Availability assessment of SunOS/Solaris Unix Systems based on Syslogd
  and wtmpx logfiles : a case study
untitled
Availability Assessment of SunOS/Solaris Unix Systems
based on Syslogd and wtmpx log files: A case study
Cristina Simache and Mohamed Kaâniche
LAAS-CNRS — 7 Avenue du Colonel Roche
31077 Toulouse Cedex 4 — France
Mohamed.Kaaniche@laas.fr
Abstract
This paper presents a measurement-based availability
assessment study using field data collected during a 4-
year period from 373 SunOS/Solaris Unix workstations
and servers interconnected through a local area
network. We focus on the estimation of machine
uptimes, downtimes and availability based on the
identification of failures that caused total service loss.
Data corresponds to syslogd event logs that contain a
large amount of information about the normal activity
of the studied systems as well as their behavior in the
presence of failures. It is widely recognized that the
information contained in such event logs might be
incomplete or imperfect. The solution investigated in
this paper to address this problem is based on the use
of auxiliary sources of data obtained from wtmpx files
maintained by the SunOS/Solaris Unix operating
system. The results obtained suggest that the combined
use of wtmpx and syslogd log files provides more
complete information on the state of the target systems
that is useful to provide availability estimations that
better reflect reality.
1. Introduction
Event logs have been widely used to analyze the
error/failure behavior of computer-based systems and
to estimate their dependability. Event logs include a
large amount of information about the occurrence of
various types of events that are collected concurrently
with normal system operation, and as such reflect
actual workload and usage. Some of the events are
informational and are issued from the normal activity
of the target systems, whereas others are recorded when
errors and failures affect local or distributed resources,
or are related to system shutdown and start-up. The
latter events are particularly useful for dependability
analysis.
Computer system dependability analysis based on
event logs has been the focus of several published
papers [1, 2, 4, 5, 7, 8, 9]. Various types of systems
have been studied (Tandem, VAX/VMS, Unix,
Windows NT, Windows 2000, etc.) including
mainframes and largely deployed commercial systems.
The issues addressed in these studies cover a large
spectrum, including the development of techniques and
methodologies for the extraction of relevant
information from the event logs, the identification of
error patterns, their causes and their effects, and the
statistical assessment of dependability measures such
as failure and recovery rates, reliability and availability.
It is widely recognized that such event log based
dependability analyses provide useful feedback to
software and system designers. Nevertheless, it is
important to note that the results obtained are
intimately related to the quality and the accuracy of the
data recorded in the logs. The study reported in [1]
points out various problems that might affect the data
included in the event logs and make incorrect
conclusions likely, considering as an example the
VAX/VMS system. Thus, extreme care is needed to
identify deficiencies in the data and to avoid that they
lead to incorrect conclusions.
In this paper, we show that similar problems can be
observed in the event logs maintained by the
SunOS/Solaris Unix operating system, and we present
a novel approach that is aimed to address such
problems and to improve the dependability estimates
based on such event logs. These results are illustrated
using field data collected during a 4-year period from
373 SunOS/Solaris Unix workstations and servers
interconnected through a LAN. The data corresponds to
event logs recorded via the syslog daemon. In
particular, we use var/adm/messages log files. We
focus on the evaluation of machine uptimes, downtimes
and availability based on the identification of failures
that caused a total service interruption of the machine.
In this study, we show that the consideration of the
information recorded in the var/adm/messages log
files only may lead to dependability estimations that do
not faithfully reflect reality due to incomplete or
imperfect data recorded in the corresponding logs. For
the estimation of these measures, we start with the
assumption that machine failures can be identified by
the last events recorded in the event log before the
machine goes down and then is rebooted. This
assumption was considered in the study reported in [3].
However, the validity of this assumption is
questionable in the following situations: 1) the machine
has a real activity between the last event logged and the
reboot without generating events in the logs, 2) the
time when the failure occurs is earlier than the
timestamp of the last event logged on the machine. To
address these problems and to obtain more realistic
estimations, we propose a solution based on utilization
of additional information obtained from wtmpx Unix
files, as well as data characterizing the state of the
machines included in the data collection that are
recorded at a regular basis during the data collection
procedure. The results clearly show that the combined
use of this additional information and syslogd log
files have a significant impact on the estimations.
