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Note that it is enough to consider resource allocation optimization for any particular operator since base station can transmit data to its own serving users independently without creating interference (with perfect synchronization or adequate guardbands between the fragments) to the users served by other operators.
A novel evolutionary optimization methodology called "Algorithm with Multiple-Input Genetic Operators" (AMIGO) for scheduling independent tasks considering resource and time constraints is presented.
Consider resource mapping.
To put our approach into practice, we consider the coordination of two autonomic managers addressing the resource optimization of a system.
In the future work, we plan to carryout further optimization by considering service-based parameters (e.g., voice, video, and data) to enhance handover efficiency and resource optimization.
However, these systems do not consider spatial constraints or spatial interactions among resources, which can be a crucial factor in resource optimization.
The second problem is related to network resource optimization.
This section presents two autonomic managers dealing with resource optimization.
From real-time management to resource optimization to predictive maintenance, data-driven technology is reshaping the urban landscape.
A search methodology with goal state optimization considering computational resource constraints is proposed.
On the other hand, we should think about resources optimization.
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