Proactive Tasks Management based on a Deep Learning Model
- URL: http://arxiv.org/abs/2007.12857v2
- Date: Fri, 31 Jul 2020 14:27:47 GMT
- Title: Proactive Tasks Management based on a Deep Learning Model
- Authors: Kostas Kolomvatsos, Christos Anagnotopoulos
- Abstract summary: We propose an intelligent, proactive tasks management model based on the demand.
We rely on a Deep Machine Learning (DML) model and more specifically on a Long Short Term Memory (LSTM) network.
We provide numerical results and reveal that the proposed scheme is capable of deciding on the fly while concluding the most efficient allocation.
- Score: 9.289846887298852
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Pervasive computing applications deal with intelligence surrounding users
that can facilitate their activities. This intelligence is provided in the form
of software components incorporated in embedded systems or devices in close
distance with end users.One example infrastructure that can host intelligent
pervasive services is the Edge Computing (EC) infrastructure. EC nodes can
execute a number of tasks for data collected by devices present in the Internet
of Things (IoT) infrastructure. In this paper, we propose an intelligent,
proactive tasks management model based on the demand. Demand depicts the number
of users or applications interested in using the available tasks in EC nodes,
thus, characterizing their popularity. We rely on a Deep Machine Learning (DML)
model and more specifically on a Long Short Term Memory (LSTM) network to learn
the distribution of demand indicators for each task and estimate the future
interest. This information is combined with historical observations and support
a decision making scheme to conclude which tasks will be offloaded due to
limited interest on them. We have to notice that in our decision making, we
also take into consideration the load that every task may add to the processing
node where it will be allocated. The description of our model is accompanied by
a large set of experimental simulations for evaluating the proposed mechanism.
We provide numerical results and reveal that the proposed scheme is capable of
deciding on the fly while concluding the most efficient allocation.
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