A Measurement Based Memory Performance Evaluation of Streaming Media Servers Garba Isa Yau and Abdul Waheed Department of Computer Engineering King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia 10th Annual IEEE Technical Exchange Meeting Presented at the March 23-24, 2003

Outline Introduction Motivation Experiments Results and Discussion Conclusions and Future Research Operating system Impact on performance

Introduction Basic architecture Unlike ordinary file downloads or Web applications, streaming media have:  stringent timing requirement  high bandwidth requirement  CPU intensive  high memory requirement

Motivation CPU – Memory speed gap  CPU speed doubles in about 18 months (Moore’s Law)  Memory access time improves by only one-third in 10 years Hierarchical memory architecture introduced to alleviate CPU–memory speed gap  It works on locality of reference of data temporal locality spatial locality Streaming media content is a continuous data  working set is normally large, cannot fit into cache  it has very poor temporal locality (data reuse is poor) Hierarchical memory architecture becomes ineffective

Experiments Testbed Metrics:  cache misses (L1 & L2)  page fault rate  throughput  server CPU utilization Factors:  number of streams  media encoding rate (56kbps and 300kbps)  stream distribution (unique or multiple)

Experiments cont. Servers:  Apple Darwin streaming server  Microsoft Windows media server Clients:  DSS- Streaming Load Simulator  WMS - Media load simulator Tools:  Intel Vtune performance analyzer  Windows performance monitor  netstat, vmstat, sar etc.

Results and Discussion L1 C ache Performance L1 cache misses (56kpbs)L1 cache misses (300kbps) L1 cache misses are mostly influenced by number of streams Worst-case performance when the number of streams is high, 300kbps encoding rate and multiple media contents are requested by clients

L2 Cache Performance Results and Discussion cont. L2 cache misses (300kbps) Comparison For both L1 and L2 caches, windows media server has better cache performance compared to Darwin streaming server

Memory Performance Results and Discussion cont. Page fault rate (300kbps) Requests for unique media object does not incur much page faults since object can easily be served from memory Requests for multiple objects leads to high page fault rate since a lot of data blocks will have to be fetched from the disk High page fault rate leads to client’s timeout due to long delay

Results and Discussion cont. Throughput and CPU utilization Throughput (300kbps)CPU utilization (300kbps) Windows media server has higher throughput compared to Darwin streaming server For unique streams, CPU utilization scales with number of streams throughout, while is not the case with multiple streams

Memory Transfer Test ECT (extended copy transfer) Characterizing the memory performance to observe what might be the impact of OS on memory performance Locality of reference:  temporal locality – varying working set size (block size)  spatial locality – varying access pattern (strides)

Conclusion Future research media object pre-fetching and stream batching are techniques we are exploring to improve memory performance of the servers Both media servers exhibit similar cache/memory behavior Worst cache/memory performance at 300kbps encoding rate and multiple stream distribution High cache misses and page faults lead to performance degradation as a result of significant wastage in CPU cycles For streaming media servers, apart from I/O bottleneck, memory subsystem is a potential bottleneck on performance.