A platform as-a-service for hybrid cloud/fog environments

Yangui, Sami; Ravindran, Pradeep; Bibani, Ons; Glitho, Roch; Hadj-Alouane, Nejib Ben; Morrow, Monique; Polakos, P.

doi:10.1109/lanman.2016.7548853

Cited by 84 publications

(72 citation statements)

References 8 publications

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“…Fog is a platform that provides compute, storage and networking services between end devices and cloud computing servers, most commonly, but not exclusively, located at the edge of network [60]. For IoT, this new platform is aimed to decentralize the data processing [288], decrease the latency [289], and bring more reliability for WSN [290]. Finally, Software-Defined Networking (SDN) is a novel concept for IoT, without any growth in the 2011-2012 period, but which was gaining popularity from one publication in 2013, and 2014, to 33 in 2016.…”

Section: Top Trending Topicsmentioning

confidence: 99%

Internet of Things: A Scientometric Review

et al. 2017

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Abstract:Internet of Things (IoT) is connecting billions of devices to the Internet. These IoT devices chain sensing, computation, and communication techniques, which facilitates remote data collection and analysis. wireless sensor networks (WSN) connect sensing devices together on a local network, thereby eliminating wires, which generate a large number of samples, creating a big data challenge. This IoT paradigm has gained traction in recent years, yielding extensive research from an increasing variety of perspectives, including scientific reviews. These reviews cover surveys related to IoT vision, enabling technologies, applications, key features, co-word and cluster analysis, and future directions. Nevertheless, we lack an IoT scientometrics review that uses scientific databases to perform a quantitative analysis. This paper develops a scientometric review about IoT over a data set of 19,035 documents published over a period of 15 years (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) in two main scientific databases (Clarivate Web of Science and Scopus). A Python script called ScientoPy was developed to perform quantitative analysis of this data set. This provides insight into research trends by investigating a lead author's country affiliation, most published authors, top research applications, communication protocols, software processing, hardware, operating systems, and trending topics. Furthermore, we evaluate the top trending IoT topics and the popular hardware and software platforms that are used to research these trends.

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Section: Top Trending Topicsmentioning

confidence: 99%

Internet of Things: A Scientometric Review

et al. 2017

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“…In contrast to Yangui et al [4] and Pahl et al [10], the PaaS architecture proposed by Liyanage et al [5] enables generating the best deployment plan by proposing a component distribution scheme. In addition, they incorporate a publication/discovery mechanism for the underlying node's specifications using Service Description Metadata (SDM).…”

Section: The State-of-the-art and Its Shortcomingsmentioning

confidence: 99%

“…It is responsible for finding the optimal deployment plan of IoT application components over the cloud and fog infrastructures. To that end, it runs a placement algorithm, such as the one presented in [4]. For instance, let us consider both applications presented in Section II, the smart parade application and the smart accident management application.…”

Section: Application Deployment Layermentioning

confidence: 99%

“…Application providers use PaaS to provision (i.e., develop, deploy, manage, and orchestrate) applications in the cloud. However, existing PaaS solutions (including IoT PaaS solutions) usually focus on cloud computing and do not enable the provisioning of applications with components spanning both cloud and fog, e.g., references [4]- [5]. Provisioning applications that span the cloud and fogs requires novel functions such as application graph generation, which are absent from existing cloud PaaS solutions.…”

Section: Introductionmentioning

confidence: 99%

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An IoT Platform-as-a-Service for NFV-Based Hybrid Cloud/Fog Systems

Mouradian

Ebrahimnezhad

Jebbar

et al. 2020

IEEE Internet Things J.

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Cloud computing, despite its inherent advantages (e.g., resource efficiency) still faces several challenges. The wide area network used to connect the cloud to end-users could cause high latency, which may not be tolerable for some applications, especially Internet of Things (IoT) applications. Fog computing can reduce this latency by extending the traditional cloud architecture to the edge of the network and by enabling the deployment of some application components on fog nodes. Application providers use Platform-as-a-Service (PaaS) to provision (i.e., develop, deploy, manage, and orchestrate) applications in cloud. However, existing PaaS solutions (including IoT PaaS) usually focus on cloud and do not enable provisioning of applications with components spanning cloud and fog. Provisioning such applications requires novel functions, such as application graph generation, that are absent from existing PaaS. Furthermore, several functions offered by existing PaaS (e.g., publication/discovery) need to be significantly extended in order to fit in a hybrid cloud/fog environment. In this paper, we propose a novel architecture for PaaS for hybrid cloud/fog system. It is IoT use case-driven, and its applications' components are implemented as Virtual Network Functions (VNFs) with execution sequences modeled as graphs with sub-structures such as selection and loops. It automates the provisioning of applications with components spanning cloud and fog. In addition, it enables the discovery of existing cloud and fog nodes and generates application graphs. A proof of concept is built based on Cloudify open source. Feasibility is demonstrated by evaluating its performance when PaaS modules and application components are placed in clouds and fogs in different geographical locations.

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“…Stream processing is often viewed as a motivating scenario. Yangui et al [109] propose a Platform as a Service (PaaS) architecture for cloud and fog integration. A proof-of-concept implementation is described, which extends Cloud Foundry [110] to ease testing and deployment of applications whose components can be hosted either in the cloud or fog resources.…”

Section: Lightweight Virtualisation and Containersmentioning

confidence: 99%

Distributed data stream processing and edge computing: A survey on resource elasticity and future directions

Assuno

Veith

Buyya

2018

Journal of Network and Computer Applications

254

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Under several emerging application scenarios, such as in smart cities, operational monitoring of large infrastructure, wearable assistance, and Internet of Things, continuous data streams must be processed under very short delays. Several solutions, including multiple software engines, have been developed for processing unbounded data streams in a scalable and efficient manner. More recently, architecture has been proposed to use edge computing for data stream processing. This paper surveys state of the art on stream processing engines and mechanisms for exploiting resource elasticity features of cloud computing in stream processing. Resource elasticity allows for an application or service to scale out/in according to fluctuating demands. Although such features have been extensively investigated for enterprise applications, stream processing poses challenges on achieving elastic systems that can make efficient resource management decisions based on current load. Elasticity becomes even more challenging in highly distributed environments comprising edge and cloud computing resources. This work examines some of these challenges and discusses solutions proposed in the literature to address them.

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A platform as-a-service for hybrid cloud/fog environments

Cited by 84 publications

References 8 publications

Internet of Things: A Scientometric Review

Internet of Things: A Scientometric Review

An IoT Platform-as-a-Service for NFV-Based Hybrid Cloud/Fog Systems

Distributed data stream processing and edge computing: A survey on resource elasticity and future directions

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