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

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56 J. Zeppenfeld and A. Herkersdorf 3 Simulation Results Given the network processor MP-SoC presented above, this section presents simulation results that compare a classical system implementation with one enhanced by an autonomic layer. In order to show the adaptive nature of the autonomic system, various types of packet bursts, each consisting of 1000 packets, are sent in alternating fashion to the input MAC for processing. The packet inter-arrival rate is fixed at 4 μs, resulting in a burst duration of 4 ms independent of the packet processing duration. For each of the incoming burst scenarios, the system must either adapt to meet the processing requirements, or maintain a sufficient reserve of processing power to be able to cope with even the largest and most processing intensive packets. All systems used below are based on a common base system configuration. The hardware components and their functionality, as well as the software tasks used for packet processing were described in sections 2.1 and 2.2, respectively. Unless stated otherwise, the initial task distribution is such that one CPU is responsible for ingress path processing (Tasks 1 and 2), one CPU is responsible for payload processing (Task 3), and the third CPU is responsible for egress path processing (Tasks 4 and 5). The initial CPU frequencies are chosen such that the system would be able to handle all burst scenarios without requiring parameter adaptation. Those systems containing autonomic enhancements use the monitors, actuators and LCT evaluator presented in section 2.3. 3.1 Comparison of Autonomic and Static Systems The simulation results shown in Figure 5 compare the objective value and frequency adjustment of four different system configurations. The first two systems are static without any autonomic enhancements, where the first corresponds to the base system configuration described above. In the second static system, both the task distribution and CPU frequency were hand optimized with prior knowledge of the incoming packet traffic. The results of this optimized system demonstrate how an ideally parameterized static system compares to a system with autonomic enhancements. The two autonomic systems are both based on the common system configuration, but are differentiated by the monitor signals available to them. While the second system uses the AE interconnect to share workload information globally among the CPU AEs, the first autonomic system must base its optimization decisions solely on local monitor information. Both systems remain capable of migrating tasks, however. In comparison to a static system, the trend of the objective value clearly shows the benefits of an autonomic system regarding fulfillment of the objective function chosen in section 2.3.3. The autonomic system with global information is able to maintain system operation within approximately 10% of the optimum for all incoming traffic scenarios. Although the hand optimized static system is able to top this for the fifth and most processing intensive traffic scenario, it is not able to maintain such a high level of optimization across all bursts. This is a direct consequence of the fact that a system optimized for one scenario is not necessarily optimized for other scenarios. Whereas the designer must choose a certain scenario for which the static system
Objective Value Avg. Frequency (MHz) 35% 30% 25% 20% 15% 10% 5% 0% 160 140 120 100 80 60 40 20 0 0 s 0 s Autonomic Workload Management for Multi-core Processor Systems 57 4 ms 4 ms 8 ms 8 ms 12 ms 12 ms 16 ms 16 ms 20 ms 20 ms 24 ms 24 ms 28 ms 28 ms Static Static Opt. Auto Local Auto Global Lower is better Static Static Opt. Auto Local Auto Global Fig. 5. Objective value and average CPU frequency of static and autonomic systems across various traffic scenarios. Lower values are better. is optimized during design time, the autonomic system can optimize itself during run time to whatever scenario it is confronted with. Not only does this provide decent optimization for a much larger set of foreseeable and unforeseeable scenarios, it also relieves the designer of having to optimize all aspects of the system at design time. Examining the trend of the average CPU frequency confirms the problem that a static system must meet the worst case processing requirements in order to remain functional over all workload scenarios, thereby wasting processing power during periods of lower activity. Except for processing of the most workload intensive fifth burst, the autonomic system is able to achieve an equal or lower average CPU frequency than the hand optimized system. The benefits of global information also become visible here, as the locally optimized system is not able to share the workload across the CPUs as effectively, requiring an increased average CPU frequency to meet the processing requirements.
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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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Organization IX Hartmut Schmeck Kar
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Keynote Table of Contents HyVM - Hy
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Table of Contents XIII JetBench:AnO
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How to Enhance a Superscalar Proces
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4 J. Mische et al. The Real-time Vi
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Abdullah, Tariq 174 Alima, Luc Onan
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