Distributed Uniform Streaming Framework: Towards an Elastic Fog Computing Platform for Event Stream Processing

Vanneste, Simon; Hoog, Jens de; Huybrechts, Thomas; Bosmans, Stig; Sharif, Muddsair; Mercelis, Siegfried; Hellinckx, Peter

doi:10.1007/978-3-030-02607-3_39

Cited by 6 publications

(6 citation statements)

References 9 publications

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“…Increasing the number of configurable parameters available for the tasks at hand improves the gain which can be used by an advanced planner. For example, Distributed Uniform Streaming (DUST) is designed with this high configurability in mind [19]. DUST is a middleware intended to simplify the creation and maintenance of distributed software.…”

Section: Planmentioning

confidence: 99%

“…Moreover, the application testbench can provide application bandwidth requirements. Using each application's communication configuration provided by the DUST framework [19], the testbench can discover and connect to these communication channels and measure the amount of data generated to determine the worst case bandwidth consumption. All this information is provided to the coordinator, to provide more accurate and optimal placements.…”

Section: E Application Testbenchmentioning

confidence: 99%

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A Generalized Approach For Practical Task Allocation Using A MAPE-K Control Loop

Eyckerman

Reiter

Mercelis

et al. 2021

2021 International Conference on Information and Communication Technology Convergence (ICTC)

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Fog and edge computing paradigms were adopted to enable Internet of Things (IoT) applications, improving response time and reducing network load. Task allocation algorithms are used on IoT-enabled networks to determine the optimal software placement. However, managing such a network is considerably more complex than allocating the tasks. To simplify management, we propose a general Monitor -Analyze -Plan -Execute over a Knowledge base (MAPE-K) framework in which all requirements for task allocation are fulfilled, and where components can easily be adapted to the use case at hand. This research identifies several pitfalls and proposes solutions. Additionally, we apply this approach to a distributed testbed, comparing it to traditional cloud approaches. ACKNOWLEDGMENTThis research received funding from the Flemish Government (AI Research Program). This article describes work in the context of the DEDICAT 6G project under the European Union (EU) H2020 research and innovation programme (Grant Agreement No. 101016499). The contents of this publication are the sole responsibility of the authors and do not in any way reflect the views of the EU.

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Section: Planmentioning

confidence: 99%

Section: E Application Testbenchmentioning

confidence: 99%

A Generalized Approach For Practical Task Allocation Using A MAPE-K Control Loop

Eyckerman

Reiter

Mercelis

et al. 2021

2021 International Conference on Information and Communication Technology Convergence (ICTC)

Self Cite

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“…The development of distributed software provides more challenges than monolithic software, such as message passing and state maintenance. Vanneste et al [8] developed the Distributed Uniform STreaming (DUST) framework as a solution to such challenges This middleware provides a transparent communication layer on top of which distributed applications can be built. Removing the complexity of communication while keeping a high amount of configurability helps to cater both new and experienced distributed software developers.…”

Section: Distributed Software Frameworkmentioning

confidence: 99%

Requirements for distributed task placement in the fog

et al. 2020

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“…Data in this queue is send to the correct application nodes (client) by separate process. [20]. However, our imaging system requires a high computational load and therefore is less suited to implement in such existing frameworks directly.…”

Section: Networkmentioning

confidence: 99%

In-Air Imaging Sonar Sensor Network with Real-Time Processing Using GPUs

Jansen

Laurijssen

Kerstens

et al. 2019

Advances on P2P, Parallel, Grid, Cloud and Internet Computing

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For autonomous navigation and robotic applications, sensing the environment correctly is crucial. Many sensing modalities for this purpose exist. In recent years, one such modality that is being used is in-air imaging sonar. It is ideal in complex environments with rough conditions such as dust or fog. However, like with most sensing modalities, to sense the full environment around the mobile platform, multiple such sensors are needed to capture the full 360-degree range. Currently the processing algorithms used to create this data are insufficient to do so for multiple sensors at a reasonably fast update rate. Furthermore, a flexible and robust framework is needed to easily implement multiple imaging sonar sensors into any setup and serve multiple application types for the data. In this paper we present a sensor network framework designed for this novel sensing modality. Furthermore, an implementation of the processing algorithm on a Graphics Processing Unit is proposed to potentially decrease the computing time to allow for real-time processing of one or more imaging sonar sensors at a sufficiently high update rate.

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Distributed Uniform Streaming Framework: Towards an Elastic Fog Computing Platform for Event Stream Processing

Cited by 6 publications

References 9 publications

A Generalized Approach For Practical Task Allocation Using A MAPE-K Control Loop

A Generalized Approach For Practical Task Allocation Using A MAPE-K Control Loop

Requirements for distributed task placement in the fog

In-Air Imaging Sonar Sensor Network with Real-Time Processing Using GPUs

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