To our knowledge, the approach discussed in this
paper and the corresponding results have not been
addressed in the previous studies published on the
exploitation of syslogd log files for the dependability
analysis of Unix based systems, including our paper
The rest of the paper is structured into 5 sections.
Section 2 describes the event logging mechanism in
Unix and the data collection procedure that we have
used in our study. Section 3 presents the dependability
measures that we have considered and discusses
different approaches and assumptions to estimate them
from the collected data. Section 4 presents some results
illustrating the benefits of the proposed approach, as
well as various statistics characterizing the
dependability of the Unix systems considered in our
study.
2. Event logging and data collection
For the Unix operating system, the event logging
mechanism is implemented by the syslog daemon
(denoted as syslogd). Running as a background
process, this daemon listens for the events generated by
different sources: kernel, system components (disk,
memory, network interfaces), daemons and
applications that are configured to communicate with
syslogd. These events inform about the normal
activity of the system as well as its behavior under the
occurrence of errors and failures including reboot and
shutdown events. The configuration file
/etc/syslog.conf specifies the destination of each
event received by syslogd, depending on its severity
level and its origin. The destination could be one or
several log files, the administration console or the
operator.
The events that are relevant to our study are
generally stored in the /var/adm/messages log file.
Each message stored in a log file refers to an event that
occurred on the system due to the local activity or its
interaction with other systems on the network. It
contains the following information: the date and time
of the event, the machine name on which the event is
logged and a description of the message. An example
of an event recorded in the log file is given below:
Mar 2 10:45:12 elgar automountd[124]:
server mahler not responding
The SunOS/Solaris Unix operating system limits the
size of the log files. Generally, only the log files
corresponding to the last 5 weeks of activity are kept. It
is necessary to set up a data collection strategy in order
to archive a large amount of data. This is essential to
obtain representative results for the dependability
measures characterizing the monitored systems.
In our study, we have included all the
SunOS/Solaris machines connected through the LAAS
local area network, excluding those used for
experimental testbeds or maintenance activities. We
have developed a data collection strategy to
automatically collect the /var/adm/messages log
files stored on these machines. This strategy takes into
account the frequent evolution of the network
configuration during the observation period in terms of
variation of the number of connected systems, updates
or changes of the operating system versions,
modification of software configurations, etc. A shell
script executed each week via the cron mechanism
implements the strategy and remotely copies the log
files from each system included in the study and
archives them on a dedicated machine. After each data
collection campaign, a text file (named DCSummary)
containing a summary of the data collection campaign
is created. This summary indicates the status of each
machine included in the campaign and how the
collection of the corresponding log file has been done.
For each machine, the status information reported in
the summary is one of the following:
• alive_OK: the machine is alive and the copy of its log
file succeeded;
• alive_KO: the machine is alive but the copy of its log
file failed. For this case, a description of the failure
symptom and cause is also included: shell problem,
connection ended by tiers, etc.
• no_answer: the machine did not answer to a ping
request before expiration of the default timeout
period.
The information included in the DCSummary file is
used to verify each data collection campaign and solve
the problems that may appear during the collection. It
is also useful to improve the accuracy of dependability
measures estimation (see Section 3.2). More detailed
information about the syslogd mechanism and the
data collection strategy are reported in [6].
3. Dependability measures estimation and
assumptions
Various types of dependability analyses can be
carried out based on the information contained in the
log files and several quantitative measures can be
considered to characterize the dependability of the
target machines: machine uptimes and downtimes,
reliability, availability, failure and recovery rates, etc.
In order to evaluate these measures, it is necessary to
identify from the log files the failure occurrences and
the corresponding service degradation durations. Such
task is tedious and requires the development of
heuristics and predefined failure criteria. An example
of such analysis is reported in [7].
In our study, we have focused on the availability
analysis of the individual machines included in the data
collection. In this context, we have considered machine
failures leading to a total interruption of the service
delivered to the users, followed by a reboot. The time
between the failure occurrence and the end of the
reboot corresponds to the total service interruption
period of the system. Apart from these periods, the
system is considered to be in the normal functioning
state where it delivers an appropriate service to the
users.
In order to evaluate the availability of the machines
included in the study, we need to estimate for each
machine the corresponding uptimes (denoted as UTi)
and downtimes (DTi), based on the information
recorded in the event logs. Each downtime value DTi
corresponds to the total service interruption period
associated to the i
failure. It is composed by the
service degradation period due to the failure occurrence
and the reboot period. Each uptime value corresponds
to the period between two successive downtimes.
