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There’s a lot to be learned from looking at systems like automatic transmission or the autopilot feature of an airplane.

By Amy Cowen, August 2006

As today’s enterprises struggle to adapt to rapidly changing business models, IT departments are under constant pressure to do more, do it faster and do it with less. Hiring additional staff is a common response, but human resources already account for a large percentage of IT costs. Adding more people into the IT loop only increases both risk and cost in data center operations.

For researchers in HP Labs, the path to increased agility, improved performance, mitigated risk and maximum return begins not with people but with the automation of labor-intensive IT tasks and data center operations.

Traditional IT systems

Tasks that can be automated are typically those that are better suited to machines than people. They may be repetitive and tedious, or involve a precise set of actions that must be conducted in a specified order. Other tasks ripe for automation may require processing and making decisions on huge amounts of data in seconds.

Because these tasks are a mismatch for human cognitive skills, people make mistakes and inject costly errors.

Kumar Goswami, who leads much of HP Labs' automation research, says there’s a lot to be learned from looking at systems like automatic transmission or the autopilot feature of an airplane.

"These things are designed as closed-loop, self-managed systems from day one,” he says.

Traditional IT systems, on the other hand, are not closed-loop systems. Instead, IT systems are developed in an ad-hoc manner; error handling, monitoring, security and management are not designed into the systems from the beginning.

Automating IT

HP Labs is taking a more structured approach, applying well-known techniques used in physical systems such as control theory, statistics and formal mathematics to develop IT systems that are more automated, less prone to errors and more agile and predictable. This frees up people from tedious maintenance chores and allow them to focus on innovation.

"Our vision," Goswami says, "is one in which the user expresses the 'what,' but not the 'how,' and our tools do the ‘how.' "

Model-driven automation -- along with high-level policies and service-level objectives to manage the entire lifecycle of the systems and their applications – is critical to HP Labs’ approach. These models are standards-based, formal, abstract and machine-readable representations of an entire system including hardware, infrastructure, applications, processes and properties.

“We take a holistic and structured approach to automation,” says Goswami. “It’s structured in that we do things at the model level. It’s holistic in that, ideally, everything is approached from a model standpoint. And, on top of that, it’s agile -- nothing is hard-coded to a specific set of tools, systems or models.”

Models, coupled with a service-oriented architecture, make it possible to work with heterogeneous systems. That makes HP unique in the automation space.

Making the transition

From Goswami’s vantage, companies can start the transition to automation by automating select or specific processes.

"You don't have to do it all at once," he says. As long as businesses plan in advance, they can start automating where it makes sense to them.

“Ultimately, we need to automate everything about IT, from the design to the deployment to the management during operations," he says.

In an automated system, a customer describes a desired service, including properties such as its availability, the service-level objectives and any other constraints (or policies) that need to be taken into account. HP Labs’ technologies then automate the process of creating a particular instance -- or model -- of the service.

But the process doesn’t end there.

Automation continues throughout the entire life cycle; each model builds on the previous one. For example, a resource-allocation tool will use the model to figure out what resources to allocate. It will then add this to the model. The deployment engine will look at the model (which now consists of the specific architecture, components and resources) and deploy the service, then update the model to reflect that.

Once a specific model is established, tools can automate every step of the service or process, including resource allocation, deployment, provisioning, optimization and maintenance.

Automation equals savings for HP-IT

An early example of HP Labs’ approach to model-driven automation took shape in the implementation of the Shared Application Server Utility (SASU) for HP-IT.

HP's IT department developed SASU to provide BEA WebLogic as a service so that separate teams could use the shared service rather than buying and maintaining their own application servers. SASU helped counteract high software licensing costs, under-utilization of existing hardware and redundant support and maintenance within HP-IT.

As the number of applications using SASU increased, accurate provisioning and planning became a challenge. Monitoring and maintaining the growing shared environment involved time- and resource-intensive capacity-planning tasks that took 7-10 days each month. An additional 2-3 days was required to clean data used for the task.

HP Labs was able to solve this problem by automating the data-cleaning process, reducing the tedious manual process to a matter of hours. The team then automated data analysis, which not only saved time, but produced more accurate results.

Current investigations

Another area researchers are exploring is automating problem diagonisis for businesses.

"This is a very difficult problem for our customers. They get tremendous amounts of data from applications and from the systems, and they’re under pressure when the system is not working properly to diagnose what the issue is and fix it in a timely fashion."

Researchers are using statistical and machine-learning techniques to help customers more quickly sift through data and determine what will be most useful in solving a problem. In addition, developed technologies to take a digital "snapshot" of the system when it is not functioning properly and compare it to previous snapshops to determine if a particular problem occured in the past and how it was resolved.

"By automating tasks like these, you're able to create an agile repeatable, and more importantly, a very predictable IT environment," Goswami says. "That's what CIOs are looking for."

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Kumar Goswami
Kumar Goswami

Videos on automation research at HP Labs

» Download Windows Media Video: IT automation overview - Part 1 (6:18) 16.23 MB
» Download Windows Media Video: IT automation innovation Part 2 (3:30) 7.54 MB

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