TY - GEN
T1 - MONITORMINING
T2 - 2005 9th IFIP/IEEE International Symposium on Integrated Network Management, IM 2005
AU - Uttamchandan, Sandeep
AU - Palmer, John
AU - Yin, Xiaoxin
AU - Agha, Gul
PY - 2005
Y1 - 2005
N2 - The effectiveness of automated system management is dependent on the domain-specific information that is encoded within the management framework. Existing approaches for defining the domain knowledge are categorized into white-box and black-box approaches, each of which has limitations. White-box approaches define detailed formulas for system behavior, and are limited by excessive complexity and brittleness of the information. On the other hand, black-box techniques gather domain knowledge by monitoring the system; they are error-prone and require an infeasible number of iterations to converge in real-world systems. MONITORMlNlNG is a gray-box approach for creating domain knowledge in automated system management; it combines simple designer-defined specifications with the information gathered using machine learning. The designer specifications enumerate input parameters for the system behavior functions, while regression techniques (such as Neural Networks, Support Vector Machines) are used to derive the mathematical function that relates these parameters. These functions are constantly refined at run-time, by periodically invoking regression on the newly monitored data. MONITORMINING has the advantage of reduced complexity of the designer specifications, better accuracy of regression functions due to a reduced parameter set, and self-evolving with the changes in the system. Our initial experimental results of applying MONITORMINING are quite promising.
AB - The effectiveness of automated system management is dependent on the domain-specific information that is encoded within the management framework. Existing approaches for defining the domain knowledge are categorized into white-box and black-box approaches, each of which has limitations. White-box approaches define detailed formulas for system behavior, and are limited by excessive complexity and brittleness of the information. On the other hand, black-box techniques gather domain knowledge by monitoring the system; they are error-prone and require an infeasible number of iterations to converge in real-world systems. MONITORMlNlNG is a gray-box approach for creating domain knowledge in automated system management; it combines simple designer-defined specifications with the information gathered using machine learning. The designer specifications enumerate input parameters for the system behavior functions, while regression techniques (such as Neural Networks, Support Vector Machines) are used to derive the mathematical function that relates these parameters. These functions are constantly refined at run-time, by periodically invoking regression on the newly monitored data. MONITORMINING has the advantage of reduced complexity of the designer specifications, better accuracy of regression functions due to a reduced parameter set, and self-evolving with the changes in the system. Our initial experimental results of applying MONITORMINING are quite promising.
KW - Automated system management
KW - Autonomic computing
KW - Domain knowledge
KW - Gray-box techniques
KW - Models
UR - http://www.scopus.com/inward/record.url?scp=33744457024&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33744457024&partnerID=8YFLogxK
U2 - 10.1109/INM.2005.1440771
DO - 10.1109/INM.2005.1440771
M3 - Conference contribution
AN - SCOPUS:33744457024
SN - 0780390873
SN - 9780780390874
T3 - 2005 9th IFIP/IEEE International Symposium on Integrated Network Management, IM 2005
SP - 61
EP - 74
BT - 2005 9th IFIP/IEEE International Symposium on Integrated Network Management, IM 2005
Y2 - 15 May 2005 through 19 May 2005
ER -