Using the uptime and downtime estimates for each
machine j, we can evaluate the corresponding
availability (noted Aj) and the unavailability (noted
UAj). These measures are computed with the following
formulas:
UAj =� UTi ⁄ �(UTi +DTi) and UAj = 1 - UAj (1)
3.1. Machine uptimes and downtimes
estimation
The estimation of machine uptimes and downtimes
is carried out in two steps:
1) Identification of machine reboots and their
duration.
2) Identification of failures associated to each reboot
and of the corresponding service interruption
period.
To identify the occurrence of machine reboots and
their duration, we have developed an algorithm based
on the sequential parsing and matching of each event
recorded in the system log files to specific patterns or
sequences of patterns characterizing the occurrence of
reboots. Indeed, whereas some reboots can be explicitly
identified by a “reboot” or a “shutdown” event, many
others can be detected only by identifying the sequence
of the initialization events that are generated by the
system when it is restarted. The algorithm is described
in [4, 6]. It gives, for each reboot i identified in the
event logs and for each machine, the timestamp of the
reboot start (dateSBi), the timestamp of the reboot end
(dateEBi) and the associated service interruption
duration.
The identification of the timestamp of the failure
associated to each reboot and the corresponding service
interruption period is more problematic. In the study
reported in [3], it was assumed that the timestamp of
the last event recorded before the reboot (denoted as
dateEBRi) identifies the failure occurrence time. With
this assumption, each uptime UTi and downtime DTi
can be evaluated as follows:
UTi = dateEBRi – dateEBi-1 and
DTi = dateEBi - dateEBRi (2)
where i is the index of the current reboot, i-1 the index
of the previous reboot.
The consideration of EBR for the estimation of UTi
and DTi parameters may not be realistic in the
following situations (denoted as S1 and S2):
S1) The system could be in a normal functioning state
during a period of time between EBR and the
following reboot although it does not generate any
event into the log files during that period.
S2) The beginning of the service interruption period
for the users could be prior to the timestamp of
the EBR event. This happens for instance when a
critical failure affects the machine in such a way
that it becomes completely unusable to the users,
without preventing the event logging mechanisms
from recording some messages into the log files.
A careful analysis of the data collected during our
study revealed that the above situations are common.
To address this problem and to improve downtime and
uptime estimation accuracy, it is necessary to use
auxiliary data that provides complementary information
on the activity of the target machines.
In this paper, we present a solution based on the
correlation of data collected from the
/var/adm/messages log files, with data issued from
wtmpx files also maintained by the SunOS/Solaris
operating system. We also use the information recorded
in the DCSummary file (see Section 2). The following
section presents the method developed to extract the
data from the wtmpx file and how we used this data to
adjust the estimation of machine uptimes and
downtimes.
3.2. Uptime and downtime estimations
refinement
3.2.1. wtmpx files. The SunOS/Solaris Unix operating
system records into the /var/adm/wtmpx binary file
information identifying the users login/logout. Through
the pseudo-user reboot it also records information on
the system reboots. The wtmpx file is organized into
records (named also entries) with a fixed size. Each
record has the format of a data structure with the
following fields:
• the user login name: “user”;
• the id associated to the current record in the
/etc/inittab file: “init_id”;
• the device name (console, lnxx): “device”;
• the process id: “pid”;
• the record type: “proc_type”;
• the exit status for a process marked as
DEAD_PROCESS: “exit_status” and “term_status”;
• the timestamp of the record: “date”;
• the session id: “session_id”;
• the length of the machine’s name: “length”;
• the machine’s name used by the user to connect, if it
is a remote one: “host”.
We developed a specific algorithm that collects the
wtmpx file of each machine included in the study on a
regular basis and processes the binary file to extract the
information that is relevant to our study. The results of
the algorithm are kept in a separate file for each
machine. Figure 1 presents examples of records
obtained for a machine of our network.
The first two records show that the root user
connected to the local system from the system named
cubitus on November 6, 2001 at 16h 37mn 41s, using
the rlogin command. The next records inform about the
occurrence of a reboot event about 3 minutes later. The
third record shows that this reboot was done via a
shutdown command executed probably by the root
user. The sequence of records corresponding to a
reboot event is much longer than this example. The
whole sequence is not presented in Figure 1, the aim of
the illustration is to show some examples of records as
extracted from wtmpx files by our algorithm.
