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What's the biggest roadblock to acceptance of an On Demand Business
environment? It isn't technology. The e-business world provides many examples of
technology evolving quickly to support new needs. The biggest roadblock is
politics. By itself, On Demand Business is an apolitical model: it looks at
what's needed to ensure that all resources are used to the best benefit of the
enterprise. But the enterprise is extremely political. Since these two models --
the apolitical On Demand Business environment and the political enterprise --
are diametrically opposed, we need technology that encourages enterprise
"kingdoms" to share their resources. As grid computing moves from
purely scientific and mathematical use to a more utility-based model, the
technology to leverage the proper use of servers in this environment must be in
place.
In this article, I'll draw examples from some work we did in the IBM Design
Center for On Demand Business for a financial institution. The models used were
based on grid workloads that followed trading examples, but they're
representative of basic grid workload models we've seen for a number of
different business clients.
For the financial institution, we used IBM TivoliĀ® Intelligent Orchestrator
(TIO) software because it enables an organization to add and remove servers from
a processing environment based on the needs of that environment. Traditionally,
TIO has been deployed in Web-based environments to ensure the best use of
servers throughout multiple tiers. This has been accomplished by analyzing the
CPU use of the server and the rate of work to the server from the network. If
TIO can be adapted to serve the grids as well, it would be a powerful tool for
managing servers across multiple heterogeneous environments. Suddenly, servers
become commodities to share across departments -- hoarding of departmental
server resources can become a thing of the past. This article defines the
methodology used to transform the TIO product in its traditional Web-based world
into one of looking across multiple worlds.
The business problem
Enterprises constantly struggle to find the best way to manage their hardware,
software, and management resources. Often new applications drag with them a new
set of servers. To ensure servers will handle expected demand, capacity planners
frequently overestimate the load to ensure there is enough room for growth as
the application usage rises. If the estimate is too low, performance suffers. If
the estimate is too high, resources are wasted. Since the typical political
climate discourages sharing of resources, the wasted resources are never used.
The disturbing thing to CIO and IT organizations is that such wasted resources
can never be brought to bear on resource-starved applications. Some users can be
stuck with poorly performing applications while perfectly useful resources lie
idle.
The solution
Several technologies are coming together now to solve this problem. The advent
of efficient Web services, the proliferation of J2EE underpinnings for those Web
services, and the power of grid computing allow application components to be
efficiently deployed within a heterogeneous environment. Applications have
become less platform- and infrastructure-dependent and more focused on solving
business problems. This paves the way for using grids in a utility model. In
environments where multiple grids must contend for the same resources or must
share resources with non-grid environments, we need something outside the grid
to ensure proper deployment of servers. In our work with the financial
institution, we used IBM Tivoli Intelligent Orchestrator (TIO) to fill this
need. TIO also provides the ability to track deployments of server environments,
which allows a person to keep track of how and for whom a server deployment is
carried out. Simply put, TIO provides the framework for removing the political
as well as technical barriers that might stand in the way of an enterprise
becoming more of an On Demand Business.
In our work with the financial institution, we combined Tivoli Intelligent
Orchestrator with DataSynapse GridServer. Let's look briefly at these products
so you can get an idea of how they work together.
Grid resource managers manage workload from requesters to the available grid
engines. What happens when there's more work than available engines can handle?
Traditionally, this condition causes queuing and additional wait times for the
user community. This article discusses how resources
can be managed into and out of a grid environment using an example
infrastructure.
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