Blind Load-Balancing Algorithm using Double-Q- learning in the Fog Environment

Tahmasebi-Pouya, Niloofar; Sarram, Mehdi Agha

doi:10.1109/iccke54056.2021.9721449

Cited by 2 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also shown is a comparison between how LB is done now in "cloud-based" infrastructure and how it is done with the "cloud-fog" pair. The end goal is to spread out the load through fog networks and make the internet less reliant on the cloud by putting data closer to the user [34,35].…”

Section: Fc Data Replication and Lb Methodsmentioning

confidence: 99%

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

Puttaswamy,

Murthy,

Degha

2024

IDA

View full text Add to dashboard Cite

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.

show abstract

Section: Fc Data Replication and Lb Methodsmentioning

confidence: 99%

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

Puttaswamy,

Murthy,

Degha

2024

IDA

View full text Add to dashboard Cite

show abstract

A Review Load balancing algorithms in Fog Computing

Mahdi,

Hassan,

Abdulsaheb

2024

BIO Web Conf.

View full text Add to dashboard Cite

With the rapid advance of the Internet of Things (IoT), technology has entered a new era. It is changing the way smart devices relate to such fields as healthcare, smart cities, and transport. However, such rapid expansion also challenges data processing, latency, and QoS. This paper aims to consider fog computing as a key solution for addressing these problems, with a special emphasis on the function of load balancing to improve the quality of service in IoT environments. In addition, we study the relationship between IoT devices and fog computing, highlighting why the latter acts as an intermediate layer that can not only reduce delays but also achieve efficient data processing by moving the computational resources closer to where they are needed. Its essence is to analyze various load balancing algorithms and their impact in fog computing environments on the performance of IoT applications. Static and dynamic load balancing strategies and algorithms have been tested in terms of their impact on throughput, energy efficiency, and overall system reliability. Ultimately, dynamic load balancing methods of this sort are better than static ones for managing load in fog computing scenarios since they are sensitive to changing workloads and changes in the system. The paper also discusses the state of the art of load balancing solutions, such as secure and sustainable techniques for Edge Data Centers (EDCs), It manages the allocation of resources for scheduling. We aim to provide a general overview of important recent developments in the literature while also pointing out limitation where improvements might be made. To this end, we set out to better understand and describe load balancing in fog computing and its importance for improving QoS. We thus hope that a better understanding of load balancing technologies can lead us towards more resilient and secure systems.

show abstract

Blind Load-Balancing Algorithm using Double-Q- learning in the Fog Environment

Cited by 2 publications

References 17 publications

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

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

A Review Load balancing algorithms in Fog Computing

Contact Info

Product

Resources

About