A Holistic Monitoring Service for Fog/Edge Infrastructures: A Foresight Study

Abderrahim, Mohamed; Ouzzif, Meryem; Jérôme, François; Lèbre, Adrien

doi:10.1109/ficloud.2017.30

Cited by 24 publications

(35 citation statements)

References 23 publications

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“…5.0.6 Fog Resource Monitoring. Few studies in the literature propose monitoring schemes for fog resources [151]. Monitoring is useful when multiple operators use a fog node, or when a fog node is located in a location where many users use the fog node.…”

Section: Mobile Fogmentioning

confidence: 99%

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Yousefpour

Fung

Nguyen

et al. 2019

Journal of Systems Architecture

1,111

613

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With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of securitycritical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.

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Section: Mobile Fogmentioning

confidence: 99%

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Yousefpour

Fung

Nguyen

et al. 2019

Journal of Systems Architecture

1,111

613

View full text Add to dashboard Cite

show abstract

“…Naha et al [14] suggested that the resource monitoring should able to detect the future needs of the applications to avoid the SLA violation. Liyanage et al [15] has given a foresight study on Fog/Edge infrastructures with requirements and specifications of Fog/Edge monitoring service. The study presented a classification of monitoring architectures, services, and tools of existing Cloud and distributed systems and suggested that our community should propose effective monitoring service in Fog/Edge computing.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Resource Monitoring Service for Fog Computing Environments

Battula

Garg

Montgomery

et al. 2020

IEEE Trans. Serv. Comput.

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With the increasing number of Internet of Things (IoT) devices, the volume and varieties of data being generated by these devices are increasing rapidly. Cloud computing cannot process this data due to its limitations such as latency and scalability. In order to process this data in less time, Fog computing has evolved as an extension to Cloud computing. In a Fog computing environment, a resource monitoring service plays a vital role in providing advanced services such as scheduling, scaling, and migration. Most of the works in Fog computing have assumed that a resource monitoring service is already available. However, there are not any studies, which investigate resource monitoring service in detail for Fog computing environments. Conventional methods proposed for other distributed systems such as Cloud and Grid may not be suitable due to the unique features of a Fog environment such as the limited capacity and heterogeneity of Fog devices. To improve the overall performance of Fog computing and optimise resource usage, effective resource monitoring techniques are required. Hence, we propose a Support and Confidence (SCB) based technique, which optimises the resource usage in the resource monitoring service. The performance of our proposed system is evaluated by examining a real-time traffic use case in a Fog emulator and the results are compared with traditional distributed computing techniques. The experimental results obtained from the Fog Emulator show that the proposed technique consumes fewer resources compared to conventional resource monitoring approaches.

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“…. ) that we identified in our previous study [10]. They may be expressed as chains of elementary algebraic functions such as aggregate, filter, join, split .…”

Section: The Mutualized Placement Problemmentioning

confidence: 99%

“…We proceed using elementary functions deduced from our previous work [10] to express the monitoring processing requests in a unified way. The best composition of functions is then achieved by mutualizing as many functions as possible among the different tenants while taking into account their QoS constraints and the infrastructure capabilities.…”

Section: Introductionmentioning

confidence: 99%

Efficient Resource Allocation for Multi-Tenant Monitoring of Edge Infrastructures

Abderrahim

Ouzzif

Jérôme

et al. 2019

2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)

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By relying on small sized and massively distributed infrastructures, the Edge computing paradigm aims at supporting the low latency and high bandwidth requirements of the next generation services that will leverage IoT devices (e.g., video cameras, sensors). To favor the advent of this paradigm, management services, similar to the ones that made the success of Cloud computing platforms, should be proposed. However, they should be designed in order to cope with the limited capabilities of the resources that are located at the edge. In that sense, they should mitigate as much as possible their footprint. Among the different management services that need to be revisited, we investigate in this paper the monitoring one. Monitoring functions tend to become compute-, storage-and network-intensive, in particular because they will be used by a large part of applications that rely on real-time data. To reduce as much as possible the footprint of the whole monitoring service, we propose to mutualize identical processing functions among different tenants while ensuring their quality-of-service (QoS) expectations. We formalize our approach as a constraint satisfaction problem and show through micro-benchmarks its relevance to mitigate compute and network footprints.

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A Holistic Monitoring Service for Fog/Edge Infrastructures: A Foresight Study

Cited by 24 publications

References 23 publications

All one needs to know about fog computing and related edge computing paradigms: A complete survey

All one needs to know about fog computing and related edge computing paradigms: A complete survey

An Efficient Resource Monitoring Service for Fog Computing Environments

Efficient Resource Allocation for Multi-Tenant Monitoring of Edge Infrastructures

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