In the following, we outline the approach that we
developed to use the information extracted from the
wtmpx files together with the information from the
DCSummary files in order to refine the uptime and
downtime estimations, considering situations S1 and
S2 discussed in Section 3.1.
2001 Nov 6 16:37:41 user=.rlogin host=
length=0 init_id=r100
device=/dev/pts/1 pid=25220
proc_type=6 term_status=0
2001 Nov 6 16:37:41 user=root host=cubitus
length=8 init_id=r100
device=/dev/pts/1 pid=25220
proc_type=7 term_status=0
2001 Nov 6 16:40:35 user=shutdown host=
length=0 init_id=
device=~
pid=0 proc_type=0
term_status=0 exit_status=0
2001 Nov 6 16:41:39 user= host=
length=0 init_id=
device=system boot pid=0
proc_type=2 term_status=0
2001 Nov 6 16:42:09 user= host=
length=0 init_id=
device=run–level 3 pid=0
proc_type=1 term_status=0
Figure 1. Examples of records from /var/adm/wtmpx
obtained with our algorithm
3.2.2. Situation S1: an operational activity exists
between EBR and SB events. The detailed analysis of
the collected data from the log files and comparison
with the information extracted from wtmpx files
showed that the situation where a real activity exists
between the last event recorded before a reboot (EBR)
and the event identifying the start of the following
reboot (SB event) recorded in the
/var/adm/messages log files appears quite often.
This situation occurs when the machine functions
normally but its activity doesn’t produce any message
into the log file maintained by the syslogd daemon. The
cause could be that the applications or services run by
the users aren’t configured to communicate with the
syslogd daemon.
To better understand this case, Figure 2 gives an
example of a sequence of events characterizing the
state of the corresponding system, taking into account
the information extracted from the
/var/adm/messages, wtmpx and DCSummary files.
For each event, we indicate the timestamp when it is
logged, a short description and the source file from
which the event is extracted. For wtmpx events, we
present only the fields which are useful to identify the
system activity, the other fields are not significant for
this analysis.
For this example, the events recorded in the
/var/adm/messages log file let us believe that the
system had no activity between December 8 at 18:06
(EBR event) and December 9 at 15:30, the timestamp
of the reboot start. However, the analysis of the
DCSummary and wtmpx files shows that the system
had a real activity between EBR and SB events. In fact,
we see that the data collection campaign was
successfully carried out on December 9 at 6:43.
Event # Event date Event description File where the event is logged
..................
2002 Dec 8 18:06:08
2002 Dec 9 06:43:34
2002 Dec 9 13:18:45
2002 Dec 9 13:35:21
2002 Dec 9 13:47:57
2002 Dec 9 13:48:48
2002 Dec 9 15:18:46
2002 Dec 9 15:29:20
2002 Dec 9 15:29:25
2002 Dec 9 15:29:25
2002 Dec 9 15:29:27
..................
2002 Dec 9 15:29:52
2002 Dec 9 15:30:52
2002 Dec 9 15:30:52
2002 Dec 9 15:30:52
2002 Dec 9 15:30:52
2002 Dec 9 15:30:53
last event before reboot <EBR>
alive_ok
user=UserC; device=pts/0; pid=2362; proc_type=7
user=UserB; device=pts/1; pid=2379; proc_type=7
user=UserB; device=pts/1; pid=2379; proc_type=8
user=UserA; device=pts/1; pid=2434; proc_type=7
user=UserA; device=pts/1; pid=2434; proc_type=8
user=UserB; device=console; pid=2644; proc_type=7
user=UserB; device=console; pid=338; proc_type=8
user=UserB; device=console; pid=2644; proc_type=8
user=LOGIN; device=console; pid=2742; proc_type=6
..................
user=troot; device=console; pid=334; proc_type=7
user=sac; device=; pid=333; proc_type=8
user=troot; device=console; pid=334; proc_type=8
user=; device=run-level 6; pid=0; proc_type=1
user=rc6; device=; pid=2899; proc_type=5
reboot start <SB>
var/adm/messages log file
DCSummary
wtmpx
wtmpx
wtmpx
wtmpx
wtmpx
wtmpx
wtmpx
wtmpx
wtmpx
..................
wtmpx
wtmpx
wtmpx
wtmpx
wtmpx
var/adm/messages log file
Figure 2. Example illustrating situation S1
Moreover, the records from wtmpx file show, for
example, that UserA used the system on December 9
between 13:48 (information given by the proc_type
field value equal to 7, that is the process with pid=2434
started at the time of this record) and 15:18
(proc_type=8, the same process ended at the time of
this record), corresponding to an utilization period of
the system of nearly one hour and a half.
