The document discusses WebLogic clustering, which allows for failover and load balancing of WebLogic server instances. It provides an introduction to clustering and its benefits like high-availability and scalability. The document then describes WebLogic clustering specifically, including the types of objects that can be clustered. It discusses load balancing algorithms and shows the results of testing a clustered WebLogic environment with round-robin, weight-based, and random algorithms. The round-robin algorithm proved most effective with improved scalability when adding a second node to the cluster.

1. WebLogic
Clustering -
Failover, and
Load Balancing
training
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2. Introduction
 What is Clustering?
 Close cooperation of two or more
replicated servers to ensure fast, continuous
service to users.
 Clustering must provide the following:
 No bottlenecks to scaling
 No single point of failure
 Transparency to application developers
 Single-system image to administrators
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3. Clustering
 Benefits of Clustering
 High-Availability and Scalability
 Key Capabilities of Clustering
 Failover
 New object can take over for the failed object
 Load Balancing
 Even distribution of jobs
 Challenges
 Replication without increasing load
 Propagating changes to servers participating in
the cluster
 Complex setup
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4. WebLogic Clustering
 What is WebLogic?
 J2EE Application Server that is a platform for
developing distributed applications
 A hosting environment for EJBs
 Provides several services such as: JMS, JNDI,
JDBC, Transaction control, etc…
 WebLogic Clustering
 Consists of multiple copies of the WL Server
working together—so that there are
alternative objects to do the same job.
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5. WebLogic Clustering
 Types of objects that can be clustered
 Servlets and JSPs
 EJBs
 RMI objects
 JMS destinations
 JDBC Connections
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6.  Replica-aware stubs created at compile time
 Contains load-balancing algorithm
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7. Typical Clustered Multi-Tier Architecture
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8. Load Balancing
 Algorithms used:
 Round-Robin
 Cycles through instances in order
 Weight-Based
 Allocation of requests based upon relative
weights
 Random
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9. What we did
 Set up a WL server in a clustered
environment
 2 Nodes and 1 Admin. server
 Deploy an EJB
 Stateful Session Bean that is replicated on the
cluster
 Simulate the action of multiple clients
 Remote Java client that creates multiple
threads and sends off requests in quick
succession
 Server logs client requests
 Used for gathering statistics
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10. Test Setup
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11. Results
 Most effective algorithm was Round-
Robin
 Weight-based is useful when there are
differences in the cluster hardware
 Degenerates to Round-Robin when using
identical weights in the cluster.
 Random was close to Round-Robin
 Scalability
 1 server results in cacheful exceptions
 Adding another node to the cluster
removed this problem with identical clients
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12. Vibrant Technologies &
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13. Thank you
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