Cost Efficient Resource Management in Fog Computing Supported Medical Cyber-Physical System

Gu, Lin; Zeng, Deze; Guo, Song; Barnawi, Ahmed; Xiang, Yong

doi:10.1109/tetc.2015.2508382

Cited by 344 publications

(199 citation statements)

References 19 publications

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“…There are two papers that solely considered the network delay. 40 the application service by measuring the allocated time slots, and the results proved the reduction of the high delays of requesting the services to the cloud provider. In some other cases, the network latency is not measured directly, and indicators such as the hop count are considered.…”

Section: Network Delay and Execution Timementioning

confidence: 80%

“…The resource capacity is usually modelled as a vector (or set) of elements, one for each of the hardware elements considered in the fog devices or the network. Examples of those vectors for the case of the fog devices are: considering CPU 24,58 ; considering CPU and storage 38 ; CPU, RAM, and storage 30,31,40,47 ; considering only storage. 39,41,45 Examples of resource capacity vectors both for fog nodes and network are: considering CPU, RAM, and network bandwidth 36,55 ; considering CPU, RAM, storage, and network bandwidth 33,50 ; considering processing and transmission capacities.…”

Section: Node Constraintsmentioning

confidence: 99%

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How to place your apps in the fog: State of the art and open challenges

et al. 2019

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Summary Fog computing aims at extending the cloud towards the Internet of things so to achieve improved quality of service and to empower latency‐sensitive and bandwidth‐hungry applications. The fog calls for novel models and algorithms to distribute multiservice applications in such a way that data processing occurs wherever it is best placed, based on both functional and nonfunctional requirements. This survey reviews the existing methodologies to solve the application placement problem in the fog, while pursuing three main objectives. First, it offers a comprehensive overview on the currently employed algorithms, on the availability of open‐source prototypes and on the size of test use cases. Second, it classifies the literature based on the application and fog infrastructure characteristics that are captured by available models, with a focus on the considered constraints and the optimized metrics. Finally, it identifies some open challenges in application placement in the fog.

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Section: Network Delay and Execution Timementioning

confidence: 80%

Section: Node Constraintsmentioning

confidence: 99%

How to place your apps in the fog: State of the art and open challenges

et al. 2019

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“…Fog resource/service management includes complicated policies and decisions for multi‐objective optimization. Unpredictable interactions, complexity, and geographical span with the system make this task demanding . In Section 5.1.1, the selected resource/service management–based approaches have been analyzed, and in Section 5.1.2, a summary of resource/service management–based approaches has been presented.…”

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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“…The major objective of resource management techniques in the fog‐enabled embedded systems are task completion time minimization . Some embedded applications also demand the execution with minimum cost without compromising QoS values . The orchestration algorithms in vehicular fog environments must be able to dynamically adapt to the changes in network environments.…”

Section: Fog Computing Aspectsmentioning

confidence: 99%

“…91 Some embedded applications also demand the execution with minimum cost without compromising QoS values. 58 The orchestration algorithms in vehicular fog environments must be able to dynamically adapt to the changes in network environments. Time-based dynamic scheduling algorithms are more effective compared with other approaches.…”

Section: Other Orchestration Approachesmentioning

confidence: 99%

Elucidating the challenges for the praxis of fog computing: An aspect‐based study

Martin

Kandasamy

Chandrasekaran

et al. 2019

Int J Communication

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Summary The evolutionary advancements in the field of technology have led to the instigation of cloud computing. The Internet of Things paradigm stimulated the extensive use of sensors distributed across the network edges. The cloud datacenters are assigned the responsibility for processing the collected sensor data. Recently, fog computing was conceptuated as a solution for the overwhelmed narrow bandwidth. The fog acts as a complementary layer that interplays with the cloud and edge computing layers, for processing the data streams. The fog paradigm, as any distributed paradigm, has its set of inherent challenges. The fog environment necessitates the development of management platforms that effectuates the orchestration of fog entities. Owing to the plenitude of research efforts directed toward these issues in a relatively young field, there is a need to organize the different research works. In this study, we provide a compendious review of the research approaches in the domain, with special emphasis on the approaches for orchestration and propose a multilevel taxonomy to classify the existing research. The study also highlights the application realms of fog computing and delineates the open research challenges in the domain.

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Cost Efficient Resource Management in Fog Computing Supported Medical Cyber-Physical System

Cited by 344 publications

References 19 publications

How to place your apps in the fog: State of the art and open challenges

How to place your apps in the fog: State of the art and open challenges

Quality of service‐aware approaches in fog computing

Elucidating the challenges for the praxis of fog computing: An aspect‐based study

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