2 WebSphere eXtreme Scale Best Practices for Operation and Management
1.1 Benefits of using WebSphere eXtreme Scale
Today’s dynamic business environment and economic uncertainty mean organizations must
work smarter to remain competitive and to respond to changing customer demands. The key
to working smarter is business agility and cost optimization. Organizations need to ensure
that crucial applications can meet the requirements of rapid increases in demand, can deliver
immediate and consistent responses, and can scale as necessary. There is also a need for
enhanced application performance to keep pace with rising customer expectations regarding
application response time while also managing the costs of the infrastructure.
Many challenges in meeting these demands center around data access. A standard three-tier
application will typically present the following challenges:
򐂰 Keeping system response time low while user load increases
򐂰 Dealing with a high database load that causes slow data access
򐂰 Scaling the current infrastructure as the data volume grows
򐂰 Dealing with a volume of data that is so large that it cannot be stored in a single, physical
򐂰 Maintaining the state in memory and replicating that state across multiple systems in
various data centers in case of failover
The solution to these challenges can be found with WebSphere eXtreme Scale. WebSphere
eXtreme Scale is an elastic, scalable, in-memory data grid. It allows business applications to
process billions of transactions per day with efficiency and near-linear scalability. The data
grid dynamically caches, partitions, replicates, and manages application data and business
logic across multiple servers and virtualization environments. With WebSphere eXtreme
Scale, you can also get qualities of service, such as transactional integrity, high availability,
and predictable response time.
1.2 Adoption of WebSphere eXtreme Scale
WebSphere eXtreme Scale provides a data grid to store data. How you access that grid, and
the type of data that you store in it can vary depending on the application. A detailed systems
and design analysis is required to determine if, when, and how you use WebSphere eXtreme
Scale. This analysis is an important exercise for devising a decision tree road map for
adoption of any new technology.
Chapter 1. Introduction to WebSphere eXtreme Scale 3
Figure 1-1 illustrates a decision tree that can help in this evaluation.
Figure 1-1 Decision tree for adopting WebSphere eXtreme Scale
The decision tree addresses the issues of inadequate response time, slow data access, data
volume issues that affect performance, and memory usage problems. Based on the analysis
of similar problems, the first tier in the tree suggests the type of solution that is appropriate at
an architectural level. The second tier in the tree suggests the usage pattern on how
WebSphere eXtreme Scale can be used to implement the solution. The third tier provides
information about the specific types of caching that might prove useful in resolving the
The problem categories shown in the decision tree are interrelated, in that high database load
leads to slow data access, which by itself is a cause of slow application response times.
Removing one scalability bottleneck by introducing caching or a more advanced data grid
solution often reveals another bottleneck. It might take a few iterations and a few uses of
WebSphere eXtreme Scale before the application meets its scalability targets.
The problems shown in the decision tree invite a number of incremental solutions, such as
tuning database settings for greater performance, optimizing queries, and scaling up
database servers. These solutions do not address the greater scalability limitations of the
application but rather move it toward the limits of the current architecture. The effectiveness of
these solutions depends on how inefficient the application currently is and the availability of
more powerful server equipment, both of which can quickly reach the point of diminishing
HTTP sessions
application data
Use eXtreme Scale
as an application
cache or a side
Data access layer
for applications
move processing
into grid
Use eXtreme Scale
as a data grid
Database cache
data access layer
for applications
Use eXtreme Scale
as a backend
system cache or
an inline cache
Slow data
HTTP session offload
application data cache
DynaCache offload
Use eXtreme Scale
to offload
application state
eXtreme Scale
usage pattern
Cache type
Analysis suggests
caching as an
Analysis suggests
database scaling
Analysis suggests
data access layer
performance tuning
Analysis suggests
external state

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