A case for run-time adaptation in packet processing systems

Kokku, Ravi; Riché, Taylor L.; Kunze, Aaron; Mudigonda, Jayaram; Jason, Jamie; Vin, Harrick M.

doi:10.1145/972374.972393

Cited by 42 publications

(33 citation statements)

References 19 publications

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“…Kokku et al [32] propose an algorithm that assigns network processing tasks to processor cores with the goal of reducing the power consumption. Wolf et al [62] propose run-time support that considers the partitioning of applications across processor cores.…”

Section: Related Workmentioning

confidence: 99%

Thread Assignment in Multicore/Multithreaded Processors: A Statistical Approach

Radojković

Carpenter

Moretó

et al. 2016

IEEE Trans. Comput.

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Abstract-The introduction of multicore/multithreaded processors, comprised of a large number of hardware contexts (virtual CPUs) that share resources at multiple levels, has made process scheduling, in particular assignment of running threads to available hardware contexts, an important aspect of system performance. Nevertheless, thread assignment of applications running on state-of-the art processors is an NP-complete problem. Over the years, numerous studies have proposed heuristic-based algorithms for thread assignment. Since the thread assignment problem is intractable, it is in general impossible to know the performance of the optimal assignment, so the room for improvement of a given algorithm is also unknown. It is therefore hard to decide whether to invest more effort and time to improve an algorithm that may already be close to optimal. In this paper, we present a statistical approach to the thread assignment problem. First, we present a method that predicts the performance of the optimal thread assignment, based on the observed performance of each thread assignment in a random sample. The method is based on Extreme Value Theory (EVT), a branch of statistics that analyses extreme deviations from the population mean. We also propose sample pruning, a method that significantly reduces the time required to apply the statistical method by reducing the number of candidate solutions that need to be measured. Finally, we show that, if no suitable heuristicbased algorithm is available, a sample of several thousand random thread assignments is enough to obtain, with high confidence, an assignment with performance close to optimal. The presented approach is architecture and application independent, and it can be used to address the thread assignment problem in various domains. It is especially well suited for systems in which the workload seldom changes. An example is network systems, which typically provide a constant set of services that are known in advance, with network applications performing a similar processing algorithm for each packet in the system. In this paper, we validate our methods with an industrial case study for a set of multithreaded network applications on an UltraSPARC T2 processor. This article is an extension of our previous work [44], which was published in

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Section: Related Workmentioning

confidence: 99%

Thread Assignment in Multicore/Multithreaded Processors: A Statistical Approach

Radojković

Carpenter

Moretó

et al. 2016

IEEE Trans. Comput.

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“…This scheme allocates frequencies to the pipeline stages statically and is not aimed at exploiting dynamic traffic variations. Kokku et al [37] show that variability in traffic can be used for run time scheduling of tasks for conserving energy. Our proposed scheme is not related to scheduling and can complement the power efficient scheduling schemes.…”

Section: Related Workmentioning

confidence: 99%

Efficient traffic aware power management in multicore communications processors

Iqbal

John

2012

Proceedings of the Eighth ACM/IEEE Symposium on Architectures for Networking and Communications Systems

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Multicore communications processors have become the main computing element in Internet routers and mobile base stations due to their flexibility and high processing capability. These processors are designed and equipped with enough resources to handle peak traffic loads. But network traffic varies significantly over time and peak traffic is observed very rarely. This variation in amount of traffic gives us an opportunity to save power during the low traffic times. Existing power management schemes are either too conservative or are unaware of traffic demands. We present a predictive power management scheme for communications or network processors. We use a traffic and load predictor to pro-actively change the number of active cores. Predictive power management provides more power efficiency than reactive schemes because it reduces the lag between load changes and changes in power adaptations since adaptations can be applied before the load changes. The proposed scheme also uses Dynamic Voltage and Frequency Scaling (DVFS) to change the frequency of the active cores to adapt to variation in traffic during the prediction interval. We perform experiments on real network traces and show that the proposed traffic aware scheme can save up to 40% more power in communications processors as compared to traditional power management schemes.

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“…For example, Kokku et al present an analytical model for allocating the appropriate number of processors to each service on NPs [10]. However, their work made several assumptions to derive the estimate for the benefits in an ideal adaptation scheme.…”

Section: Related Workmentioning

confidence: 99%