If the workload management component is not properly distributing
the workload across servers in multi-node configuration, use the following
options to isolate the problem.
Eliminate environment or configuration issues
Determine
if the servers are capable of serving the applications for which they have
been enabled. Identify the cluster that has the problem.
- Are there network connection problems with the members of the cluster
or the administrative servers, for example deployment manager or node agents?
- If so, ping the machines to ensure that they are
properly connected to the network.
- Is there other activity on the machines where the servers are installed
that is impacting the servers ability to service a request? For example, check
the processor utilization as measured by the task manager, processor ID, or
some other outside tool to see if:
- It is not what is expected, or is erratic rather than constant.
- It shows that a newly added, installed, or upgraded member of the cluster
is not being utilized.
- Are all of the application servers you started on each node running, or
are some stopped?
- Are the applications installed and operating?
- If the problem relates to distributing workload across container-managed
persistence (CMP) or bean-managed persistence (BMP) enterprise beans, have
you configured the supporting JDBC providers and JDBC data source on each
server?
If you are experiencing workload management problems related to
HTTP requests, such as HTTP requests not being served by all members of the
cluster, be aware that the HTTP plug-in balances the load across all servers
that are defined in the PrimaryServers list if affinity has not been established.
If you do not have a PrimaryServers list defined then the plug-in load balances
across all servers that are defined in the cluster if affinity has not been
established. If affinity has been established, the plug-in should go directly
to that server for all requests.
For workload management problems relating
to enterprise bean requests, such as enterprise bean requests not getting
served by all members of a cluster:
- Are the weights set to the allowed values?
- For the cluster in question, log onto the administrative console and:
- Select Servers > Clusters.
- Select your cluster from the list.
- Select Cluster members.
- For each server in the cluster, click on server_name and note the
assigned weight of the server.
- Ensure that the weights are within the valid range of 0-20. If a server
has a weight of 0, no requests are routed to it. Weights greater than 20 are
treated as 0.
The remainder of this article deals with enterprise bean workload
balancing only. For more help on diagnosing problems in distributing Web (HTTP)
requests, view the "Web server plug-in troubleshooting tips" and "Web resource
does not display" topics.
Browse log files for
WLM errors and CORBA minor codes
If you still encounter problems
with enterprise bean workload management, the next step is to check the activity
log for entries that show:
- A server that has been marked unusable more than once and remains unusable.
- All servers in a cluster have been marked bad and remain unusable.
- A Location Service Daemon (LSD) has been marked unusable more than once
and remains unusable.
To do this, use the Log Analyzer
tool to open the service log (activity.log) on the affected
servers, and look for the following entries:
- WWLM0061W: An error was encountered sending a request to cluster
member member and that member has been marked unusable for future requests
to the cluster cluster.
Note: It is not unusual for a server
to be marked unusable. The server may be tagged unusable for normal operational
reasons, such as a ripple start being executed while there is still a load
on the server from a client.
- WWLM0062W: An error was encountered sending a request to cluster
member member that member has been marked unusable, for future requests
to the cluster cluster two or more times.
- WWLM0063W: An error was encountered attempting to use the LSD LSD_name to
resolve an object reference for the cluster cluster and has been marked
unusable for future requests to that cluster.
- WWLM0064W: Errors have been encountered attempting to send a request
to all members in the cluster cluster and all of the members have been
marked unusable for future requests that cluster.
- WWLM0065W: An error was encountered attempting to update a cluster
member server in cluster cluster, as it was not reachable from
the deployment manager.
- WWLM0067W: Client is signalled to retry a request. A server request
could not be transparently retried by WLM because of exception:{0}
In attempting
to service a request, WLM encountered a condition that would not allow the
request to be transparently resubmitted. The originating exception is being
caught, and a new CORBA.TRANSIENT with minor code 0x49421042 (SERVER_SIGNAL_RETRY)
is being thrown to the client.
