Low-Latency Service Data Aggregation Using Policy Obligations

Reiff-Marganiec, Stephan; Tilly, Marcel; Janicke, Helge

doi:10.1109/icws.2014.80

Cited by 8 publications

(8 citation statements)

References 19 publications

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“…Latency-aware: Another important metric that is often considered in edge-based systems for aggregation includes latency [24], [25]. A mediation architecture has been proposed in the context of data services for reducing latency [26]. In this architecture, policies for filtering data produced by the source based on concepts of complex event processing are proposed.…”

Section: Decoupled Techniquesmentioning

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: Decoupled Techniquesmentioning

confidence: 99%

Resource Management in Fog/Edge Computing

2019

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“…While Storm stream management is based on an operator-defined and static graph topology, the architecture proposed in the remainder of this paper is extremely dynamic, as the number of nodes and edges in the Graph Framework can change according to the workload and listener's requirements. The work described in [6] address the problem to process, procure, and provide information related to the IoT scenario with almost zero latency. The authors propose a publish/subscribe architecture is based on the concept of a Mediator, which encapsulates the processing of the incoming requests from the consumer and the incoming events from the services side.…”

Section: Related Workmentioning

confidence: 99%

Big Stream Cloud Architecture for the Internet of Things

Belli

2015

Proceedings of the 2015 on MobiSys PhD Forum

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The Internet of Things (IoT) will consist of billions of interconnected devices denoted as "Smart Objects:" (SOs) tiny, constrained devices which are going to be pervasively deployed in several contexts. The actors involved in IoT scenarios have extremely heterogeneous characteristics (in terms of processing and communication capabilities, energy supply and consumption, availability, and mobility), spanning from constrained SOs, to smartphones and other personal devices, Internet hosts, and the Cloud. SOs are typically equipped with sensors and/or actuators and are thus capable to perceive and act on the environment where they are deployed. By 2020, 50 billions of SOs are expected to be deployed in urban, home, industrial, and rural scenarios [3], in order to collect relevant information, which may be used to build new useful applications. In a typical IoT scenario, sensed data are collected by SOs, deployed in and populating the IoT network, and sent uplink to collection entities as the Cloud.With billions of nodes capable of gathering data and generating information, the availability of efficient and scalable mechanisms for collecting, processing, and storing data is crucial. Big Data techniques, which were developed in the last few years, address the need to process extremely large amounts of heterogeneous data for multiple purposes. These techniques have been designed mainly to deal with huge volumes of information (focusing on storage, aggregation, analysis, and provisioning of data), rather than to provide real-time processing and dispatching. One of the distinctive features of IoT systems is the deployment of a huge amount of heterogeneous data sources collecting data from the environment and sending information through the internet to collectors. The work of all data sources generate, as a whole, streams with a very high frequency. Moreover, several relevant IoT scenarios need real-time or predictable latency. The number of data sources, on one side, and the subsequent frequency of incoming data, on the other side, create a new need for Cloud architectures to handle such massive information flows. Big Data approaches typically have an intrinsic

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“…(2) Latency-conscious Latency is one of the significant benchmarks frequently examined in edge-based systems for aggregation [76,77]. It can be reduced by proposing an intermediary architecture concerning data services [78].…”

Section: ) Optimization Of Bandwidthmentioning

confidence: 99%