Machine Learning Based Task Distribution in Heterogeneous Fog-Cloud Environments

Pourkiani, Mohammadreza; Abedi, Masoud

doi:10.23919/softcom50211.2020.9238309

Cited by 2 publications

(1 citation statement)

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“…As a result, big data analytics has garnered considerable interest. In contemporary times, there is a prevailing trend among organisations to give utmost importance to the examination of accumulated data with the objective of deriving valuable insights that can inform critical decision-making processes [2]. In contemporary times, it has been imperative for organisations to possess a flexible information technology infrastructure in response to the transition towards cloud computing (CC).…”

Section: Introductionmentioning

confidence: 99%

A Comparative Review of Internet of Things Model Workload Distribution Techniques in Fog Computing Networks

Puttaswamy,

Murthy,

Degha

2024

IDA

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In the realm of fog computing (FC), a vast array of intelligent devices collaborates within an intricate network, a synergy that, while promising, has not been without its challenges. These challenges, including data loss, difficulties in workload distribution, a lack of parallel processing capabilities, and security vulnerabilities, have necessitated the exploration and deployment of a variety of solutions. Among these, software-defined networks (SDN), double-Q learning algorithms, service function chains (SFC), virtual network functions (VNF) stand out as significant. An exhaustive survey has been conducted to explore workload distribution methodologies within Internet of Things (IoT) architectures in FC networks. This investigation is anchored in a parameter-centric analysis, aiming to enhance the efficiency of data transmission across such networks. It delves into the architectural framework, pivotal pathways, and applications, aiming to identify bottlenecks and forge the most effective communication channels for IoT devices under substantial workload conditions. The findings of this research are anticipated to guide the selection of superior simulation tools, validate datasets, and refine strategies for data propagation. This, in turn, is expected to facilitate optimal power consumption and enhance outcomes in data transmission and propagation across multiple dimensions. The rigorous exploration detailed herein not only illuminates the complexities of workload distribution in FC networks but also charts a course towards more resilient and efficient IoT ecosystems.

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Section: Introductionmentioning

confidence: 99%