Discovering likely invariants of distributed transaction systems for autonomic system management

Large amount of monitoring data can be collected from distributed systems as the observables to analyze system behaviors. However, without reasonable models to characterize systems, we can hardly interpret such monitoring data effectively for system management. In this paper, a new concept named flow intensity is introduced to measure the intensity with which internal monitoring data reacts to the volume of user requests in distributed transaction systems. We propose a novel approach to automatically model and search relationships between the flow intensities measured at various points across the system. If the modeled relationships hold all the time, they are regarded as invariants of the underlying system. Experimental results from a real system demonstrate that such invariants widely exist in distributed transaction systems. Further we discuss how such invariants can be used to characterize complex systems and support autonomic system management. Guofei Jiang received the B.S. and Ph.D. degrees in electrical and computer engineering from Beijing Institute of Technology, China, in 1993 and 1998, respectively. During 1998–2000, he was a postdoctoral fellow in computer engineering at Dartmouth College, NH. He is currently a research staff member with the Robust and Secure Systems Group in NEC Laboratories America at Princeton, NJ. During 2000–2004, he was a research scientist in the Institute for Security Technology Studies at Dartmouth College. His current research focus is on distributed system, dependable and secure computing, system and information theory. He has published over 50 technical papers in these areas. He is an associate editor of IEEE Security and Privacy magazine and has served in the program committees of many conferences. Haifeng Chen received the BEng and MEng degrees, both in automation, from Southeast University, China, in 1994 and 1997 respectively, and the PhD degree in computer engineering from Rutgers University, New Jersey, in 2004. He has worked as a researcher in the Chinese national research institute of power automation. He is currently a research staff member at NEC laboratory America, Princeton, NJ. His research interests include data mining, autonomic computing, pattern recognition and robust statistics. Kenji Yoshihira received the B.E. in EE at University of Tokyo in 1996 and designed processor chips for enterprise computer at Hitachi Ltd. for five years. He employed himself in CTO at Investoria Inc. in Japan to develop an Internet service system for financial information distribution through 2002 and received the M.S. in CS at New York University in 2004. He is currently a research staff member with the Robust and Secure Systems Group in NEC Laboratories America, inc. in NJ. His current research focus is on distributed system and autonomic computing.