Support for user-centric modular real-time resource management in the Rialto operating system

1995 ◽  
pp. 53-63 ◽  
Author(s):  
Michael B. Jones ◽  
Paul J. Leach ◽  
Richard P. Draves ◽  
Joseph S. Barrera
Keyword(s):  
Operating System ◽  
Resource Management ◽  
Real Time ◽  
User Centric ◽  
Time Resource

scholarly journals Sparse Beamforming for Real-Time Resource Management and Energy Trading in Green C-RAN

2017 ◽  
Vol 8 (4) ◽  
pp. 2022-2031 ◽  
Author(s):  
Wan Nur Suryani Firuz Wan Ariffin ◽  
Xinruo Zhang ◽  
Mohammad Reza Nakhai
Keyword(s):  
Resource Management ◽  
Real Time ◽  
Energy Trading ◽  
Sparse Beamforming ◽  
Time Resource

scholarly journals Intelligent Dynamic Real-Time Spectrum Resource Management for Industrial IoT in Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7902
Author(s):  
Deok-Won Yun ◽  
Won-Cheol Lee
Keyword(s):  
Resource Allocation ◽  
Resource Management ◽  
Real Time ◽  
Time Spectrum ◽  
Dynamic Spectrum ◽  
Case Based Reasoning ◽  
Industrial Iot ◽  
Frequency Domains ◽  
Time Resource ◽  
Spectrum Resource Management

Intelligent dynamic spectrum resource management, which is based on vast amounts of sensing data from industrial IoT in the space–time and frequency domains, uses optimization algorithm-based decisions to minimize levels of interference, such as energy consumption, power control, idle channel allocation, time slot allocation, and spectrum handoff. However, these techniques make it difficult to allocate resources quickly and waste valuable solution information that is optimized according to the evolution of spectrum states in the space–time and frequency domains. Therefore, in this paper, we propose the implementation of intelligent dynamic real-time spectrum resource management through the application of data mining and case-based reasoning, which reduces the complexity of existing intelligent dynamic spectrum resource management and enables efficient real-time resource allocation. In this case, data mining and case-based reasoning analyze the activity patterns of incumbent users using vast amounts of sensing data from industrial IoT and enable rapid resource allocation, making use of case DB classified by case. In this study, we confirmed a number of optimization engine operations and spectrum resource management capabilities (spectrum handoff, handoff latency, energy consumption, and link maintenance) to prove the effectiveness of the proposed intelligent dynamic real-time spectrum resource management. These indicators prove that it is possible to minimize the complexity of existing intelligent dynamic spectrum resource management and maintain efficient real-time resource allocation and reliable communication; also, the above findings confirm that our method can achieve a superior performance to that of existing spectrum resource management techniques.

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