GASP: Genetic Algorithms for Service Placement in Fog Computing Systems

Abstract

Fog computing is becoming popular as a solution to support applications based on geographically distributed sensors that produce huge volumes of data to be processed and filtered with response time constraints. In this scenario, typical of a smart city environment, the traditional cloud paradigm with few powerful data centers located far away from the sources of data becomes inadequate. The fog computing paradigm, which provides a distributed infrastructure of nodes placed close to the data sources, represents a better solution to perform filtering, aggregation, and preprocessing of incoming data streams reducing the experienced latency and increasing the overall scalability. However, many issues still exist regarding the efficient management of a fog computing architecture, such as the distribution of data streams coming from sensors over the fog nodes to minimize the experienced latency. The contribution of this paper is two-fold. First, we present an optimization model for the problem of mapping data streams over fog nodes, considering not only the current load of the fog nodes, but also the communication latency between sensors and fog nodes. Second, to address the complexity of the problem, we present a scalable heuristic based on genetic algorithms. We carried out a set of experiments based on a realistic smart city scenario: the results show how the performance of the proposed heuristic is comparable with the one achieved through the solution of the optimization problem. Then, we carried out a comparison among different genetic evolution strategies and operators that identify the uniform crossover as the best option. Finally, we perform a wide sensitivity analysis to show the stability of the heuristic performance with respect to its main parameters.

Optimizing Genetic Algorithm Performance for Effective Traffic Lights Control using Balancing Technique (GABT)

“…Selecting the best parameters of a genetic algorithm, so as to obtain good results to optimize its performance, is very important to its effectiveness. Crossover, mutation rate and population size are the most influencing control parameters as reported by previous works [16][17][18][19][20]. However, pressure selection and population size in correlation is a new approach in balancing and GA algorithm optimization.…”

Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization

“…In [7], a hybrid genetic-simulated annealing latency-minimum offloading decision algorithm in IoT-fog computing is designed to find the best offloading decision with minimum latency. Also, in [27], a genetic algorithm is proposed for the offloading decisions in IoT-fog computing. However, these algorithms do not consider the balancing the load balancing on the fog nodes.…”

“…The IoT-fog network architecture used in this paper consists of three layers, the IoT layer, the fog layer, and the cloud layer, as shown in Figure 1. The same architecture is used in [7], [27]. In the bottom layer, the IoT layer, a number of geographically distributed sensor nodes are connected using a local network.…”

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

“…Canali et al [60] proposed a GA for service placement in FC. They studied mapping data streams over fog nodes and presented an optimization model.…”

“…In the future, efficient service discovery protocols are needed to design, such that users can identify and locate the relevant service providers to meet their demands. • Real-Time Optimization: For many edge application scenarios, the service environments are of high dynamics [60] and it is hard to correctly foresee future events. Thus, it would require the remarkable capabilities of online edge resource orchestration and provisioning to continuously handle massive dynamic workloads and tasks.…”