DISCO: Distributed computation offloading framework for fog computing networks

Tran-Dang, Hoa; Kim, Dong-Seong

doi:10.23919/jcn.2022.000058

Cited by 22 publications

(17 citation statements)

References 42 publications

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“…In Fog Computing, decisions regarding task execution locations are made based on factors such as resource availability, latency, and energy consumption. In the same way this work [28], delves into the practice of distributing computational tasks across multiple nodes within a Fog Computing network. This approach becomes pertinent in scenarios where tasks can be executed more efficiently by harnessing resources from various locations within the network.…”

Section: Ai-based Methodsmentioning

confidence: 99%

“…The collaborative functioning of the fog and cloud layers is orchestrated to execute tasks requiring a combination of low-latency and high-performance computing. Examples include real-time monitoring, control, and analytics [29]. It's noteworthy that this architecture remains an active area of research, with various iterations and implementations existing based on specific use cases and requirements.…”

Section: Fog and Cloud Computing: Effective Collaborationmentioning

confidence: 99%

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A Multi-Objective Approach for Optimizing IoT Applications Offloading in Fog-Cloud Environments with NSGA-II

mokni,

Yassa

2024

Preprint

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Internet of Things (IoT) applications have gained widespread adoption across diverse sectors such as healthcare, smart agriculture, smart cities, transportation, and water management, leading to a substantial generation of Big Data. To efficiently process this voluminous data, there is a pressing need for a platform capable of handling large quantities. However, real-time applications face challenges in cloud processing due to high latency. Fog computing, serving as a complementary infrastructure to the cloud, emerges as a viable solution by facilitating task processing, networking, and data storage in cloud data centers accessible to mobile users. Task offloading emerges as a promising fog computing solution to overcome resource constraints in IoT applications. This involves the execution of part or all of mobile applications in the cloud, aiming to enhance execution time and reduce energy consumption. Our research concentrates on optimizing the IoT task offloading problem within heterogeneous environments, considering conflicting constraints. This optimization challenge is formulated as a multi-objective problem, emphasizing energy consumption and latency Quality of Service (QoS) metrics. Our proposed solution, named Tof-NSGAII, is tailored to respect the finite resources of fog computing by adeptly balancing workloads and meeting the latency requirements of IoT tasks. We have adapted the widely employed meta-heuristic, the non-dominant sorting genetic algorithm (NSGA-II), to generate a set of non-dominated multi-objective task offloading optimization solutions, considering both energy consumption and latency. Experimental results showcase the efficacy of Tof-NSGAII in generating task offloading solutions that judiciously distribute executed tasks between fog and cloud computing environments based on their specific requirements. Additionally, the generated non-dominated solutions demonstrate optimality in terms of energy consumption, boasting an average energy reduction of 12.18\% compared to alternative approaches. Notably, our approach introduces only a marginal increase in latency, amounting to 0.38\%, a difference that can be considered negligible.

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Section: Ai-based Methodsmentioning

confidence: 99%

Section: Fog and Cloud Computing: Effective Collaborationmentioning

confidence: 99%

A Multi-Objective Approach for Optimizing IoT Applications Offloading in Fog-Cloud Environments with NSGA-II

mokni,

Yassa

2024

Preprint

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“…RQ 4 : Both FC and RSs rely on digital data to make recommendations. The proficiency to collect and analyze big data for decision making is crucial, provided that the functionality of digital technologies all rely on digital data [74]. What levels of digital knowledge will be needed for consumers to understand FC recommendations?…”

Section: Future Trends and Challengesmentioning

confidence: 99%

Fog Computing-Based Smart Consumer Recommender Systems

Hornik,

Ofir,

Rachamim

et al. 2024

JTAER

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The latest effort in delivering computing resources as a service to managers and consumers represents a shift away from computing as a product that is purchased, to computing as a service that is delivered to users over the internet from large-scale data centers. However, with the advent of the cloud-based IoT and artificial intelligence (AI), which are advancing customer experience automations in many application areas, such as recommender systems (RS), a need has arisen for various modifications to support the IoT devices that are at the center of the automation world, including recent language models like ChatGPT and Bard and technologies like nanotechnology. This paper introduces the marketing community to a recent computing development: IoT-driven fog computing (FC). Although numerous research studies have been published on FC “smart” applications, none hitherto have been conducted on fog-based smart marketing domains such as recommender systems. FC is considered a novel computational system, which can mitigate latency and improve bandwidth utilization for autonomous consumer behavior applications requiring real-time data-driven decision making. This paper provides a conceptual framework for studying the effects of fog computing on consumer behavior, with the goal of stimulating future research by using, as an example, the intersection of FC and RS. Indeed, our conceptualization of the “fog-based recommender systems” opens many novel and challenging avenues for academic research, some of which are highlighted in the later part of this paper.

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“…The objective is to achieve the stable matching outcome which matches tasks with HNs appropriately. To deal with dynamic task division, a recent DISCO algorithm introduced in [28] proposes a matching based distributed computation scheme for the fog computing network in which the theory of matching with group firstly is applied. Accordingly, a task divided into multiple subtasks is preferred to be processed by a group of HNs to minimize its execution delay [29].…”

Section: Related Workmentioning

confidence: 99%

Dynamic collaborative task offloading for delay minimization in the heterogeneous fog computing systems

Tran-Dang

Kim

2023

J. Commun. Netw.

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DISCO: Distributed computation offloading framework for fog computing networks

Abstract: Article that has been accepted for inclusion in a future issue of a journal. Content is final as presented, with the exception of pagination.

Cited by 22 publications

References 42 publications

A Multi-Objective Approach for Optimizing IoT Applications Offloading in Fog-Cloud Environments with NSGA-II

A Multi-Objective Approach for Optimizing IoT Applications Offloading in Fog-Cloud Environments with NSGA-II

Fog Computing-Based Smart Consumer Recommender Systems

Dynamic collaborative task offloading for delay minimization in the heterogeneous fog computing systems

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