Improving productivity and efficiency in February 2013

Improving productivity and efficiency through a multistage implementation
Financial services firms can take an existing inefficient infrastructure for
risk management and compliance and gradually grow it into an integrated,
highly efficient grid system.
As shown, an existing infrastructure may comprise stove
pipes of legacy applications disparate islands of applications, tools
and compute and storage resources with little to no communication among
them. A firm can start by enabling one application a simulation application
for credit risk modeling, for example to run faster by using grid
middleware to virtualize the compute and storage resources supporting
that application.
The firm can extend the same solution to another application, for example,
a simulation application used to model market risk. Compute and storage
resources for both simulation applications are virtualized by
extending the layer of grid middleware; thus both applications can
share processing power, networked storage and centralized scheduling.
Resiliency is achieved at the application level through failover built into the
DataSynapse GridServer. If failure occurs or the need to prioritize particular
analyses arises, one application can pull unutilized resources that are
supporting the other application. This process also facilitates communication
and collaboration across functional areas and applications to provide
a better view of enterprise risk exposure.
Alternatively, a firm can modernize by grid-enabling a particular decision
engine. A decision engine, such as one developed with Fair Isaac’s tools,
can deliver the agility of business rules and the power of predictive analytic
models while leveraging the power of the grid to execute decisions
in record time. This approach guarantees that only the computeintensive
components are gridenabled while simultaneously migrating
these components to technology specifically designed for decision
components.
Over time, all applications can become completely grid-enabled or
can share a common set of gridenabled decision engines. All compute
and data resources become one large resource pool for all the applications,
increasing the average utilization rate of compute resources
from 2 to 50 percent in a heterogeneous architecture to over 90 percent
in a grid architecture .
Based on priorities and rules,DataSynapse GridServer automatically
matches application requests with available resources in the distributed
infrastructure. This real-time brokering of requests with available
resources enables applications to be immediately serviced, driving greater
throughput. Application workloads can be serviced in task units of milliseconds,
thus allowing applications with run times in seconds to execute
in a mere fraction of a second. This run-time reduction is crucial as banks
move from online to real-time processing, which is required for functions such as credit decisions made
at the point of trade execution. Additionally, the run time of applications
that require hours to process, such as end-of-day process and loss
reports on a credit portfolio, can be reduced to minutes by leveraging this
throughput and resource allocation strategy.