E-AHRW: An Energy-Efficient Adaptive Hash Scheduler for Stream Processing on Multi-core Servers

We study a streaming network application -- video transcoding to be executed on a multi-core server. It is important for the scheduler to minimize the total processing time and preserve good video quality in an energy-efficient manner. However, the performance of existing scheduling schemes is largely limited by ineffective use of the multi-core architecture characteristic and undifferentiated transcoding cost in terms of energy consumption. In this paper, we identify three key factors that collectively play important roles in affecting transcoding performance: memory access (M), core/cache topology (C) and transcoding format cost (C), or MC^2 for short. Based on MC^2, we propose E-AHRW, an Energy-efficient Adaptive Highest Random Weight hash scheduler by extending the HRW scheduler proposed for packet scheduling on a homogeneous multiprocessor. E-AHRW achieves stream locality and load balancing at both stream and packet (frame) level by adaptively adjusting the hashing decision according to real-time weighted queue length of each processing unit (PU). Based on E-AHRW, we also design, implement and evaluate a hash-tree scheduler to further reduce the computation cost and achieve more effective load balancing on multi-core architectures. Through implementation on an Intel Xeon server and evaluations on realistic workload, we demonstrate that E-AHRW improves throughput, energy efficiency and video quality due to better load balancing, lower L2 cache miss rate and negligible scheduling overhead.