Genetic algorithm for delay efficient computation offloading in dispersed computing

As a promising computing paradigm, dispersed computing uses all devices with computing, communication, and storage capabilities as ubiquitous network computing points (NCPs) to achieve low-latency, efficient collaborative computation for tasks, which greatly supports the newly arising computationally intensive applications. However, how to fully utilize the limited resources of NCPs to reduce task latency remains a challenging problem. Considering the heterogeneous communication modes and computing capabilities of NCPs, a distributed multi-hop computing task offloading framework based on an improved genetic algorithm is proposed to address this challenge, where tasks can be recursively offloaded among NCPs. The algorithm reduces the solution space dimension by designing filter chains to filter schedulable nodes before the initialization phase, and improves the population initialization, crossover operator to speed up the convergence, and avoids the risk of overconsumption of resources due to circular scheduling. Compared with the D2D-Fogging and Min-Min algorithms, the experimental results show that our scheduling algorithm improves the global resource utilization by 14% and 8%, and reduces the average task computation delay by 39.18% and 28.21%.

Automated summary

Dispersed computing is a promising paradigm that utilizes devices with computing, communication, and storage capabilities as network computing points. This framework addresses the challenge of fully utilizing limited resources by proposing a distributed multi-hop computing task offloading framework based on an improved genetic algorithm. Experimental results demonstrate that this scheduling algorithm improves global resource utilization and reduces average task computation delay compared to existing algorithms.