In this situation, the EBR event as defined earlier
doesn’t correspond to the beginning of the total service
interruption period. Thus, the estimated value of the
downtime parameter using the assumption discussed in
Section 3.1, does not faithfully reflect the real value of
the service interruption period. Based on the correlation
of the information provided by the three data source
files, a refined and more accurate estimation of
machine downtimes and uptimes could be obtained.
The refinement consists in associating the failure
occurrence time to the timestamps of the last event
recorded before the reboot based on the information
contained in /var/adm/messages, wtmpx and
DCSummary files.
3.2.3. Situation S2: the service interruption period
starts before the EBR event. This situation occurs
when critical failures affect the system in such a way
that it becomes completely unusable, without
preventing the event logging mechanisms from
recording some messages into the log files. During the
recovery phase, the actions performed by the system
administrators may include several unsuccessful reboot
attempts that are not recorded in the
/var/adm/messages log file, but some events
referring to them are written in the wtmpx file. Using
this information, just like in the previous case, we can
refine the downtime and uptime estimations by
associating the failure occurrence time to the
timestamps of the events recorded in the wtmpx file
that better reflects the start of the service interruption.
An example of a sequence of events illustrating this
case is given in Figure 3.
Event # Event date Event description File where the event is logged
2003 Jan 9 10:18:59
2003 Jan 9 10:21:39
2003 Jan 9 10:21:39
2003 Jan 9 10:21:39
2003 Jan 9 10:21:39
2003 Jan 9 10:21:48
2003 Jan 9 10:21:48
2003 Jan 9 10:22:05
2003 Jan 9 10:22:13
2003 Jan 9 10:22:13
2003 Jan 9 10:22:16
user=root; device=console; pid=2370; proc_type=7
user=sac; device=; pid=425; proc_type=8
user=root; device=console; pid=2370; proc_type=8
user=; device=run-level 5; pid=0; proc_type=1
user=rc5; device=; pid=25952; proc_type=5
user=UserC; device=pts/3; pid=11584; proc_type=8
user=UserC; device=pts/1; pid=11359; proc_type=8
last event before reboot <EBR>
user=rc5; device=; pid=25953; proc_type=8
user=uadmin;device=; pid=26121; proc_type=5
reboot start <SB>
wtmpx
wtmpx
wtmpx
wtmpx
wtmpx
wtmpx
wtmpx
var/adm/messages log file
wtmpx
wtmpx
var/adm/messages log file
Figure 3. Example illustrating situation S2
We can identify the events extracted from the
wtmpx file informing upon the stop of the system:
event # 2 with user field “sac” and proc_type “ 8”
(dead process) followed by events #3, #4, and #5
notifying the system run-level change to run-level 5
(this one is used to properly stop the system).
This example shows that the start of the service
interruption period is prior to the EBR event recorded
in the /var/adm/messages log file. The refinement
of the uptime and downtime estimations
corresponding to such situations consists in
associating the failure occurrence time to the
timestamps of the last event recorded in the wtmpx
file before the start of the reboot sequence.
4. Experimental results
The analyses presented in this Section are based on
/var/adm/messages log file data collected during
45 months (October 1999 – July 2003) from 418
SunOS/Solaris Unix workstations and servers
interconnected through the LAAS local area
computing network. As shown in Figure 4, the data
collection period differed significantly from one
machine to another due to the dynamic evolution of
the network. For more than 70 % of the machines, the
data collection period was longer than 21 months. On
the other hand, it can be noticed that some machines
have a quite short data collection period. In order to
have significant statistical analysis results, we
excluded from the analysis the machines for which
the data collection period was shorter than 2000 hours
(about 3 months). Consequently, the results presented
in the following concern 373 Unix machines. Among
these machines, 17 correspond to major servers for
the entire network or a sub-set of users: WWW,
NIS+, NFS, FTP, SMTP, file servers, printing
servers, etc.
