Recent computer systems are popularly composed of two or more computer servers connected via LAN (Local Area Network), where each computer server individually serves a specific function. The computer system provides the required service via the combination of individual server operations. When running such networked systems, a system administrator must monitor the occurrences of errors. However, there are often a lack of engineers who have expert knowledge in all fields related to the system, including hardware and software knowledge. Each computer is equipped with various hardware components, such as a CPU, memory, and hard disk drives. If any one of such parts breaks down, the computer will not operate. When the faulty component carries out various functions within the computer, it is difficult to specify which part is broken. Moreover, it is difficult to detect whether the state of the total system is stable or not, because various software applications work in each computer and many computers connected via LAN cooperate to realize the total service. In this chapter, we propose a web-based management tool for maintaining computers in a LAN environment. The proposed tool has two main features. One function is to detect hardware faults by utilizing Cacti [1]. Cacti is a complete network graphing solution designed to harness the power of RRDTool (Round Robin Database Tool)’s data storage and graphing functionality. The other function is to detect software errors from abnormal state messages appearing in system LOG files not only in the operating system (OS) but also in the application software. The system
[1]
Paul Kavanagh,et al.
The Open Source Definition
,
2004
.
[2]
Marshall T. Rose,et al.
Management Information Base for network management of TCP/IP-based internets
,
1990,
RFC.
[3]
Akira Hara,et al.
Emergence of the cooperative behavior using ADG; Automatically Defined Groups
,
1999,
GECCO.
[4]
Chris Lonvick,et al.
The BSD Syslog Protocol
,
2001,
RFC.
[5]
James Bessen.
Open Source Software
,
2006
.
[6]
Akira Hara,et al.
Extraction of Error Detection Rules without Supervised Information from Log Files Using Automatically Defined Groups
,
2006,
2006 IEEE International Conference on Systems, Man and Cybernetics.
[7]
Akira Hara,et al.
Discovering Multiple Diagnostic Rules from Coronary Heart Disease Database using Automatically Defined Groups
,
2005,
J. Intell. Manuf..