If any of these warning are encountered,
follow the user response given in the log. If, after following the user response,
the warnings persist, look at any other errors and warnings in the Log Analyzer
on the affected servers to look for:
- A possible user response, such as changing a configuration setting.
- Base class exceptions that might indicate a WebSphere Application Server
defect.
You may also see exceptions with "CORBA" as part of the exception
name, since WLM uses CORBA (Common Object Request Broker Architecture) to
communicate between processes. Look for a statement in the exception stack
specifying a "minor code". These codes denote the specific reason a CORBA
call or response could not complete. WLM minor codes fall in range of 0x4921040
- 0x492104F. For an explanation of minor codes related to WLM, see the topic
"Reference: Generated API documentation"for the package and class com.ibm.websphere.wlm.WsCorbaMinorCodes.
Analyze PMI data
The purpose for analyzing the PMI data is to understand
the workload arriving for each member of a cluster. The data for any one
member of the cluster is only useful within the context of the data of all
the members of the cluster.
Use the
Tivoli Performance Viewer to verify that, based on the weights assigned to
the cluster members (the steady-state weights), each server is getting the
correct proportion of the requests.
To turn on PMI metrics using the
Tivoli Performance Viewer:
- Select Data Collection in the tree view. Servers that do not have
PMI enabled will be grayed out.
- For each server that data you wish to collect data on, click Specify...
- You can now enable the metrics. Set the monitoring level to low on
the Performance Monitoring Setting panel
- Click OK
- You must hit Apply for the changes you have made to be saved.
WLM PMI metrics can be viewed on a server by server basis. In
the Tivoli Performance Viewer select Node > Server >WorkloadManagement
>Server/Client. By default the data is shown in raw form in a table, collected
every 10 seconds, as an aggregate number. You can also choose to see the data
as a delta or rate, add or remove columns, clear the buffer, reset the metrics
to zero, and change the collection rate and buffer size.
After you have
obtained the PMI data, you should calculate the percentage of numIncomingRequests
for each member of the cluster to the total of the numIncomingRequests of
all members of the cluster. A comparison of this percentage value to the
percentage of weights directed to each member of the cluster provides an initial
look at the balance of the workload directed to each member of a cluster.
In
addition to the numIncomingRequests two other metrics show how work is balanced
between the members of a cluster, numincomingStrongAffinityRequests and numIncomingNonWLMObjectRequests.
These two metrics show the number of requests directed to a specific member
of a cluster that could only be serviced by that member.
For example,
consider a 3-server cluster. The following weights are assigned to each of
these three servers:
- Server1 = 5
- Server2 = 3
- Server3 = 2
Allow our cluster of servers to start servicing requests, and
wait for the system to reach a steady state, that is the number of incoming
requests to the cluster equals the number of responses from the servers. In
such a situation, we would expect that the percentage of requests routed to
each server to be:
- % routed to Server1 = weight1 / (weight1+weight2+weight3) = 5/10 or 50%
- % routed to Server2 = weight2 / (weight1+weight2+weight3) = 3/10 or 30%
- % routed to Server3 = weight3 / (weight1+weight2+weight3) = 2/10 or 20%
Now let us consider a case where there are no incoming requests
with neither strong affinity nor any non-WLM object requests.
In this
scenario, let us assume that the PMI metrics gathered show the number of incoming
requests for each server are:
- numIncomingRequestsServer1 = 390
- numIncomingRequestsServer2 = 237
- numIncomingRequestsServer3 = 157
Thus, the total number of requests coming into the cluster is:
numIncomingRequestsCluster = numIncomingRequestsServer1 + numIncomingRequestsServer2
+ numIncomingRequestsServer3 = 784
numincomingStrongAffinityRequests
= 0
numIncomingNonWLMObjectRequests = 0
Can we decide based on
this data if WLM is properly balancing the incoming requests among the servers
in our cluster? Since there are no requests with strong affinity, the question
we need to answer is, are the requests in the ratios we expect based on the
assigned weights? The computation to answer that question is straightforward:
- % (actual) routed to Server1 = 390 / 784 = 49.8%
- % (actual) routed to Server2 = 237 / 784 = 30.2%
- % (actual) routed to Server3 = 157 / 784 = 20.0%
So WLM is behaving as designed, as the data are completely what is expected,
based on the weights assigned the servers.
