Experience Transfer for the Con guration Tuning in Large Scale Computing Systems Haifeng Chen(1) Wenxuan Zhang(2) Guofei Jiang(1) (2) NEC Laboratories America, Inc. CS Department, Rutgers University {haifeng, gfj}@nec-labs.com wzhang@cs.rutgers.edu (1) Categories and Subject Descriptors K.6.4 [Management of Computing and Information Systems]: System Management; G.1.6 [Numerical Analysis]: Optimization General Terms Algorithms, Management, Performance Keywords Distributed systems, con guration tuning, knowledge acquisition, knowledge reuse 1. MOTIVATION Recent years have witnessed the emergence of autonomic management tools in large scale information systems. They utilize the knowledge learned from system experts or historical data to automate the process of management tasks. However, current autonomic solutions only focus on the knowledge discovery and modeling to bene t the management in the same system. It is sometimes also important to utilize the knowledge of one system to facilitate the management of other systems. For example, a lot of special kinds of systems, such as the online banking systems, usually run on similar platforms, i.e., the J2EE based infrastructure, to support applications with similar business logics. If we can learn the behavior of one system and transfer the learned knowledge to other similar systems, it is not necessary to spend the same amount of
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Experience transfer for the configuration tuning in large scale computing systems
Chen, Haifeng; Zhang, Wenxuan; Jiang, Guofei
Follow ACM SIGMETRICS Performance Evaluation Review
, Volume 37 (2)
Association for Computing Machinery – Oct 16, 2009
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