Architecture of Computing Systems (Lecture Notes in Computer ...

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46 P. Bellasi, W. Fornaciari, and D. Siorpaes Table 1. Devices’ operating modes and platform constraints. Each device defines its operating modes (a) by mapping them on SMWs’ ranges: DSP CLK (lower bound) and BANDWIDTH (upper bound). The platform code defines some constraints (b) to track dependencies between CPU CLK and DSP CLK. (a) Operating Modes SWM Audio DPS PCM MP3 WMA OGG DSP CLK 0 50 70 100 SWM Video DPS OFF MPEG4 H.263 H.264 DSP CLK 0 40 60 100 SWM 3G Modem GPRS EDGE1 UMTS EDGE2 HSDPA BANDWIDTH 57.6 236.8 384 473.6 7200 (b) Constraints Platform CPU CLK DSP CLK 19.2 19.2 100.8 100.8 201.6 100.8 264.0 132.0 302.4 75.6 393.6 98.4 451.2 112.8 The assertion of these constraints invalidates the current FSCs. Thus, CPM coordinates the selection of a new candidate FSC and the corresponding DWRs are communicated to the modem and DSPs for a distributed agreement. The required codecs are bound to a specific DSP CLK frequency: 50MHz for DSP A and 60MHz for DSP V. The platform constraints (Fig. 1b) allow to manage the dependency with the CPU CLK which is setup to support the desired frequency. Therefore the CPU frequency optimization policy will be able to scale the processor frequency according to the imposed constraint (not less than 100.8MHz). After the agreement, the candidate FSC is activated and all involved subsystems will move to the new working mode, e.g., the modem switches from GPRS to EDGE1. The use-case continues and during the playback, it starts an application that downloads data from the web, e.g., an email update application. This new download application asserts a requirement on the bandwidth for an amount of 200 kb/s. Since the ASM bandwidth is of type additive, the aggregation function takes into account all the previously asserted requirements and aggregates with a sum. Thus, 464 kbit/s becomes the new active constraint on the bandwidth and thus a new FSC is selected, which brings the modem device to move to the EDGE2 working mode in order to satisfy the requirements. Finally, the video stream playback ends and the corresponding requirements on bandwidth and codecs are de-asserted. This leads to a new aggregation on the bandwidth, which results in a subtraction of the value on the ASM bandwidth. At this point only the download application is still active and a new FSC is selected and activated. 4.3 Discussion on Use Case Results System resources management. The declaration and aggregation of requirements allows to keep a correct and precise view of used and still available system
Hierarchical Distributed Control of Power and Performances 47 resources. This could be exploited to configure the hardware devices on the best feasible operating mode that supports the resources demand. A positive effect of this method is the energy saving that could be achieved by selecting, for each QoS demand, the optimal working mode not only with respect to a multi-objective performances optimization policy but also considering the system-wide power consumption, which can be associated to every FSC. Dependency tracking. CPM allows to track hardware dependencies among different subsystems of a SoC that may prevent a correct operation of a system. Instead of patching each device driver to adapt to the platform, developers declare platform DWRs to solve dependencies issues. In that way code portability is improved. Identification of Feasible System-wide Configurations. The automatic computation of the FSCs allows to identify all the feasible working points of an entire platform. This is done by exploiting the information defined, independently, in each device driver code. Other approaches to PM require to code all the working points by hand. Considering that in the presented use case the total number of FSCs was 415, we understand how interesting is the ability to automatically compute these point. Thus, this is a relevant result by itself. Moreover, it improves the portability of the solution across different platforms because allows to reuse drivers defined informations. Additive aggregation. This is a new concept, introduced in CPM, to solve a limitation present in the implementation of QoSPM where even for resources that are intrinsically additive (e.g., bandwidth) the aggregation function is of type min/max: not allowing to keep a correct view of system resources and bringing to select devices’ WMs that actually can’t support the QoS level required, e.g., if two applications require 300 kb/s each, QoSPM aggregates with the max thus with a final value of 300 Kb/s, i.e. an incorrect view of the resource’s requirement. 5 Conclusions We have presented CPM, Linux kernel framework for system-wide power and resources’ optimization. The proposed method efficiently implements a well-known formal technique to solve optimization problems. The cross-layer design of the framework allows to collect and aggregate QoS requirements from the application layer and to coordinate the reconfiguration of device drivers working mode. A system-wide optimization, of both perceived performances and energy consumption, is supported by the definition of a global dynamic and multi-objective policy. As revealed both theoretically and experimentally, the CPM approach allows to capture energy savings while fulfilling QoS constraints, thanks to a systemwide cross-layer dynamic optimization. Work is in progress, within a FP7 EUfunded project, to extend the approach towards multi-core architectures.
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Lecture Notes in Computer Science 5
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Volume Editors Christian Müller-Sc
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General Chair Organization Christia
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Exploiting Inactive Rename Slots fo
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130 M. Kayaalp et al. INSTRUCTION 1
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Efficient Transaction Nesting in Ha
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Decentralized Energy-Management to
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Abdullah, Tariq 174 Alima, Luc Onan
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