A QoS-aware streaming service over fog computing infrastructures

Lai, Chin‐Feng; Song, Dongmei; Hwang, Ren-Hung; Lai, Ying-Xun

doi:10.1109/dmiaf.2016.7574909

Cited by 25 publications

(12 citation statements)

References 8 publications

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“…Additionally, it can be used as a feature that may be used for further evaluation of the reviewed algorithms. When taking into consideration the use of cloud computing, as a solution concept, we should be aware that the data transfer between cloud and clients will contribute towards the increase in feedback latency [ 43 ]. This will lead to restrain the cloud service to provide quality of service to clients [ 43 ].…”

Section: Classification Of Resource Management Algorithms Applied mentioning

confidence: 99%

“…When taking into consideration the use of cloud computing, as a solution concept, we should be aware that the data transfer between cloud and clients will contribute towards the increase in feedback latency [ 43 ]. This will lead to restrain the cloud service to provide quality of service to clients [ 43 ]. The QoS concept is defined in the ITU-T Recommendation E.800 and refers to the following [ 44 ]:…”

Section: Classification Of Resource Management Algorithms Applied mentioning

confidence: 99%

See 1 more Smart Citation

Resource Management Techniques for Cloud/Fog and Edge Computing: An Evaluation Framework and Classification

Mijuskovic

Chiumento

Bemthuis

et al. 2021

Sensors

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Processing IoT applications directly in the cloud may not be the most efficient solution for each IoT scenario, especially for time-sensitive applications. A promising alternative is to use fog and edge computing, which address the issue of managing the large data bandwidth needed by end devices. These paradigms impose to process the large amounts of generated data close to the data sources rather than in the cloud. One of the considerations of cloud-based IoT environments is resource management, which typically revolves around resource allocation, workload balance, resource provisioning, task scheduling, and QoS to achieve performance improvements. In this paper, we review resource management techniques that can be applied for cloud, fog, and edge computing. The goal of this review is to provide an evaluation framework of metrics for resource management algorithms aiming at the cloud/fog and edge environments. To this end, we first address research challenges on resource management techniques in that domain. Consequently, we classify current research contributions to support in conducting an evaluation framework. One of the main contributions is an overview and analysis of research papers addressing resource management techniques. Concluding, this review highlights opportunities of using resource management techniques within the cloud/fog/edge paradigm. This practice is still at early development and barriers need to be overcome.

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Section: Classification Of Resource Management Algorithms Applied mentioning

confidence: 99%

Section: Classification Of Resource Management Algorithms Applied mentioning

confidence: 99%

Resource Management Techniques for Cloud/Fog and Edge Computing: An Evaluation Framework and Classification

Mijuskovic

Chiumento

Bemthuis

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…As mentioned in Lai et al [ 29 ] and Yi et al [ 30 ], fog computing can drastically improve the QoS of IoT networks by decreasing the ‘packet latency’ and ‘network congestion’ while increasing the ‘failure detection’ and ‘loss recovery’.…”

Section: Implications Of Fog Computing Usage In Iotmentioning

confidence: 99%

Hardware Security of Fog End-Devices for the Internet of Things

Bütün

Sari

Österberg

2020

Sensors

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The proliferation of the Internet of Things (IoT) caused new application needs to emerge as rapid response ability is missing in the current IoT end-devices. Therefore, Fog Computing has been proposed to be an edge component for the IoT networks as a remedy to this problem. In recent times, cyber-attacks are on the rise, especially towards infrastructure-less networks, such as IoT. Many botnet attack variants (Mirai, Torii, etc.) have shown that the tiny microdevices at the lower spectrum of the network are becoming a valued participant of a botnet, for further executing more sophisticated attacks against infrastructural networks. As such, the fog devices also need to be secured against cyber-attacks, not only software-wise, but also from hardware alterations and manipulations. Hence, this article first highlights the importance and benefits of fog computing for IoT networks, then investigates the means of providing hardware security to these devices with an enriched literature review, including but not limited to Hardware Security Module, Physically Unclonable Function, System on a Chip, and Tamper Resistant Memory.

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“…This type of the model gave the ability of wide base of ubiquitous, context-aware, and real-time services. The [39] introduced a quality of service-aware streaming via fog a computing infrastructure which has been suggested for relieving the delivery issues of conventional content via the adapting of videos to existing conditions of the network and probably utilizing local resources of computing. Fog computing has been modeled for extending cloud network edge for the sake of decreasing network congestion and latency.…”

Section: Overview Of Streaming Fog Computing Systemsmentioning

confidence: 99%

A Study on Data Streaming in Fog Computing Environment

Jameel¹,

Croock²

2019

IJCI

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In lately years, data streaming is become more important day by day, considering technologies employed to serve that manner and share number of terminals within the system either direct or indirect interacting with them. Smart devices now play active role in the data streaming environment as well as fog and cloud compatibility. It is affecting the data collectivity and appears clearly with the new technologies provided and the increase for the number of the users of such systems. This is due to the number of the users and resources available system start to employ the computational power to the fog for moving the computational power to the network edge. It is adopted to connect system that streamed data as an object. Those inter-connected objects are expected to be producing more significant data streams, which are produced at unique rates, in some cases for being analyzed nearly in real time. In the presented paper a survey of data streaming systems technologies is introduced. It clarified the main notions behind big data stream concepts as well as fog computing. From the presented study, the industrial and research communities are capable of gaining information about requirements for creating Fog computing environment with a clearer view about managing resources in the Fog. The main objective of this paper is to provide short brief and information about Data Streaming in Fog ComputingEnvironment with explaining the major research field within this meaning.

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A QoS-aware streaming service over fog computing infrastructures

Cited by 25 publications

References 8 publications

Resource Management Techniques for Cloud/Fog and Edge Computing: An Evaluation Framework and Classification

Resource Management Techniques for Cloud/Fog and Edge Computing: An Evaluation Framework and Classification

Hardware Security of Fog End-Devices for the Internet of Things

A Study on Data Streaming in Fog Computing Environment

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