Overcome Six Obstacles to Efficient Data Center Automation

Mehul Amin is Director of Engineering at Advanced Systems Concepts, Inc. (ASCI).

Today’s data center managers often oversee an increasingly complex environment containing a heterogeneous mix of applications, databases and competing platforms. Managing this environment via scripting and platform/application-specific scheduling tools can be inefficient and time-consuming, breeding silos of automation across the data center, leading to workload management challenges. These uncoordinated tools require continuous updating in the face of change, increasing operational costs and reducing staff productivity.

However, there is a more efficient enterprise-wide strategy to workload automation which can help data center managers with increasing responsibilities. Often dealing with flat budgets and IT skills gaps, enterprises are looking into such strategies to avoid disruption. In fact, according to Gartner’s “Predictions 2018: IT Operations,” by 2020, 75 percent of enterprises will experience visible business disruptions due to infrastructure and operations skills gaps.

Solving automation problems with specific automation tools is a fragmented approach which can be described as “elemental.” The more efficient approach is “architectural,” facilitating database automation and scheduling to simplify management of complex workflows, reducing the need for custom scripting while improving productivity and reducing IT operational costs.

The architectural approach helps data center managers overcome six automation obstacles, such as:

Fragmented Scheduling Across Servers – Platform-specific scheduling tools increase risk by forcing the scheduling of data center tasks and processes to take place at the machine and operating system level. A workload automation solution unifies these tools within a single framework for better control, eliminating the need to license, deploy and manage multiple tools, and eliminates cumbersome and error-prone practices of scheduling and executing jobs at the database level.

Repetitive and Time-Consuming Data Center Operations – Essential to the effective operation of the data center are maintenance functions such as database backups, file system movements, FTP operations and more. A workload automation solution can enable management via a single solution, improving batch success rates while reducing runtimes, improve resource availability by dynamically balancing workload execution across multiple databases and platforms, and provide centralized monitoring and alerts for faster resolution of problems.

Complex Database Growth and Change – Implementing new technologies into the data center as the enterprise grows is a key element for competitive and efficient operations. The elemental approach to data center automation presents a short-term, temporary fix. This is best reflected in the predominant method for automation: the script. Creating, modifying and testing scripts is time consuming and resource intensive, which can inhibit the ability to update existing workflows or incorporate new ones. The architectural approach leverages production-ready templates and workflows to reduce reliance on custom scripting, provides an object-based architecture that emphasizes reusability, simplifies the modification of existing workloads, and accommodates for data growth and complexity by simplifying data integration, ETL and data warehousing processes.

Security Vulnerability – An elemental data center automation strategy risks unauthorized changes to production processes and workloads, potentially resulting in the release of sensitive information. In regulated industries, for example, any unauthorized change to a production workflow involving patient data or financial account information can be devastating. Additionally, platform-specific scheduling tools can cause unmanaged proliferation of jobs and scripts housed across disparate servers, increasing job failure risk. A workload automation solution establishes user-based roles, providing a platform to prevent unauthorized changes and giving data center managers the ability to monitor and audit changes for security compliance.

Virtual and Cloud Complications – Virtual and/or cloud-based data centers encounter challenges when numerous machines on the same network need to be managed. Further complications occur when the data center is comprised of a heterogeneous combination of on premise, virtual and/or cloud computing environments. These platforms come equipped with their own native job schedulers, but just as physical databases, they represent silos of automation that prevent data centers from dynamically managing and provisioning virtual and/or cloud instances within data center-wide workloads. An architectural solution provides a single point of control for virtual and physical computing platforms, reduces IT operational costs by automatically provisioning and de-provisioning virtual/cloud resources based on workload execution, and automates desktop virtualization and administrative processes that involve virtualized assets.

Interdepartmental Orchestration Difficulties – As automation is frequently moving beyond the data center, and even beyond the IT department, transitions among departments can create unnecessary additional work. For example, when one department finishes part of a high-level business workflow, the information and data is handed off to another department, which often requires manual dependencies and intervention. The architectural approach offers tools for coordination, often eliminating many interdepartmental dependencies involved in handing off workflow checkpoints.

While custom scripting and platform-specific tools can be a quick fix for busy data center managers facing increased responsibilities and static resources, taking the time to implement an architectural approach can solve a number of issues managers face today, and prevent problems in the future. A simple, single point of control for database automation that follows the architectural model can assist data center managers with repetitive tasks and free staff for more advanced projects that provide greater business value to the enterprise.

Opinions expressed in the article above do not necessarily reflect the opinions of Data Center Knowledge and Informa.

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