Changes between Version 56 and Version 57 of ResourceManagement

Timestamp:

07/24/11 15:54:19 (13 years ago)

Author:

vuk (IP: 87.221.3.14)

Comment:

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ResourceManagement

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 [[Image(CRM.png, 550px, align=right)]]
 
-SDR presents a hard real-time computing challenge with varying (computing) system conditions. The computing resource management framework thus 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.
+SDR presents a hard real-time computing challenge with varying (computing) system conditions. The computing resource management framework thus needs to track the states of the computing resources and allocate or reallocate them as needed.
 
-An SDR processing chain, SDR application or waveform, is the part of an SDR transceiver that is implemented in software. It can be understood as a set of concurrent processes that continuously process and propagate real-time data. Such a processing chain is not specifically tailored but rather executable on any general-purpose platform with sufficient computing capacity. Therefore, an automatic mapping process needs to dynamically assign software modules to hardware resources, while meeting all computing system constraints.
+An SDR processing chain, SDR application or waveform, is the part of an SDR transceiver that is implemented in software. It can be understood as a set of concurrent processes that continuously process and propagate real-time data. An automatic mapping process (computing resource manager) dynamically assigns software modules to hardware resources, while meeting all computing system constraints.
 
-Our computing resource management framework is modular. It features the computing system modeling on the one hand and the computing resource management  on the other (Figure 1). The SDR computing system modeling captures 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), particularly, model the processing and interprocessor data flow capacities and requirements.
+The computing resource management framework features the computing system modeling and computing resource management modules (Figure 1). Figure 2 illustrates ALOE's time management principle. The pipelined execution pattern facilitates the synchronized execution of the waveform modules on distributed computing resources. The SDR computing system modeling then captures the computing resources and the computing requirements on time slot basis. Million operations per time slot (MOPTS) and mega-bits per time slot (MBPTS) are used for modeling the processing and interprocessor data flow capacities and requirements.
 
-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.
+The framework offers general-purpose mapping algorithms and a customizable cost function. The cost function implements the computing resource management objective and guides the mapping process under the given computing resource constraints. The mapping algorithms and cost function are presented in [attachment:FlexCRM_July11.pdf FlexCRM_July11]. The computing resource management framework--mapping API, algorithms and cost function, and simulation test suite--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, following the mapping process, and analyzing the results. [wiki:ALOEedu ALOE Sessions 7 and 8] introduce the framework and its tools.
 
-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].
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-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.
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-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.
+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 mapping algorithms with the objective of applying them to large-scale computing systems.