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An integrated approach of ML-metaheuristics for secure service placement in fog-cloud ecosystem by Supriya Singh; D.P. Vidyarthi is a Computer Science article available to read on EtoBox.

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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

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Author

Supriya Singh; D.P. Vidyarthi

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)