An integrated approach of ML-metaheuristics for secure service placement in fog-cloud ecosystem

Abstract

With the proliferation of the Internet of Things (IoT), a huge amount of data is being produced which requires processing. Usually, Cloud computing provides the resources for processing such requests. However, cloud has limitations in terms of latency and security. To overcome, Fog computing came into existence to complement cloud wherein the consolidation of Fog and Cloud computing substantially improves the Quality of Service (QoS). Due to inherent heterogeneity in the Cloud and Fog nodes, selecting an appropriate node becomes a complex task which requires utmost attention. Fog nodes have minimal and limited resources with a thin processing capacity. To utilize the fog nodes optimally, a secure two-step service placement framework has been proposed in this work. The framework consists of the classification of services to decide on the appropriate tier (Cloud/Fog) followed by the scheduling of the services at the Fog tier. For classification, a machine learning based improved adaptive neuro-fuzzy inference model (ANFIS) is implemented, which predicts the suitable computing tier. Further, a novel metaheuristic-based hybrid algorithm that integrates a chaotic-based grasshopper (CGOA) with genetic algorithm (GA) is applied for scheduling at the Fog tier. Basic GOA suffers from several shortcomings, such as falling into local minima, slow convergence, high time complexity, etc. The concept of chaos theory and opposition-based learning is incorporated into GOA to overcome this. The performance of the proposed model has been analysed on the Google trace data set, and its effectiveness has been studied on makespan, computational cost, and energy dissipation of the network where it yields respectively 9.2%, 4.25% and 2.75% average better results than state of art.