Enabling Data Processing at the Network Edge through Lightweight Virtualization Technologies

Morabito, Roberto; Beijar, Nicklas

doi:10.1109/seconw.2016.7746807

Cited by 44 publications

(31 citation statements)

References 2 publications

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“…Bellavista et al [94] employed Docker-based containers on a Raspberry Pi 1 board that is used as a fog node for collecting data from heterogeneous sensors in a transit vehicle or other infrastructural components. Morabito et al [112] utilized single-board computers, including RaspberryPi 2, Odroid C1+, and Odroid XU4 boards as edge processing devices running Docker containers. ParaDrop [86] adopted lightweight containerization for WiFi Access Points (APs) or other wireless gateways.…”

Section: B System Softwarementioning

confidence: 99%

Resource Management in Fog/Edge Computing

2019

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Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets, is an upcoming computing paradigm, referred to as fog/edge computing. Fog/edge resources are typically resource-constrained, heterogeneous, and dynamic compared to the cloud, thereby making resource management an important challenge that needs to be addressed. This article reviews publications as early as 1991, with 85% of the publications between 2013-2018, to identify and classify the architectures, infrastructure, and underlying algorithms for managing resources in fog/edge computing.

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Section: B System Softwarementioning

confidence: 99%

Resource Management in Fog/Edge Computing

2019

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“…Morabito and Beijar [111] Hochreiner et al [114] propose the VIenna ecosystem for elastic Stream Processing (VISP) which exploits the use of lightweight containers to enable application deployment on hybrid environments (e.g. clouds and edge resources), a graphical interface for easy assemble of processing graphs, and reuse of processing operators.…”

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

257

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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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“…The RAM memory usage analysis defines if the gateway has to deal with memory intensive applications and how the increasing number of devices affects the memory performance. This estimation represents a relevant aspect because it suggests if the gateway could host further memory-intensive applications, intended to specific functionalities requested by a tenant and not covered by this work -e.g., data compression applications [19]. Figure 8a depicts the RAM SBCs memory usage in function of the number of sensor nodes -the result is normalized since the devices feature different RAM capacities.…”

Section: Southbound Interface Performancementioning

confidence: 99%

“…In [19], we proposed the design of an IoT gateway that can be efficiently employed also in edge computing architectures. In that study, we have shown how to efficiently and flexibly use Docker containers in order to customize an IoT platform, which offers several virtualized services that range from: (i) Device Management capabilities; (ii) Software Defined Networking (SDN) support; (iii) Orchestration and Data Management capabilities.…”

Section: Related Workmentioning

confidence: 99%