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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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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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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Replica-aware stubs (client representation of a server-side object)
Contains a list of servers participating in the cluster
IP Multicast – server instances broadcast availability of services
IP Sockets – peer-to-peer communication