Optimizing Throughput and Latency under Given Power Budget for Network Packet Processing

Current state-of-the-art task scheduling algorithms for network packet processing schedule the program into a parallel-pipeline topology on network processors to maximize the throughput. However, there has been no existing work targeting power budget for packet processing on off-the-shelf multicore architectures. As energy consumption, reliability and cooling cost for packet processing systems become increasingly important, it is necessary to integrate power-awareness into a scheduler to meet the power budget. In this paper, we propose a novel scheduling algorithm to optimize both throughput and latency given a power budget for network packet processing on multicore architectures. This algorithm addresses power-aware parallel-pipeline scheduling problem by applying per-core DVFS to optimally adjust frequency on each core. We implement our algorithm on an AMD machine with two Quad-Core Opteron 2350 processors and compare the results with existing algorithms given the same power budget. For six real packet processing applications, our algorithm improves throughput and reduces latency by an average of 64.6% and 25.2%, respectively.