Distributed genetic algorithm for application placement in the compute continuum leveraging infrastructure nodes for optimization

TL;DR

This paper introduces and evaluates three distributed genetic algorithm designs for optimizing resource placement in fog computing, balancing solution quality, network overhead, and distribution degree.

Contribution

It proposes three novel distributed GA architectures tailored for fog environments, analyzing their trade-offs in solution quality and network load.

Findings

Distributed design with centralized objective storage achieves similar quality to centralized GA.

Fully distributed population reduces network overhead but lowers solution diversity.

Neighbor-interchange design minimizes network load with some compromise on solution quality.

Abstract

The increasing complexity of fog computing environments calls for efficient resource optimization techniques. In this paper, we propose and evaluate three distributed designs of a genetic algorithm (GA) for resource optimization in fog computing, within an increasing degree of distribution. The designs leverage the execution of the GA in the fog devices themselves by dealing with the specific features of this domain: constrained resources and widely geographical distribution of the devices. For their evaluation, we implemented a benchmark case using the NSGA-II for the specific problem of optimizing the fog service placement, according to the guidelines of our three distributed designs. These three experimental scenarios were compared with a control case, a traditional centralized version of this GA algorithm, considering solution quality and network overhead. The results show that the design with the lowest distribution degree, which keeps centralized storage of the objective space, achieves comparable solution quality to the traditional approach but incurs a higher network load. The second design, which completely distributes the population between the workers, reduces network overhead but exhibits lower solution diversity while keeping enough good results in terms of optimization objective minimization. Finally, the proposal with a distributed population and that only interchanges solution between the workers' neighbors achieves the lowest network load but with compromised solution quality.