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Adaptive Policy Triggering for Load Balancing

  • Conference paper
Distributed and Parallel Computing (ICA3PP 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3719))

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Abstract

Load balancing is an attractive problem in storage system. With the fast growth of high-speed network technology and novel storage architecture, smarter storage device becomes an effective way to solve the problem. In this paper, we present an effective object migration & replication policy in our object storage system (OSS), denoted adaptive policy triggering. This policy migrates/replicates object from congested object storage controllers to relatively uncongested object storage controllers according to the information of three facets in the OSS, including metadata server, object storage controller and storage object itself. First, clients interact with the metadata server (MS). So those global policies are triggered by the MS. Second, the OSC has better knowledge of its own load than MS. It is reasonable that local policy is triggered by the OSC. Third, the storage object is encapsulated with data, attributes and methods. These attributes can be set or got when objects are accessed. And object attribute values are good as policy threshold. Furthermore, object methods also can be triggered according to some policies.

This work was supported by the National Basic Research Program of China (973 Program) under Grant No. 2004CB318201, Huo Yingdong Education Foundation under Grant No.91068, the National Science Foundation of China under Grant No.60273074 and No.60303032.

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© 2005 Springer-Verlag Berlin Heidelberg

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Feng, D., Zeng, L. (2005). Adaptive Policy Triggering for Load Balancing. In: Hobbs, M., Goscinski, A.M., Zhou, W. (eds) Distributed and Parallel Computing. ICA3PP 2005. Lecture Notes in Computer Science, vol 3719. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564621_27

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  • DOI: https://doi.org/10.1007/11564621_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29235-7

  • Online ISBN: 978-3-540-32071-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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