Answer Posted / raj
http://www.oracle.com/technology/pub/articles/dev2arch/2006/
01/workload-management.html
Applications can request their own fair share of thread
resources by using the fair-share parameter. This tells the
WebLogic Server kernel to allocate threads to the two
applications proportionately, irrespective of their arrival
pattern. In fact, this is done by default without any
configuration.
Administrators no longer have to configure thread counts,
and the optimal thread count is determined by the server
automatically based on the overall throughput measurements.
It is possible to set constraints to enforce minimum and
maximum concurrency without creating dedicated thread
pools.
Unlike execute queues, all WorkManagers share a common
thread pool and a priority-based queue. The size of the
thread pool is determined automatically by the kernel and
resized as needed. WorkManagers (described on the next
page) become very lightweight, and customers can create
WorkManagers without worrying about the size of the thread
pool. Thread dumps look much cleaner with fewer threads.
In the new model, it is possible to specify different
service-level agreements (SLAs) such as fair shares or
response-time goals for the same servlet invocation
depending on the user associated with the invocation. For
example, it is possible to give higher fair share
to "platinum" users than to "evaluation" users for the same
servlet or EJB invocation.
I will describe more about these features in the following
sections.
New Thread Pool Implementation
WebLogic Server 9.0 has a single thread pool for requests
from all applications. Similarly, all pending work is
enqueued in a common priority-based queue. The thread count
is automatically tuned to achieve maximum overall
throughput. Priority of the requests is dynamic and
computed internally to meet the stated goals.
Administrators state goals simply, using application-level
parameters like fair-share, response time goals.
In earlier releases, each servlet or RMI request was
associated with a dispatch policy that mapped to an execute
queue. Requests without an explicit dispatch policy use the
server-wide default execute queue. In WebLogic Server 9.0,
requests are still associated with a dispatch policy but
are mapped to a WorkManager instead of to an execute queue.
Note that the concept of WorkManager described here is
completely different from the Timer and WorkManager
specification described on page 5.
Requests without an explicit dispatch policy use the
default WorkManager of the application. This means that
each application has its own default WorkManager that is
not shared with other applications. This distinction is
important to note. Execute queues are always global whereas
WorkManagers are always application scoped. Even
WorkManagers defined globally in the console are
application scoped during runtime. This means that each
application gets into own runtime instance that is distinct
from others, but all of them share the same characteristics
like fair-share goals. This is done to track work at the
application layer and to provide capabilities like graceful
suspension of individual applications.
As mentioned earlier, each servlet or RMI request is
associated with a WorkManager. By default, all requests are
associated with the application default WorkManager. The
dispatch-policy element can be used to associate a request
with a specific WorkManager either defined within an
application scope or globally at the server level. I will
provide examples on how to use the dispatch policy in a
later section.
Thread Count Self-tuning
One of the major differences between execute queues and the
new thread scheduling model is that the thread count does
not need to be set. In earlier releases, customers defined
new thread pools and configured their size to avoid
deadlocks and provide differentiated service. It is quite
difficult to determine the exact number of threads needed
in production to achieve optimal throughput and avoid
deadlocks. WebLogic Server 9.0 is self-tuned, dynamically
adjusting the number of threads to avoid deadlocks and
achieve optimal throughput subject to concurrency
constraints. It also meets objectives for differentiated
service. These objectives are stated as fair shares and
response-time goals as explained in the next section.
The self-tuning thread pool monitors the overall throughput
every two seconds and uses the collected data to determine
if thread count needs to change. Present thread count, the
measured throughput, and the past history is taken into
account by the algorithm to determine if the thread count
needs to increase or decrease, and new threads are
automatically added to the pool or removed, as needed.
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