Quantum-Adaptive Scheduling for multi-core network processors

Efficiency and effectiveness are always the emphases of a scheduler, for both link and processor scheduling. Well-known scheduling algorithms such as Surplus Round Robin (SRR) and Elastic Round Robin (ERR) suffer from two fold shortcomings: 1) additional pre-processing queuing delay and post-processing resequencing delay are incurred due to the lack of short-term load-balancing; 2) bursty scheduling is caused due to blind preservation of scheduling history under non-backlogged traffic. In this paper, we propose a Quantum-Adaptive Scheduling (QAS) algorithm, which: 1) synchronizes all the quanta in a fine-grained manner and, 2) adjusts the quanta intelligently based on processor utilization. We theoretically prove that the Queuing Fairness Bound (QFB) for QAS is one third tighter than SRR and ERR. This result approaches the optimal value as obtained in Shortest Queue First (SQF) algorithm, while still maintaining 0(1) complexity. Trace-driven simulations show that QAS reduces average packet delay by 18%∼24% while cutting down the resequencing buffer size by more than 40% compared to SRR and ERR.