Now let us consider a 3-server
cluster. We have assigned the following weights to each of these three servers:
- Server1 = 5
- Server2 = 3
- Server3 = 2
Allow this cluster of servers to start servicing requests and
wait for the system to reach a steady state, that is the number of incoming
requests to the cluster equals the number of responses from the servers. In
such a situation, we would expect that the percentage of requests that are
routed to Server1-3 would be:
- % routed to Server1 = weight1 / (weight1+weight2+weight3) = 5/15 or 1/3
of the requests.
- % routed to Server2 = weight2 / (weight1+weight2+weight3) = 5/15 or 1/3
of the requests.
- % routed to Server3 = weight3 / (weight1+weight2+weight3) = 5/15 or 1/3
of the requests.
In this scenario, let us assume that the PMI metrics gathered
show the number of incoming requests for each server are:
- numIncomingRequestsServer1 = 1236
- numIncomingRequestsServer2 = 1225
- numIncomingRequestsServer3 = 1230
Thus, the total number of requests coming into the cluster:
- numIncomingRequestsCluster = numIncomingRequestsServer1 + numIncomingRequestsServer2
+ numIncomingRequestsServer3 = 3691
- numincomingStrongAffinityRequests = 445, and that all 445 requests are
aimed at Server1.
- numIncomingNonWLMObjectRequests = 0.
In this case, we see that the number of requests was not evenly
split among the three servers, as expected. Instead, the distribution is:
- % (actual) routed to Server1 = 1236 / 3691= 33.49%
- % (actual) routed to Server2 = 1225 / 3691= 33.19%
- % (actual) routed to Server3 = 1230 / 3691= 33.32%
However, the correct interpretation of this data is the routing
of requests is not perfectly balanced because Server1 had several hundred
strong affinity requests. WLM attempts to compensate for strong affinity requests
directed to 1 or more servers by distributing new incoming requests preferentially
to servers that are not participating in transactional affinity, to compensate
for those servers that are participating in transactions. In the case of incoming
requests with strong affinity and non-WLM object requests, the analysis would
be analogous to this case.
If, after you have analyzed the PMI data
and accounted for transactional affinity and non-WLM object requests, the
percentage of actual incoming requests to servers in a cluster to do not reflect
the assigned weights, this indicates that requests are not being properly
balanced. If this is the case, it is recommended that you repeat the steps
described above for eliminating environment and configuration issues and browsing
log files before proceeding.
Resolve problem or contact IBM support
If the PMI data or client logs indicate an error in
WLM, collect the following information and contact IBM support.
If
the client logs indicate an error in WLM, collect the following information
and contact IBM support.
- A detailed description of your environment.
- A description of the symptoms.
- The SystemOut.logs and SystemErr.logs files for
all servers in the cluster.
- The activity.log file.
- The First Failure Data Capture log files.
- The PMI metrics.
- A description of what the client is attempting to do, and a description
of the client. For example, 1 thread, multiple threads, servlet, J2EE client,
etc.
If none of these steps solves the problem, check to see if the
problem has been identified and documented using the links in the "Diagnosing
and fixing problems: Resources for learning" topic. If you do not see a problem
that resembles yours, or if the information provided does not solve your problem,
contact IBM support for further assistance.
If you do not find your problem listed there, contact
IBM Support.
For current information available from IBM Support on known
problems and their resolution, see the IBM Support page. You should also refer to this page
before opening a PMR because it contains documents that can save you time
gathering information needed to resolve a problem.