Figure 4. Examples of records from
/var/adm/wtmpx obtained with our algorithm
The application of the reboot identification
algorithm on the collected data allowed us to identify
12805 reboots for the 373 machines, only 476 reboots
concern the 17 servers. Based on the information
provided by the reboot identification algorithm, we
evaluated for each machine the associated uptimes
UTi and downtimes DTi, and the availability measure.
The collection of wtmpx files started later than the
/var/adm/messages log files. For this reason, we
were able to analyze the impact of uptimes and
downtimes estimation refinement algorithms only on
a subset of UTi and DTi values associated with the
reboots identified from the log files. Among the
12805 reboots, this analysis concerned 6163 reboots
(48.13%). For the remaining 6642 reboots, the
corresponding data from the wtmpx files was not
available.
In the following, we first present in Section 4.1 the
results of machine uptimes and downtimes estimation
based on the processing of the set of 6163 reboots
focusing on the impact of the estimation refinement
algorithms. Then, global results taking into account
the whole data collected during our study are
presented in Section 4.2 in order to give an overall
picture on the availability and the rate of occurrence
of reboots characterizing the Unix machines included
in our study.
4.1. Machine uptimes and downtimes
estimation and refinement
The correlation of the information contained in the
/var/adm/messages log files, the wtmpx files, and
the DCSummary files, revealed that both situations S1
and S2 discussed in Section 3.2 are common:
• Situation S1 was observed for 79.35% of the
analyzed reboots;
• Situation S2 was observed for 10.77% of the
analyzed reboots;
For the 9.88% remaining reboots, the assumption
that the EBR recorded in /var/adm/messages file
identifies the last event recorded on the machine
before the reboot was consistent with the information
available in the wtmpx and the DCSummary files.
In order to analyze the impact of the estimation
refinement algorithms on the results, Table 1 gives
the Mean, Median and Standard Deviation of uptime
and downtime values, before and after the application
of our estimation refinement algorithms discussed in
Section 3. Considering the median of the downtime
values, it can be seen that the refinement algorithms
have a significant impact on the results. The median
estimated after the refinement is 66 times lower than
the value obtained without the refinement. The
refinement algorithms have also an impact on the
uptimes estimation, but as expected the improvement
factor is lower than the one observed for the
downtime values (1.8 compared to 66).
Table 1. Machine uptimes and downtimes
estimates before and after refinement
Uptimes UTi Downtimes DTi
before
refinement
after
refinement
before
refinement
after
refinement
Mean 28.3days 1.1 month 5.9 days 1.9 days
Median 6.1 days 10.8 days 8.9 hours 8.1 min
1.7 months 1.8 months 24.1 days 21.1 days
The impact of the estimation refinement
algorithms on availability is summarized in Table 2.
It can be seen the estimated average unavailability
after the refinement is three times lower than the
value estimated based only on the information in the
/var/adm/messages log files. Clearly, the
difference is significant and cannot be ignored.
Table 2. Impact of the estimation refinement
algorithms on Availability and Unavailability
before refinement after refinement
A 89.3% 96.3 %
UA 39.0 days/year 13.7 days/year
4.2. Availability and reboot rates estimated
from the whole data set
This section presents some results concerning the
reboot rates and the availability of the 373
SunOS/Solaris Unix machines included in our study
taking into account the whole set of 12805 reboots
identified from the /var/adm/messages files.
When the wtmpx files were not available (this
concerned 6642 reboots), the estimation of the UTi,
DTi, availability and reboot rates was based only on
the information in the /var/adm/messages files,
using the assumption discussed in Section 3.1. In the
other case (i.e., for the 6163 reboots), we applied the
estimation refinement algorithms presented in
Section 3.2.
Figure 5 plots the reboot rates estimated for each
machine as a function of the data collection period.
The estimated reboot rate for each machine
corresponds to the average number of reboots
recorded during the corresponding observation. It can
be seen that the reboot rates are uniformly distributed
between 10
/hour and 10
/hour.
Figure 5. Unix machines reboot rates as a function
of the data collection period
As indicated in Table 3, the mean value of
machine reboot rates is 1.3 10
/hour, when
considering all Unix machines including workstations
and servers. If we take into account only the servers,
the mean reboot rate is 1.5 times lower (7.7 10
/hour)
corresponding to one reboot every two months.
Table 3. Reboot rate statistics
Mean Median Std. Dev.
