Efficient traffic aware power management in multicore communications processors

Abstract

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.

Efficient Prediction of Network Traffic for Real-Time Applications

“…For both AR and ARMA predictors, the order of AR and MA processes was varied from 0 to 20 and the best performing order is chosen. In most cases, AR (8) and ARMA(9, 8) gave the best answers and the results with these orders are presented. For ANN-based predictor, we used one input, one output, and one hidden layer for neurons.…”

“…If the network traffic can be predicted accurately, additional processors in these core routers can be turned off during low traffic times to save power. A precisely predicted future traffic load will help us design greener traffic-aware networks [5][6][7][8].…”

LEoNIDS: A Low-Latency and Energy-Efficient Network-Level Intrusion Detection System

“…Closer to our work, Niccolini et al [24] present an approach for a power-proportional software router using DVFS and C-states. Iqbal and John [25] propose a predictive power management scheme to pro-actively change the frequency and number of active cores in network processors. Nedevschi et al [23] also use sleep states and rate adaption to save energy.…”

Having your cake and eating it too: Energy savings without performance loss through resource sharing driven power management

“…Typical DVFS policies include rulebased [7], PID control [8] and machine learning [9]. Iqbal and John introduce a traffic aware power management technique for communication processors [10]. Similarly, Takagi, et al introduce a cooperative shared resource access control technique [11].…”