A dilated-CPU-consumption-based performance prediction for multi-core software routers

Suksomboon, Kalika; Fukushima, M.; Okamoto, Satoru; Hayashi, Michiaki

doi:10.1109/netsoft.2016.7502413

Cited by 9 publications

(10 citation statements)

References 7 publications

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“…In this paper, we extend our previous work to cover more practical cases and demonstrate the applicability of our proposed prediction in various dimensions. That is, we extend the testbed experiments from assigning 1 to 4 CPU cores at 2.6 GHz to 3.1‐GHz CPU speeds in the previous work to assigning 1 to 8 CPU cores at 1.2‐ to 2.6‐GHz CPU speeds in this work so as to observe more relationship between the resources assigned to the packet‐forwarding function and its performance. Moreover, we also explore the effect of different traffic load conditions by sending a large number of traffic flows (ie, 16 384 flows) and a small number of traffic flows (ie, 50 flows).…”

Section: Introductionmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

“…The contributions of this paper are summarized as follows; We conduct the testbed experiments by extending our previous work to obtain the comprehensive observations. It is worth nothing that these observations cover more results than that of the previous work . That is, (1) the performance bound by Ethernet maximum bandwidth, (2) a nonlinear relationship between the number of CPU cores assigned to the packet‐forwarding function and its performance, and (3) a linear relationship between the CPU speed and the performance of packet‐forwarding function. We elaborate the cache contention analysis, which has not yet been indicated in our previous work, by providing an important evidence showing a linear relationship between the number of CPU cores assigned to the packet‐forwarding function and the number of cache misses at the LLC. We extend our experiments from our previous work in various dimensions.…”

Section: Introductionmentioning

confidence: 99%

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Towards performance prediction of multicore software routers

et al. 2017

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Rapid advances in network function virtualization technologies have led to the emergence of flexible and scalable embedded data plane processing functions, eg, packet forwarding, on commodity hardware. However, performance prediction of softwarized network functions on such shared resources is challenging but very important for obtaining the full benefits of network function virtualization. This paper addresses the problem of performance prediction for multicore software routers and reveals a key technique to achieve high accuracy. Motivated by observations, we first analyze how many CPU cycles are spent for forwarding a packet in multicore processing systems. Our prediction model of the CPU usage based on cache contention can capture its nonlinear dilation scaled by the number of CPU cores-called dilated CPU consumption (DCC). We validate the accuracy of the DCC model with measured data. On the basis of the DCC model, we develop 2 performance prediction algorithms to predict the maximum throughput of the packet-forwarding function corresponding to the assigned resources. The first algorithm includes CPU utilization statistics (called DCC-u), while its simplified version (called sDCC) does not require CPU utilization statistics. We validate the proposed models through the exhaustive set of experiments under various amounts of assigned resources (ie, CPU speeds and the number of CPU cores) and different traffic loads (ie, a large and small number of traffic flows). The results show that DCC-u and sDCC achieve high accuracy under various amounts of resources and traffic conditions. Remarkably, they improve the precision of estimation from that of the existing techniques by up to 105% to 163%. KEYWORDS functional areas, multicore software router, network function virtualization, performance management, performance prediction 1 Int J Network Mgmt. 2017;27:e1966.wileyonlinelibrary.com/journal/nem

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Section: Introductionmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards performance prediction of multicore software routers

et al. 2017

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“…Related to our work is either experimental work [2]- [10] of NFV systems or modeling work [11]- [15], [21]- [26] that either shares a similar technique or NFV focus.…”

Section: Related Workmentioning

confidence: 99%

“…Models of Network Functions Virtualization (NFV) have also recently appeared [11], [24]- [26]. In particular, queueing models are used in [24] and [25] to describe software-based networks.…”

Section: Related Workmentioning

confidence: 99%

Discrete-Time Modeling of NFV Accelerators that Exploit Batched Processing

Geissler

Lange

Linguaglossa

et al. 2021

ACM Trans. Model. Perform. Eval. Comput. Syst.

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Network Functions Virtualization (NFV) is among the latest network revolutions, promising increased flexibility and avoiding network ossification. At the same time, all-software NFV implementations on commodity hardware raise performance issues when comparing to ASIC solutions. To address these issues, numerous software acceleration frameworks for packet processing have been proposed in the last few years. One central mechanism of many of these frameworks is the use of batching techniques , where packets are processed in groups as opposed to individually. This is required to provide high-speed capabilities by minimizing framework overhead, reducing interrupt pressure, and leveraging instruction-level cache hits. Several such system implementations have been proposed and experimentally benchmarked in the past. However, the scientific community has so far only to a limited extent attempted to model the system dynamics of modern NFV routers exploiting batching acceleration. In this article, we propose a simple, generic model for this type of batching-based systems that can be applied to predict all relevant key performance indicators. In particular, we extend our previous work and formulate the calculation of the queue size as well as waiting time distributions in addition to the batch size distribution and the packet loss probability. Furthermore, we introduce the waiting time distribution as a relevant QoS parameter and perform an in-depth parameter study, widening the set of investigated variables as well as the range of values. Finally, we contrast the model prediction with experimental results gathered in a high-speed testbed including an NFV router, showing that the model not only correctly captures system performance under simple conditions, but also in more realistic scenarios in which traffic is processed by a mixture of functions.

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