Changes between Version 55 and Version 56 of ResourceManagement
- Timestamp:
- 07/23/11 19:14:48 (13 years ago)
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ResourceManagement
v55 v56 11 11 The ALOE computing resource management is based on two simple time management principles: time slots and pipelining (Figure 2). 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 a general-purpose mapping algorithm, the ''tw''-mapping, and a problem-specific cost function. The cost function implements the computing resource management objective or policy that guides the allocation of computing resources to computing requirements in a controlled manner. 12 12 13 The ''tw''-mapping, the cost function, and further details about the ALOE computing resource management approach can be found in the attached document. [attachment:FlexCRM ,July11.pdf Click here to download it].13 The ''tw''-mapping, the cost function, and further details about the ALOE computing resource management approach can be found in the attached document. [attachment:FlexCRM_July11.pdf Click here to download it]. 14 14 15 The computing resource management API and a simulation test suite with different mapping algorithms and scenarios can be downloaded following [attachment: ALOE framework, July11.zip this link]. The [attachment:CRM tools,July11.zip CRM tools] permit executing the computing resource management framework from Matlab with custom computing system models, following the mapping process and analyzing the results. [wiki:ALOEedu ALOE Sessions 7 and 8] introduce the framework and its tools. They provide practical experience with the framework for education and research.15 The computing resource management API and a simulation test suite with different mapping algorithms and scenarios can be downloaded following [attachment:CRMframework_July11.zip this link]. The [attachment:CRMtools_July11.zip CRM tools] permit executing the computing resource management framework from Matlab with custom computing system models, following the mapping process and analyzing the results. [wiki:ALOEedu ALOE Sessions 7 and 8] introduce the framework and its tools. They provide practical experience with the framework for education and research. 16 16 17 17 We currently investigate how to minimize the pipelining latency at the mapping and scheduling stages. Therefore, we examine new cost functions and evaluate their performance in different computing resource management scenarios. We, furthermore, address the scalability of the ''tw''-mapping with the objective of applying it to large processor arrays and multiprocessor systems-on-chip (MPSoCs). We work on extensions of the ''tw''-mapping algorithm itself, but, also, on pre and postprocessing add-ons.
