Running Genetic Algorithms in the Edge: A First Analysis

Abstract

Nowadays, the volume of data produced by different kinds of devices is continuously growing, making even more difficult to solve the many optimization problems that impact directly on our living quality. For instance, Cisco projected that by 2019 the volume of data will reach 507.5 zettabytes per year, and the cloud traffic will quadruple. This is not sustainable in the long term, so it is a need to move part of the intelligence from the cloud to a highly decentralized computing model. Considering this, we propose a ubiquitous intelligent system which is composed by different kinds of endpoint devices such as smartphones, tablets, routers, wearables, and any other CPU powered device. We want to use this to solve tasks useful for smart cities. In this paper, we analyze if these devices are suitable for this purpose and how we have to adapt the optimization algorithms to be efficient using heterogeneous hardware. To do this, we perform a set of experiments in which we measure the speed, memory usage, and battery consumption of these devices for a set of binary and combinatorial problems. Our conclusions reveal the strong and weak features of each device to run future algorihms in the border of the cyber-physical system.

Genetic-based optimization in Fog Computing: current trends and research opportunities

“…In this way, it would be possible to take advantage of the distributed characteristics of a Fog system to be able to parallelize the execution of the proposed GAs. Morell and Alba [127] have carried out a preliminary study of the execution of GAs for the optimization of classic problems in Edge Computing environments (with mobile devices and with very heterogeneous characteristics). Their results are promising and, despite the differences with Edge environments, their conclusions can be transferred to Fog infrastructures, especially those related to the heterogeneity of the devices.…”

Evaluation and efficiency comparison of evolutionary algorithms for service placement optimization in fog architectures

“…In the context of in-situ deployment of distributed GAs within a fog infrastructure, Morell and Alba [3] designed a GA version specifically tailored for execution on portable devices. Their study primarily focused on evaluating resource usage and execution time on highly resource-constrained devices such as smartphones, routers, and wearables.…”

“…To mitigate this drawback, distributed execution of optimization algorithms has been proposed. While some studies have explored distributed and parallel approaches for executing GAs in fog domains [3], to the best of our knowledge, no previous research has investigated the utilization of fog infrastructure resources for its own optimization.…”

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“…Também podemos observar através do resumo apresentado na tabela 3, que a maioria dos trabalhos utilizam programação linear para a solução dos problemas de otimização, seguido de heurísticas e abordagens evolucionárias. O uso de algoritmos genéticos (GA) [44], já é um tema consolidado na área de computação em nuvem, contudo, em ambientes Fog Computing ainda são poucos explorados.…”