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Solutions have been proposed that utilise a mathematical programming method [21] and this method has been applied to a real resource allocation problem [20].
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In that sense, they are fundamentally irrelevant for real resource allocations.
This chapter concerns the use of convex optimization in real-time embedded systems, in areas such as signal processing, automatic control, real-time estimation, real-time resource allocation and decision making, and fast automated trading.
This work presents a new mechanism for real-time resource allocation, order administration, and process monitoring in Collaborative Networked Enterprises CNEE).
The system controller is based on a restrictive (not maximally permissive) deadlock avoidance policy to resolve deadlocks and control the real-time resource allocation decisions in the system.
Figure 4 Example of ranking application for real-time resource allocation of business seat upgrades.
It, particularly, needs to ensure real-time resource allocation and execution in dynamic environments with different resource and service constraints.
To reduce the complexity and achieve efficient real-time resource allocation, cognitive radio networks need to be equipped with learning and reasoning abilities.
By exploiting centralized network intelligence, real-time resource allocation can be implemented in order to adapt to network conditions and user needs [46].
In order to reduce the complexity and achieve efficient real-time resource allocation, cognitive radio networks use machine learning and artificial intelligence.
The decision-making can be based on optimization algorithms; however, in order to reduce the complexity and achieve efficient real-time resource allocation, cognitive radio networks use machine learning and artificial intelligence.
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