SunOS/Solaris
machines
(Workstations
+ Servers)
1.3 10
/h 1.0 10
/h 1.3 10
Servers only 7.7 10
/h 6.4 10
/h 5.6 10
The results illustrating the availability and
unavailability of the Unix machines including
workstations and servers are given in Figure 6 and
Table 4. The mean availability is 97.81 %
corresponding to an average unavailability of 8 days
per year. Detailed analysis shows that only 15 among
the 373 Unix machines included in the study have an
availability lower than 90%.
When considering only the servers, the estimated
availability varies between 99.36% and 99.1% with
an average unavailability of 12 hours per year.
Figure 6. SunOS/Solaris Unix machines
availability distribution
Table 4. Availability and Unavailability statistics
Mean Median Std. Dev.
A 97.81 % 98.79 % 3.07 %
UA 7.99 day/year 4.41 day/year 11.20 day/year
6. Conclusion
Dependability analyses based on event logs
provide useful feedback to software and system
designers. Nevertheless, the results obtained are
intimately related to the quality and the completeness
of the information recorded in the logs. As the
information contained in such event logs could be
incomplete or imperfect, it is important to use
additional sources of information to ensure that the
conclusions derived from such analyses faithfully
reflect reality. The approach investigated in this paper
is aimed to fulfill this objective considering
SunOS/Solaris Unix systems as an example.
In particular, we have shown that the combined us
of the data contained in the syslogd files and the
information recorded in the wtmpx files or through
the monitoring of systems state during the data
collection campaigns provides uptime and downtime
estimations that are closer to reality than the
estimations obtained based on syslogd files only.
This result is illustrated based on a large set of field
data collected from 373 machines during a 45 month
observation period.
In our future work, we will investigate the
applicability of the approach proposed in this paper to
other operating systems such as Linux, Windows 2K
and Mac OS X.
References
[1] M. F. Buckley, D. P. Siewiorek, “VAX/VMS Event
Monitoring and Analysis”, 25th IEEE Int. Symp. on
Fault-Tolerant Computing (FTCS-25), (Pasadena, CA,
USA), pp. 414-423, IEEE Computer Society, 1995.
[2] R. K. Iyer, D. Tang, “Experimental Analysis of
Computer System Dependability”, in Fault-Tolerant
Computer System Design, D. K. Pradhan, Ed., Prentice
Hall PTR, 1996, pp. 282-392.
[3] M. Kalyanakrishnam, Z. Kalbarczyk, R. K. Iyer,
“Failure Data Analysis of a LAN of Windows NT
Based Computers”, 18th IEEE Symp. on Reliable
Distributed Systems (SRDS-18), (Lausanne,
Switzerland), pp. 178-187, 1999.
[4] C. Simache, M. Kaâniche, “Measurement-based
Availability Analysis of Unix Systems in a Distributed
Environment”, The 12th Int. Symp. on Software
Reliability Engineering (ISSRE-2001), (Hong Kong,
China), pp. 346-355, IEEE Computer Society, 2001.
[5] C. Simache, M. Kaâniche, “Event Log based
Dependability Analysis of Windows NT and 2K
Systems”, 2002 Pacific Rim Int. Symposium on
Dependable Computing (PRDC-2002), (Tsukuba,
Japan), pp. 311-315, IEEE Computer Society, 2002.
[6] C. Simache, “Dependability evaluation of Unix and
Windows Systems based on operational data: A
Method and Application”, PhD Thesis, LAAS Report
N°04333, 2004 (in French).
[7] A. Thakur, R. K. Iyer, “Analyze-NOW — An
Environment for Collection & Analysis of Failures in a
Network of Workstations”, IEEE Transactions on
Reliability, vol. 45, pp. 561-570, 1996.
[8] M. Tsao, D. P. Siewiorek, “Trend Analysis on System
Error Files”, 13th IEEE Int. Symp. on Fault-Tolerant
Computing (FTCS-13), (Milano, Italy), pp. 116-119,
IEEE Computer Society, 1983.
[9] J. Xu, Z. Kalbarczyk, R. K. Iyer, “Networked
Windows NT System Field Failure Data Analysis”,
Proc. 1999 IEEE Pacific Rim Int. Symp. on
Dependable Computing (PRDC-1999), (Los Alamitos,
CA), pp. 178-185, 1999
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