Paper Info
Title
E-AHRW: An Energy-Efficient Adaptive Hash Scheduler for Stream Processing on Multi-core Servers
Abstract
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.
Year
DOI
Venue
2011
10.1109/ANCS.2011.15
ANCS
Keywords
Field
DocType
hrw scheduler,weight hash scheduler,hash-tree scheduler,undifferentiated transcoding cost,stream processing,good video quality,computation cost,effective load balancing,transcoding format cost,energy-efficient adaptive hash scheduler,multi-core servers,better load balancing,transcoding performance,real time,video transcoding,energy efficient,energy efficiency,file servers,resource allocation,load balance,energy conservation,transcoding,video quality,tree data structures
Transcoding,Scheduling (computing),Cache,Computer science,CPU cache,Load balancing (computing),Computer network,Multiprocessing,Real-time computing,Hash function,Video quality
Conference
Citations 
PageRank 
References 
2
0.37
26
Authors
4
Name
Order
Citations
PageRank
Jilong Kuang13817.00
Laxmi N. Bhuyan22393248.44
Haiyong Xie3112169.10
Danhua Guo41016.66