Dynamic decision support for resource offloading in heterogeneous Internet of Things environments

Jaddoa, Ali; Sakellari, Georgia; Panaousis, Emmanouil; Loukas, George; Sarigiannidis, Panagiotis

doi:10.1016/j.simpat.2019.102019

Cited by 30 publications

(20 citation statements)

References 34 publications

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“…In all cases, group A showed longer processing times because the requested tasks were allocated to the first server with free resources or the smallest free-resource capacity. It makes for a long queuing time at the edge server [54]. Worst fit and FuB algorithms have approximately the same performance results when the system load is low.…”

Section: A Comparison Of Fuzzy-based Meo Using Differentmentioning

confidence: 91%

“…According to the standard practice in computing offloading modeling [5,[54][55][56], the time it takes to transmit the requested task of an application from local edge server j connecting to mobile user i to neighboring edge server k depends on the size of the input data that the task is associated with, b ij , which is given as…”

Section: Transmission Delay At the Edge Serversmentioning

confidence: 99%

“…In accordance with the usual representation of processing time in the task allocation problem [5,54], the time needed for a task from mobile user i at edge server j based on the number of CPU cycles required is given as…”

Section: Processing Timementioning

confidence: 99%

See 2 more Smart Citations

Fuzzy‐Based Mobile Edge Orchestrators in Heterogeneous IoT Environments: An Online Workload Balancing Approach

Khanh

Nguyen

Huh

2021

Wireless Communications and Mobile Computing

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Online workload balancing guarantees that the incoming workloads are processed to the appropriate servers in real time without any knowledge of future resource requests. Currently, by matching the characteristics of incoming Internet of Things (IoT) applications to the current state of computing and networking resources, a mobile edge orchestrator (MEO) provides high-quality service while temporally and spatially changing the incoming workload. Moreover, a fuzzy-based MEO is used to handle the multicriteria decision-making process by considering multiple parameters within the same framework in order to make an offloading decision for an incoming task of an IoT application. In a fuzzy-based MEO, the fuzzy-based offloading strategy leads to unbalanced loads among edge servers. Therefore, the fuzzy-based MEO needs to scale its capacity when it comes to a large number of devices in order to avoid task failures and to reduce service times. In this paper, we investigate and propose an online workload balancing algorithm, which we call the fuzzy-based (FuB) algorithm, for a fuzzy-based MEO. By considering user configuration requirements, server geographic locations, and available resource capacities for achieving an online algorithm, our proposal allocates the proximate server for each incoming task in real time at the MEO. A simulation was conducted in augmented reality, healthcare, compute-intensive, and infotainment applications. Compared to two benchmark schemes that use the fuzzy logic approach for an MEO in IoT environments, the simulation results (using EdgeCloudSim) show that our proposal outperforms the existing algorithms in terms of service time, the number of failed tasks, and processing times when the system is overloaded.

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Section: A Comparison Of Fuzzy-based Meo Using Differentmentioning

confidence: 91%

Section: Transmission Delay At the Edge Serversmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy‐Based Mobile Edge Orchestrators in Heterogeneous IoT Environments: An Online Workload Balancing Approach

Khanh

Nguyen

Huh

2021

Wireless Communications and Mobile Computing

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“…The default value of the threshold is set to C th = 0.75, i.e., the decisions with C < C th are reevaluated. We adopt here a standard, confidence-score based decision that is simple but effective; for more advanced mechanisms on how to offload decisions from the edge, see, e.g., [47]. The threshold C th is a tunable parameter that allows to trade-off confidence in the decision about anomaly and response time.…”

Section: ) Anomaly Detection Based On Autoencodersmentioning

confidence: 99%

Deep Learning Anomaly Detection for Cellular IoT With Applications in Smart Logistics

Savić

Lukić

Danilovic³

et al. 2021

IEEE Access

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The number of connected Internet of Things (IoT) devices within cyber-physical infrastructure systems grows at an increasing rate. This poses significant device management and security challenges to current IoT networks. Among several approaches to cope with these challenges, data-based methods rooted in deep learning (DL) are receiving an increased interest. In this paper, motivated by the upcoming surge of 5G IoT connectivity in industrial environments, we propose to integrate a DL-based anomaly detection (AD) as a service into the 3GPP mobile cellular IoT architecture. The proposed architecture embeds autoencoder based anomaly detection modules both at the IoT devices (ADM-EDGE) and in the mobile core network (ADM-FOG), thereby balancing between the system responsiveness and accuracy. We design, integrate, demonstrate and evaluate a testbed that implements the above service in a real-world deployment integrated within the 3GPP Narrow-Band IoT (NB-IoT) mobile operator network.

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“…Dentro de las arquitecturas de la IoT destaca la computación en la nube (Cloud Computing) [7], la cual ofrece la posibilidad de acceder a una amplia variedad de servicios a través de Internet, con la ventaja de que los datos provistos por dispositivos y usuarios sean accedidos desde cualquier lugar y estar disponibles sin requerir de una instalación de software previa en un dispositivo, reduciendo los costos que esto conlleva. Una de las principales falencias encontradas en el estado del arte es la falta de distinción entre las arquitecturas IoT en el borde y en la niebla, siendo términos que los propios autores deciden intercambiar y asumir como iguales [8,9,10]. La computación en el borde (Edge Computing) y la computación en la niebla (Fog Computing) son arquitecturas que extienden la arquitectura del Cloud Computing.…”

Section: Introductionunclassified

Criterios de ubicación de algoritmos de inteligencia computacional en la web de las cosas: Revisión sistemática de la literatura

Parra

Zambrano

Cobos

2021

Investigación e Innovación en Ingenierías

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Objetivo: Determinar y caracterizar el estado actual del conocimiento acerca de los criterios de ubicación del procesamiento de datos, utilizando técnicas de inteligencia computacional en un Ecosistema de Objetos Inteligentes de la Web de las Cosas. Metodología: La revisión sistemática que se presenta a continuación se basa en los estudios realizados por Petersen y Kitchenham , se plantearon cuatro preguntas de investigación, se aplicó el método PICOC para identificar las palabras clave, se planteó una cadena de búsqueda y cuatro motores de búsqueda, se plantearon los criterios de inclusión y exclusión de estudios primarios, así como los criterios de evaluación de la calidad, la estrategia de extracción de datos y el método de síntesis. Resultados: Se lograron responder las cuatro preguntas de investigación planteadas, encontrando que la mayoría de los estudios carecen de una implementación en las tres ubicaciones analizadas y un único estudio que compara el desempeño obtenido por un algoritmo de inteligencia computacional al procesar información en distintas ubicaciones del ecosistema. Conclusiones: Se demostró la necesidad de continuar realizando estudios en el área de la localización del procesamiento en ecosistemas inteligentes utilizando técnicas de inteligencia computacional para el procesamiento en distintas ubicaciones. Además, se evidencia una necesidad en hacer un mayor énfasis en la comparativa del rendimiento obtenido al realizar implementaciones teniendo en cuenta distintas técnicas de inteligencia computacional.

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Dynamic decision support for resource offloading in heterogeneous Internet of Things environments

Cited by 30 publications

References 34 publications

Fuzzy‐Based Mobile Edge Orchestrators in Heterogeneous IoT Environments: An Online Workload Balancing Approach

Fuzzy‐Based Mobile Edge Orchestrators in Heterogeneous IoT Environments: An Online Workload Balancing Approach

Deep Learning Anomaly Detection for Cellular IoT With Applications in Smart Logistics

Criterios de ubicación de algoritmos de inteligencia computacional en la web de las cosas: Revisión sistemática de la literatura

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