Fog computing and its role in the internet of things

Bonomi, Flavio; Milito, Rodolfo A.; Zhu, Jiang; Addepalli, Sateesh

doi:10.1145/2342509.2342513

Cited by 5,068 publications

(2,704 citation statements)

References 3 publications

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“…The European project TROPIC [15] pursued the idea of bringing cloud capabilities closer to the mobile devices. A cloud brought close to the ground to support smart things is referred to as Fog computing, which is a concept introduced by Cisco [16]. The service can be provided by the Nano-Servers (NSs), which are usually positioned between the cloud and smart devices.…”

Section: Related Workmentioning

confidence: 99%

Energy efficient Nano Servers provisioning for Information Piece Delivery in a vehicular environment

Igder

Bhattacharya

Qazi

et al. 2017

Vehicular Communications

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In this paper, we propose energy efficient Information Piece Delivery (IPD) through Nano Servers (NSs) in a vehicular network. Information pieces may contain any data that needs to be communicated to a vehicle. The available power (renewable or non-renewable) for a NS is variable. As a result, the service rate of a NS varies linearly with the available energy within a given range. Our proposed system therefore exhibits energy aware rate adaptation (RA), which uses variable transmission energy. We have also developed another transmission energy saving method for comparison, where sleep cycles (SC) are employed. Both methods are compared against an acceptable download time. To reduce the operational energy, we first optimise the locations of the NSs by developing a mixed integer linear programming (MILP) model, which takes into account the hourly variation of the traffic. The model is validated through a Genetic Algorithm (GA1). Furthermore, to reduce the gross delay over the entire vehicular network, the available renewable energy (wind farm) is optimally allocated to each NS according to piece demand. This, in turn, also reduces the network carbon footprint. A Genetic Algorithm (GA2) is also developed to validate the MILP results associated with this system. Through transmission energy savings, RA and SC further reduce the NSs energy consumption by 19% and 18% respectively, however at the expense of higher download time. MILP model 4 (with RA) and model 5 (with SC) reduced the delay by 81% and 83% respectively, while minimising the carbon footprint by 96% and 98% respectively, compared to the initial MILP model.

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Section: Related Workmentioning

confidence: 99%

Energy efficient Nano Servers provisioning for Information Piece Delivery in a vehicular environment

Igder

Bhattacharya

Qazi

et al. 2017

Vehicular Communications

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show abstract

“…For instance, the sensing application can be located in a proximate cloud facility that is typically at the network edges (the so called Fog computing [1]), the data analysis application in a distant and resource-rich cloud data center, and the visualization application in a different remote cloud providing high computing power.…”

Section: Example Scenariomentioning

confidence: 99%

Decentralized Planning for Self-Adaptation in Multi-cloud Environment

Ismail

Cardellini

2015

Communications in Computer and Information Science

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Abstract. The runtime management of Internet of Things (IoT) oriented applications deployed in multi-clouds is a complex issue due to the highly heterogeneous and dynamic execution environment. To effectively cope with such an environment, the cross-layer and multi-cloud effects should be taken into account and a decentralized self-adaptation is a promising solution to maintain and evolve the applications for quality assurance. An important issue to be tackled towards realizing this solution is the uncertainty effect of the adaptation, which may cause negative impact to the other layers or even clouds. In this paper, we tackle such an issue from the planning perspective, since an inappropriate planning strategy can fail the adaptation outcome. Therefore, we present an architectural model for decentralized self-adaptation to support the crosslayer and multi-cloud environment. We also propose a planning model and method to enable the decentralized decision making. The planning is formulated as a Reinforcement Learning problem and solved using the Q-learning algorithm. Through simulation experiments, we conduct a study to assess the effectiveness and sensitivity of the proposed planning approach. The results show that our approach can potentially reduce the negative impact on the cross-layer and multi-cloud environment.

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“…Bonomi et al [7], in their paper on Fog Computing for the IoT, introduce the defining characteristics and requirements of Fog Computing systems as 1) interoperability to support heterogeneous devices and data, 2) low latency/high performance for streaming data and real-time applications and 3) distributed infrastructures that have the ability to scale horizontally due to the potential wide-spread distribution and large number of resource-constrained Fog nodes.…”

Section: Introductionmentioning

confidence: 99%

“…The contribution of this paper is to propose a new Fog Computing architecture, Eywa, and provide the components for stream processing with it, focused on streams and data interoperability and to evaluate it based on the key metrics of interoperability, performance and scalability (measures of a Fog Computing system as defined by Bonomi et al [7]) by:…”

Section: Introductionmentioning

confidence: 99%

Ewya: An Interoperable Fog Computing Infrastructure with RDF Stream Processing

2017

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Abstract. Fog computing is an emerging technology for the Internet of Things (IoT) that aims to support processing on resource-constrained distributed nodes in between the sensors and actuators on the ground and compute clusters in the cloud. Fog Computing benefits from low latency, location awareness, mobility, wide-spread deployment and geographical distribution at the edge of the network. However, there is a need to investigate, optimise for and measure the performance, scalability and interoperability of resource-constrained Fog nodes running real-time applications and queries on streaming IoT data before we can realise these benefits. With Eywa, a novel Fog Computing infrastructure, we 1) formally define and implement a means of distribution and control of query workload with an inverse publish-subscribe and push mechanism, 2) show how data can be integrated and made interoperable through organising data as Linked Data in the Resource Description Format (RDF), 3) test if we can improve RDF Stream Processing query performance and scalability over state-of-the-art engines with our approach to query translation and distribution for a published IoT benchmark on resource-constrained nodes and 4) position Fog Computing within the Internet of the Future.

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Fog computing and its role in the internet of things

Cited by 5,068 publications

References 3 publications

Energy efficient Nano Servers provisioning for Information Piece Delivery in a vehicular environment

Energy efficient Nano Servers provisioning for Information Piece Delivery in a vehicular environment

Decentralized Planning for Self-Adaptation in Multi-cloud Environment

Ewya: An Interoperable Fog Computing Infrastructure with RDF Stream Processing

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