Fuzzy Handoff Control in Edge Offloading

Basic, Fani; Aral, Atakan; Brandić, Ivona

doi:10.1109/icfc.2019.00020

Cited by 19 publications

(28 citation statements)

References 30 publications

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“…In this research, four functions have been used (average VM utilization, task length, network bandwidth and delay sensitivity) and each function has three variables (Low, Medium, High). The values of each fuzzy variable are determined empirically based on a number of experiments similar to approaches used in [41,57]. Figure 4 shows the four membership functions.…”

Section: Fuzzy Logic Systemmentioning

confidence: 99%

A novel approach for IoT tasks offloading in edge-cloud environments

Almutairi

Aldossary

2021

J Cloud Comp

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Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. Although Edge Computing is a promising enabler for latency-sensitive related issues, its deployment produces new challenges. Besides, different service architectures and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents a novel approach for task offloading in an Edge-Cloud system in order to minimize the overall service time for latency-sensitive applications. This approach adopts fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand and delay sensitivity) as well as resource utilization and resource heterogeneity. A number of simulation experiments are conducted to evaluate the proposed approach with other related approaches, where it was found to improve the overall service time for latency-sensitive applications and utilize the edge-cloud resources effectively. Also, the results show that different offloading decisions within the Edge-Cloud system can lead to various service time due to the computational resources and communications types.

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Section: Fuzzy Logic Systemmentioning

confidence: 99%

A novel approach for IoT tasks offloading in edge-cloud environments

Almutairi

Aldossary

2021

J Cloud Comp

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“…In this research, four functions have been used (average VM utilization, task length, network bandwidth and delay sensitivity) and each function has three variables (Low, Medium, High). The values of each fuzzy variable are determined empirically based on a number of experiments similar to approaches used in [57], [41]. Figure 4 shows the four membership functions.…”

Section: Fuzzy Logic Systemmentioning

confidence: 99%

A Novel Approach for IoT Tasks Offloading in Edge-Cloud Environments

Almutairi

Aldossary

2021

Preprint

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Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT task to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. Although Edge Computing is a promising enabler for latency-sensitive related issues, its deployment produces new challenges. Besides, different service architectures and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents a novel approach for task offloading in an Edge-Cloud system in order to minimize the overall service time for latency-sensitive applications. This approach adopts the fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand and delay sensitivity) as well as resource utilization and resource heterogeneity. A number of simulation experiments are conducted to evaluate the proposed approach with other related approaches, where it was found to improve the overall service time for latency-sensitive applications and utilize the edge-cloud resources effectively. Also, the results show that different offloading decisions within the Edge-Cloud system can lead to various service time due to the computational resources and communications types.

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“…Alguns trabalhos encontrados na literatura [Habak et al 2015] [Zhou et al 2016] [Chilukuri et al 2017] [Basic et al 2019] têm abordado a seleção do melhor REE para offloading levando em consideração diversas métricas, sendo as principais: o cálculo da capacidade computacional, consumo energético, escolhas de usuários e desenvolvedores, e a heterogeneidade e mobilidade (dinamicidade) dos dispositivos.…”

Section: Trabalhos Relacionadosunclassified

“…O trabalho [Basic et al 2019] propõe um algoritmo baseado em lógica Fuzzy para selecionar um REE para realização do processo de offloading computacional com base nos parâmetros de largura de banda, velocidade do processador e latência.É proposto também um controlador de transferência do processo de offloading, que leva em consideração o tempo de resposta da aplicação como indicador para decidir se deve mover a execução da tarefa para outro REE. Apesar de considerarem a dinamicidade dos dispositivos, os autores não especificam a abordagem utilizada para evitar falhas na comunicação.…”

Section: Trabalhos Relacionadosunclassified

Uma abordagem sensível a contexto para tomada de decisão de offloading de processamento

Alves¹,

Rego²,

Trinta³

2020

Anais XXXVIII Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC 2020)

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As aplicações para dispositivos móveis tem evoluído possibilitando a realização das mais diversas atividades. Para auxiliar os dispositivos, a técnica de offloading computacional tem sido utilizada para migrar tarefas para servidores em nuvem com poder de processamento superior ou maior capacidade energética. Entretanto, quando indisponível tal infraestrutura ou quando a latência da comunicação é um empecilho para a execução do offloading, uma estratégia que pode ser utilizada é o offloading para um ambiente remoto de execução (do inglês, remote execution environment - REE), que pode ser inclusive outros dispositivos móveis próximos. A escolha de um REE é um processo complexo, pois a heterogeneidade dos dispositivos interfere no tempo de conclusão das tarefas migradas. Este trabalho apresenta um método para selecionar um REE que considera o contexto do usuário. Para validar a solução desenvolvida, uma arquitetura de software foi projetada e implementada, e experimentos foram executados.

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Fuzzy Handoff Control in Edge Offloading

Cited by 19 publications

References 30 publications

A novel approach for IoT tasks offloading in edge-cloud environments

A novel approach for IoT tasks offloading in edge-cloud environments

A Novel Approach for IoT Tasks Offloading in Edge-Cloud Environments

Uma abordagem sensível a contexto para tomada de decisão de offloading de processamento

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