ENORM: A Framework For Edge NOde Resource Management

Wang, Nan; Varghese, Blesson; Matthaiou, Michail; Nikolopoulos, Dimitrios S.

doi:10.1109/tsc.2017.2753775

Cited by 134 publications

(135 citation statements)

References 37 publications

(46 reference statements)

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“…Moreover, distributing the intelligence across the fog nodes is challenging since most of the neural network, artificial intelligence and machine learning algorithms require high processing power. Current research addresses this issue by implementing different optimization strategies which prioritize different aspects, such as a number of resources without violating application QoS [6], subjective notions of the value of data to the user to decide the location of data processing [31], or prioritising the device's primary function over offloaded workloads [1].…”

Section: Distributionmentioning

confidence: 99%

Exploring the Effectiveness of Service Decomposition in Fog Computing Architecture for the Internet of Things

Alturki

Reiff-Marganiec

Perera

et al. 2022

IEEE Trans. Sustain. Comput.

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The Internet of Things (IoT) aims to connect everyday physical objects to the internet. These objects will produce a significant amount of data. The traditional cloud computing architecture aims to process data in the cloud. As a result, a significant amount of data needs to be communicated to the cloud. This creates a number of challenges, such as high communication latency between the devices and the cloud, increased energy consumption of devices during frequent data upload to the cloud, high bandwidth consumption, while making the network busy by sending the data continuously, and less privacy because of less control on the transmitted data to the server. Fog computing has been proposed to counter these weaknesses. Fog computing aims to process data at the edge and substantially eliminate the necessity of sending data to the cloud. However, combining the Service Oriented Architecture (SOA) with the fog computing architecture is still an open challenge. In this paper, we propose to decompose services to create linked-microservices (LMS). Linked-microservices are services that run on multiple nodes but closely linked to their linked-partners. Linked-microservices allow distributing the computation across different computing nodes in the IoT architecture. Using four different types of architectures namely cloud, fog, hybrid and fog+cloud, we explore and demonstrate the effectiveness of service decomposition by applying four experiments to three different type of datasets. Evaluation of the four architectures shows that decomposing services into nodes reduce the data consumption over the network by 10% -70%. Overall, these results indicate that the importance of decomposing services in the context of fog computing for enhancing the quality of service.

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Section: Distributionmentioning

confidence: 99%

Exploring the Effectiveness of Service Decomposition in Fog Computing Architecture for the Internet of Things

Alturki

Reiff-Marganiec

Perera

et al. 2022

IEEE Trans. Sustain. Comput.

View full text Add to dashboard Cite

show abstract

“…Wang et al presented a model for edge node resource management in a fog system. In the proposed framework, to manage resources in edge nodes, provisioning and auto‐scaling mechanisms have been applied.…”

Section: Classification Of the Selected Approachesmentioning

confidence: 99%

Quality of service‐aware approaches in fog computing

Kashani

Rahmani

Navimipour

2020

Int J Communication

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Summary In recent years, fog computing, a novel paradigm, has emerged for location and latency‐sensitive applications. It is a powerful complement for cloud computing that enables provisioning services and resources outside the cloud near the end devices. In a fog system, the existence of several nonhomogenous devices, which are potentially mobile, led to quality of service (QoS) worries. QoS‐aware approaches are presented in various parts of the fog system, and several different QoS factors are taken into account. In spite of the importance of QoS in fog computing, no comprehensive study on QoS‐aware approaches exists in fog computing. Hence, this paper reviews the current research used to guarantee QoS in fog computing. This paper investigates the QoS‐ensuring techniques that fall into three categories: service/resource management, communication management, and application management (published between 2013 and October 2018). Regarding the selected approaches, this paper represents merits, demerits, tools, evaluation types, and QoS factors. Finally, on the basis of the reviewed studies, we suggest some open issues and challenges which are worth further studying and researching in QoS‐aware approaches in fog computing.

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“…Wang et al [11] proposed a framework named ENORM for resource management in fog computing environment. A novel auto-scaling mechanism for managing the edge resources is studied, which can reduce the latency of target applications and improve the QoS.…”

Section: Related Workmentioning

confidence: 99%

“…Papers [6][7][8]10] only consider the energy and delay as optimization objects that ignore the management of cloud resources. Moreover, some works [11,12,14] focus on resource management for task offloading, which do not consider utilization rate of cloud resources. These works rarely consider utilization rate of PM in cloud server and delay caused by arranging the offloaded task to different PMs in the cloud simultaneously.…”

Section: Related Workmentioning

confidence: 99%

A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay

Wang

Ren

2018

Future Internet

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Abstract:Mobile devices could augment their ability via cloud resources in mobile cloud computing environments. This paper developed a novel two-layered reinforcement learning (TLRL) algorithm to consider task offloading for resource-constrained mobile devices. As opposed to existing literature, the utilization rate of the physical machine and the delay for offloaded tasks are taken into account simultaneously by introducing a weighted reward. The high dimensionality of the state space and action space might affect the speed of convergence. Therefore, a novel reinforcement learning algorithm with a two-layered structure is presented to address this problem. First, k clusters of the physical machines are generated based on the k-nearest neighbors algorithm (k-NN). The first layer of TLRL is implemented by a deep reinforcement learning to determine the cluster to be assigned for the offloaded tasks. On this basis, the second layer intends to further specify a physical machine for task execution. Finally, simulation examples are carried out to verify that the proposed TLRL algorithm is able to speed up the optimal policy learning and can deal with the tradeoff between physical machine utilization rate and delay.

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ENORM: A Framework For Edge NOde Resource Management

Cited by 134 publications

References 37 publications

Exploring the Effectiveness of Service Decomposition in Fog Computing Architecture for the Internet of Things

Exploring the Effectiveness of Service Decomposition in Fog Computing Architecture for the Internet of Things

Quality of service‐aware approaches in fog computing

A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay

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