Today, all CIOs demand that their data centers be able to mange dynamic workloads and have the ability to scale up or scale down dynamically based on their workloads. The demands of digital transformation of various business process implies that the entire IT services stack must be built for "Everything-As-A-Service." And this needs to be intelligent, self-learning and self healing IT system.
The always ON paradigm of the digital era brings in its own set of IT challenges. Today, IT systems are expected to:
Manage all incoming data. Data from a wide variety of sources and formats must be imported, normalized, sorted and managed for business applications. The volume of incoming data can vary greatly - but the systems must be able to handle it. IT systems must accommodate a wide variety of large scale data sources and formats, including Big Data technologies and integration with legacy in-house and third-party applications.
- Enable Business Apps to sequence data, run data analysis and generate reports on demand. On demand workloads makes it difficult to predict future workloads on IT systems - but the systems must be able to handle it.
- Ensure all Business Apps adhere to the published SLA.
This expectation on IT systems places a tremendous pressure to automate the management of all IT resources. As a result CIOs want:
- All workloads are effectively spread across n-tier architectures, across heterogeneous compute resources and across global data center networks.
- Predictively detect IT infrastructure failures, Automatically remediated failed/disrupted processes and workflows in near real time.
- Intelligently predict future work loads to automatically apply policies about when and where data can reside and how processes can be executed.
The traditional IT workload management solutions relied on time based scheduling to move data and integrate workloads. This is no longer sustainable as it takes too much time and delays in responses to the modern business needs.
As a result, we need an intelligent workload automation system which can not only automate the workload management, but also made intelligent policy based decisions on how to manage business work loads.
Today, the IT industry has responded by developing plethora of automation tools such as Puppet, Chef, Ansible etc. Initially these were designed to simplify IT operations and automate IT operations - mainly support rapid application upgrades and deployments driven by the adoption of DevOps strategies.
However, these tools have to be integrated with deep learning or machine learning systems to:
- Respond dynamically to unpredictable, on-demand changes in human-to-machine and machine-to-machine interactions.
- Anticipate, predict, and accommodate support for fluctuating workload requirements while maintaining business service-level agreements (SLAs)
- Reduce overall cost of operations by enabling IT generalists to take full advantage of sophisticated workload management capabilities via easy-to-use self-service interfaces, templates, and design tools.
Over the next few years, we will see a large number of automation tools that can collectively address the needs of legacy IT systems (such as ERM, Databases, Business collaboration tools: Emails, fileshare, unified communications, and eCommerce, Webservices) and 3rd platform Business Apps - mainly entered around IoT, Big-Data, Media streaming & Mobile platforms.
Current digital transformation of the global economy is driving all business operations and services to become more digitized, mobile, and Interactive. This leads to increasing complexity of everyday transactions - which translates to complex workflows and application architectures. For example - a single online taxi booking transactions will involve multiple queries to GIS systems, transactions and data exchanges across several legacy & modern systems.
To succeed in this demanding environment, one needs an intelligent, scalable and a flexible IT workload automation solutions.
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