Changes between Version 36 and Version 37 of ResourceManagement
- Timestamp:
- 07/02/10 00:48:41 (14 years ago)
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ResourceManagement
v36 v37 3 3 SDR presents a hard real-time computing challenge with varying (computing) system conditions. The framework needs to track the states of the computing resources for being able to take advantage of the reconfiguration capabilities of mobile terminals and network elements. 4 4 5 [[Image(CRM_org1.png, 400px, align=right)]] 5 [[Image(CRM_org1.png, 400px, align=left)]] 6 [[Image(time_slots.png, 500px, align=right)]] 6 7 7 Our computing resource management framework is modular. It features the computing system modeling on the one hand and management mechanisms on the other ( Figure 3). The SDR computing system modeling monitors and models the SDR platforms' computing resources and the SDR applications' computing requirements. We, therefore, suggest equivalent metrics for modeling computing resources and requirements: million operations per second (MOPS) and mega-bits per second (Mbps) for modelling the processing and interprocessor data flow capacities and requirements.8 Our computing resource management framework is modular. It features the computing system modeling on the one hand and management mechanisms on the other (left figure). The SDR computing system modeling monitors and models the SDR platforms' computing resources and the SDR applications' computing requirements. We, therefore, suggest equivalent metrics for modeling computing resources and requirements: million operations per second (MOPS) and mega-bits per second (Mbps) for modelling the processing and interprocessor data flow capacities and requirements. 8 9 9 The ALOE computing resource management is based on two simple time management principles: time slots and pipelining ( Figure 4). This facilitates the synchronized execution of the waveform modules on distributed computing resources, while taking advantage of the continuous data flow that characterizes wireless communications. Based on these principles, ALOE applies general-purpose mapping algorithms and problem-specific cost functions. The cost function implements the computing resource management objective or policy while guiding the allocation of computing resources to computing requirements in a controlled manner.10 The ALOE computing resource management is based on two simple time management principles: time slots and pipelining (right figure). This facilitates the synchronized execution of the waveform modules on distributed computing resources, while taking advantage of the continuous data flow that characterizes wireless communications. Based on these principles, ALOE applies general-purpose mapping algorithms and problem-specific cost functions. The cost function implements the computing resource management objective or policy while guiding the allocation of computing resources to computing requirements in a controlled manner. 10 11 11 12 12 13 14 See the attached document FlexCRM for more information about the ALOE computing resource management approach.
