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Predictive Workload Automation

By Domenico D'ALTERIO posted Wed November 17, 2021 08:51 AM

  

Introduction

Organizations are drawn to the promise of #AIOps to leverage AI-driven Intelligence and automation to make quick and accurate decisions to maintain resiliency. AIOps uses artificial intelligence to simplify IT operations management and accelerate and automate problem resolution in complex modern IT environments. A recent blog by Sanjay Chandru set the stage for guiding you on Best practices for taking a hybrid approach to AIOps. We learned that a key capability of AIOps is to act.   Acting swiftly to resolve issues with predictive workload automation.    
In this blog we will focus on Predictive workload automation:  enable predictive workload automation with open scheduling for integration with DevOps and hybrid cloud solutions.       

Client challenges

IT operations teams face three main challenges in today’s market: growing complexity of workload, shrink of the “batch window” and skill optimization.

Complexity of workload to be managed increases over time mainly due to dependencies between different processing running on different platforms (mainframe, distributed servers, in containers and on the cloud). The challenge is to ensure that the dependencies are correctly managed from a single point of control across the different platforms, with a consolidated view of the processing execution at business level. All of the above is made even more difficult due to the shrinking of the batch window, that required a continuous optimization of the processing execution to meet Service Level Agreements (SLA) constraints, every violation of an SLA constraint can cost real dollars to the company.

Finally, most of the enterprises are facing with a skill issue, with the experienced schedulers and administrators leaving the company, due to retirement or for whatever reason. The generational shift, that cause the loss of experiences Z people, is a general trend that is impacting most of the enterprises running a mainframe, with the associated challenge to reduce the need for expertise and manual intervention.

 

What's now required and how different then what I have today?

Today, many enterprises are embracing devOps best practices to deliver faster new applications and services, keeping high quality standards. It increases the pressure on scheduling team because workload processing added on demand is higher than in the past. There is the need to open scheduling to external teams for an easy integration in devOps toolchain and hybrid cloud application, so part of the on-demand request can be managed automatically, without scheduling team intervention … but in a secure and controlled way. Another consequence of the volatility and variability of the workload to be managed is the increased complexity in keeping overall batch execution under control, avoiding SLA constraints violations.

 

How IBM can help

IBM Z Service Automation Suite provides a full set of tools to automate your enterprise cross platform, with a user-friendly user interface (IBM Service Management Unite) and the possibility to enable a collaborative problem solving with the recent addition of IBM Z ChatOps. IBM Z Workload Scheduler, part of the IBM Z Service Automation Suite, provides E2E workload automation capabilities to manage in a consistent and reliable way any unattended process, with the possibility to have dependencies specified as well event driven scheduling, for job running over different platforms, including containers and cloud environment, from a single point of control.


Key IBM Z Workload Scheduler differentiators are the Dynamic Workload Console, Workload Service Assurance feature and ZoweTM API Mediation Layer and CLI extensions provided as part of the product, that makes IBM Z Service Automation Suite a complete offering for predictive automation.

The Dynamic Workload Console is a modern web-based user interface that enable the user to easily interact with the product, from the modelling to the monitoring and control of the workload execution. It is highly customizable to allow users to easily create their own dashboards in order to perfect fit user needs.


Workload Service Assurance feature leverage artificial intelligence algorithms to predict possible SLA constrains violation in advance and provide instruments to fix the problem before it happens. Leveraging a built-in integration with IBM Workload Manager for z/OS, the product is able to automatically improve the situation is a job is classified as high risk to violate a SLA milestone.



Finally, IBM Z Workload Scheduler extensions of Zowe API Mediation Layer and CLI provide a full set of REST API and workload automation commands to easily integrate scheduling in hybrid cloud applications and devOps toolchain.



Client outcome

With IBM Z Workload Scheduler, an Italian bank was able to significantly reduce cost to manage workload automation across the enterprise, cross-platform, form a single point of control.

What are my next steps?

Depending on where you are on your journey to adopting more of these AIOps best practices we are sharing the following resources to obtain a deeper understanding:

  • To assess your current stage of AIOps maturity and identify action oriented next steps for adopting more AIOps best practices, inquire about the 15-minute online AIOps Assessment for IBM Z.
  • Join the AIOps on IBM Z Community to follow this blog series about best practices for taking a hybrid approach to AIOps

  • And finally, to research our IBM Z products that are implementing AIOps technologies to improve operational resiliency visit our product portfolio page.
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