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If no suitable nodes are identified, then this means that certain decision-making requests will have to be dropped.
The main contribution of this article is the introduction of a novel algorithmic framework for load balancing of user decision-making requests, integrating prediction schemes.
The latter is based on the proposed metric of user satisfaction; such metric is a function of the network response time for serving the decision-making requests.
The key part of this work is that the predicted values of the user satisfaction are used to proactively trigger the load balancing of the decision-making requests.
The low computational complexity of the prediction schemes yields a lightweight framework for load balancing which enables the optimal proactive management of the decision-making requests.
At this point, we should point out that the proposed scheme enables the management of decision-making requests coming from both reconfigurable and autonomous mobile devices.
Based on our previous work [1], the system capacity is defined as the number of simultaneous decision-making requests that can be handled by the network.
The target application system consists of four network nodes, simulating the eNodeBs in an LTE architecture that manages the decision-making requests originating from mobile devices.
We have analysed the administration of the users' decision-making requests according to their originating class, considering requests coming from reconfigurable and autonomous mobile devices.
To this end, a lightweight management mechanism of the decision-making requests is proposed, which allows for their specialised administration according to the type of originating mobile device.
On the network side, we focus on the nodes that receive the decision-making requests (e.g. eNB): the latter incorporate decision-making functionality for the requests handling.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com