Abstract
IP-networked streaming media storage has been increasingly used as a part of many applications. Random placement of data blocks has been proven to be an effective approach to balance heterogeneous workload in multi-disk steaming architectures. However, the main disadvantage of this technique is that statistical variation can still result in short term load imbalances in disk utilization. We propose a packet level randomization (PLR) technique to solve this challenge. We quantify the exact performance trade-off between PLR approach and the traditional block level randomization (BLR) technique through both theoretical analysis and extensive simulation. Our results show that the PLR technique can achieve much better load balancing in scalable streaming architectures by using more memory space.
This research has been funded in part by NSF grants EEC-9529152 (IMSC ERC), IIS-0082826 and CMS-0219463, and unrestricted cash/equipment gifts from Intel, Hewlett-Packard, Raptor Networks Technology and the Lord Foundation.
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Fu, K., Zimmermann, R. (2005). Randomized Data Allocation in Scalable Streaming Architectures. In: Zhou, L., Ooi, B.C., Meng, X. (eds) Database Systems for Advanced Applications. DASFAA 2005. Lecture Notes in Computer Science, vol 3453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11408079_42
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DOI: https://doi.org/10.1007/11408079_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25334-1
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