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2-Step Power Scheduling with Adaptive Control Interval for Network Intrusion Detection Systems on Multicores

机译:多核网络入侵检测系统的自适应控制间隔两步功率调度

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Network intrusion detection system (NIDS) is becoming an important element even in embedded systems as well as in data centers since embedded computers have been increasingly exposed to the Internet. The demand for power budget of these embedded systems is a critical issue in addition to that for performance. In this paper, we propose a technique to minimize power consumption in the NIDS called by 2-step power scheduling with the adaptive control interval. In addition, we also propose a CPU-core controlling algorithm so that our scheduling technique can preserve the performance for other applications and NIDS assuming the cases of multiplexing NIDS and them simultaneously on the same device such as a home server or a mobile platform. We implement our 2-step algorithm into Suricata, which is a popular NIDS, as well as a 1-step algorithm and a simple fixed interval algorithm for evaluations. Experimental results show that our 2-step scheduling with both the adaptive and the fixed 30-millisecond interval achieve 75% power saving comparing with the Ondemand governor and 87% comparing with the Performance governor in Linux, respectively, without affecting their performance capability on four ARM Cortex-A15 cores at the network traffic of 1,000 packets/seconds. In contrast, when the network traffic reaches to 17,000 packets/seconds, our 2-step scheduling and the Ondemand as well as the Performance governor can maintain the packet processing capacity while the fixed 30-milliseconds interval processes only 50% packets with two and three cores, and about 80% packets on four cores.
机译:网络入侵检测系统(NIDS)甚至在嵌入式系统以及数据中心中也变得越来越重要,因为嵌入式计算机已经越来越多地暴露于Internet。除了性能之外,这些嵌入式系统对功率预算的需求也是一个关键问题。在本文中,我们提出了一种通过自适应控制间隔将两步功率调度称为NIDS的最小化功耗的技术。此外,我们还提出了一种CPU核心控制算法,以使我们的调度技术能够在假定将NIDS及其同时在同一设备(例如家庭服务器或移动平台)上多路复用的情况下,保留其他应用程序和NIDS的性能。我们将受欢迎的NIDS Suricata实施两步算法,以及一步算法和简单的固定间隔算法进行评估。实验结果表明,在Linux上,自适应和固定30毫秒间隔的两步调度分别与Ondemand调控器相比节省了75%的功耗,与Performance调控器相比节省了87%的功耗,而不会影响它们在四个方面的性能。 ARM Cortex-A15的核心网络流量为1,000包/秒。相反,当网络流量达到17,000个数据包/秒时,我们的两步调度和按需调度以及性能调节器可以保持数据包处理能力,而固定的30毫秒间隔仅处理50%的具有两个和三个数据包核心,四个核心上约80%的封包。

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