Dynamic Resource Allocation for Load Balancing in Fog Environment

Xu, Xiaolong; Fu, Shucun; Qing, Cai; Tian, Wei; Liu, Wenjie; Dou, Wanchun; Sun, Xingming; Liu, Alex X.

doi:10.1155/2018/6421607

Cited by 147 publications

(92 citation statements)

References 46 publications

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“…Computing. Xu et al [91] proposed a method called "DRAM" to dynamically allocate resources in fog computing environment, which can avoid both too high and too low loads. e method first analyzes the load balance of different kinds of computing nodes.…”

Section: Fogmentioning

confidence: 99%

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Wang

Sun

et al. 2020

Complexity

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With an increase of service users’ demands on high quality of services (QoS), more and more efficient service computing models are proposed. The development of cloud computing, fog computing, and edge computing brings a number of challenges, e.g., QoS optimization and energy saving. We do a comprehensive survey on QoS optimization and energy saving in cloud computing, fog computing, edge computing, and IoT environments. We summarize the main challenges and analyze corresponding solutions proposed by existing works. This survey aims to help readers have a deeper understanding on the concepts of different computing models and study the techniques of QoS optimization and energy saving in these models.

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Section: Fogmentioning

confidence: 99%

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Wang

Sun

et al. 2020

Complexity

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“…Simple application profiling may be used for resource placement in this context [35]. However, finding an appropriate balance for performance and cost is challenging when the application demands and the availability of resources keeps changing over time, making continuous re-evaluation necessary [58].…”

Section: Flexibility Of Resource Placement and Allocationmentioning

confidence: 99%

A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering

Tanwani

Mor

Kubiatowicz

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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The growing demand of industrial, automotive and service robots presents a challenge to the centralized Cloud Robotics model in terms of privacy, security, latency, bandwidth, and reliability. In this paper, we present a 'Fog Robotics' approach to deep robot learning that distributes compute, storage and networking resources between the Cloud and the Edge in a federated manner. Deep models are trained on non-private (public) synthetic images in the Cloud; the models are adapted to the private real images of the environment at the Edge within a trusted network and subsequently, deployed as a service for lowlatency and secure inference/prediction for other robots in the network. We apply this approach to surface decluttering, where a mobile robot picks and sorts objects from a cluttered floor by learning a deep object recognition and a grasp planning model. Experiments suggest that Fog Robotics can improve performance by sim-to-real domain adaptation in comparison to exclusively using Cloud or Edge resources, while reducing the inference cycle time by 4× to successfully declutter 86% of objects over 213 attempts.

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“…In addition, a dynamic resource allocation method, named DRAM, to perform the load balance for the fog systems is proposed by the authors of [19]. They presented a system framework for IoT applications in fog, and designed a static resource allocation and dynamic service migration strategy.…”

Section: Related Workmentioning

confidence: 99%

TACRM: trust access control and resource management mechanism in fog computing

Daoud

Obaidat

Meddeb-Makhlouf

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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Despite the fact that cloud computing is an effective solution for handling data in distributed environments, it is considered as an appropriate way to efficiently process the mass data generated by IoT devices [1]. It delivers centralized resources for data computation and storage, which can affect metrics like delay and bandwidth limitation [2, 3]. Inherently, Fog nodes are distributed within the proximity of users; a characteristic that reduces latency and establishes adjacent localized connections. Recently, the combination of cloud/fog, and IoT communication networks has received a great attention and widely emerged [4]. IoT exploits the fog computing capacities for virtualizing the tasks of IoT devices, but it still has restricted capability and acquires long delay [5]. Though the primary purpose of Fog paradigm is to achieve all tasks with high performance, the security features must be considered as part of the Fog system to guarantee

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Dynamic Resource Allocation for Load Balancing in Fog Environment

Cited by 147 publications

References 46 publications

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering

TACRM: trust access control and resource management mechanism in fog computing

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