the SRS algorithm. It exhibits a remarkable improvement in comprehensive performance indices, ranging from 139.10% to 261.18%, and optimization quality enhancement rates of 64.25% to 154.13% compared to classical standard optimization algorithms. Compared with advanced algorithms, the SRS algorithm also outperforms, with optimization quality improvement rates of 22.74% and 71.11% compared to AEOSSA and CHMPAD.