AMTrac: adaptive meta-caching for transcoding

  • Authors:

  • Dongyu Liu;Songqing Chen;Bo Shen

  • Affiliations:

  • George Mason University, Fairfax, VA;George Mason University, Fairfax, VA;Hewlett-Packard Laboratory, Palo Alto, CA

  • Venue:
  • Proceedings of the 2006 international workshop on Network and operating systems support for digital audio and video
  • Year:
  • 2006

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Abstract

The increase of aggregate Internet bandwidth and the rapid development of 3G wireless networks demand efficient delivery of multimedia objects to all types of wireless devices. To handle requests from wireless devices at runtime, the transcode-enabled caching proxy has been proposed and a lot of research has been conducted to study online transcoding. Since transcoding is a CPU-intensive task, the transcoded versions can be saved to reduce the CPU load for future requests. However, extensively caching all transcoded results can quickly exhaust cache space. Constrained by available CPU and storage, existing transcode-enabled caching schemes always selectively cache certain transcoded versions, expecting that many future requests can be served from the cache while leaving CPU cycles for online transcoding for other requests. But such schemes treat the transcoder as a black box, leaving little room for flexible control of joint resource management between CPU and storage. In this paper, we first introduce the idea of meta-caching by looking into a transcoding procedure. Instead of caching certain selected transcoded versions in full, meta-caching identifies intermediate transcoding steps from which certain intermediate results (called metadata) can be cached so that a fully transcoded version can be easily produced from the metadata with a small amount of CPU cycles. Achieving big saving in caching space with possibly small sacrifice on CPU load, the proposed meta-caching scheme provides a unique method to balance the utilization of CPU and storage resources at the proxy. We further construct a model to analyze the meta-caching scheme. Based on modeling results, we propose AMTrac, Adaptive Meta-caching for Transcoding, which adaptively applies meta-caching based on the client request pattern and available resources. Experimental results show that our proposed AMTrac can significantly improve the system throughput over existing approaches.