Data Synopses Management based on a Deep Learning Model
- URL: http://arxiv.org/abs/2008.01560v1
- Date: Sat, 1 Aug 2020 12:04:21 GMT
- Title: Data Synopses Management based on a Deep Learning Model
- Authors: Panagiotis Fountas, Kostas Kolomvatsos, Christos Anagnostopoulos
- Abstract summary: We argue on the delivery of data synopses to EC nodes making them capable to take offloading decisions fully aligned with data present at peers.
Our approach involves a Deep Learning model for learning the distribution of calculated synopses and estimate future trends.
- Score: 14.180331276028662
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Pervasive computing involves the placement of processing services close to
end users to support intelligent applications. With the advent of the Internet
of Things (IoT) and the Edge Computing (EC), one can find room for placing
services at various points in the interconnection of the aforementioned
infrastructures. Of significant importance is the processing of the collected
data. Such a processing can be realized upon the EC nodes that exhibit
increased computational capabilities compared to IoT devices. An ecosystem of
intelligent nodes is created at the EC giving the opportunity to support
cooperative models. Nodes become the hosts of geo-distributed datasets
formulated by the IoT devices reports. Upon the datasets, a number of
queries/tasks can be executed. Queries/tasks can be offloaded for performance
reasons. However, an offloading action should be carefully designed being
always aligned with the data present to the hosting node. In this paper, we
present a model to support the cooperative aspect in the EC infrastructure. We
argue on the delivery of data synopses to EC nodes making them capable to take
offloading decisions fully aligned with data present at peers. Nodes exchange
data synopses to inform their peers. We propose a scheme that detects the
appropriate time to distribute synopses trying to avoid the network overloading
especially when synopses are frequently extracted due to the high rates at
which IoT devices report data to EC nodes. Our approach involves a Deep
Learning model for learning the distribution of calculated synopses and
estimate future trends. Upon these trends, we are able to find the appropriate
time to deliver synopses to peer nodes. We provide the description of the
proposed mechanism and evaluate it based on real datasets. An extensive
experimentation upon various scenarios reveals the pros and cons of the
approach by giving numerical results.
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