Uncovering the Potential Workload Management

By the time I left the meeting room, finishing my very first operations review with a customer, I was convinced the reason for the turbulent and often gloomy feeling during the meeting was due to the old but consistent problem of misalignment between business and IT strategy. Despite the fact that enterprises run behind cutting-edge technologies, they often operate without knowing the application of the technology to solve their own problems. As a result, the distance between business and IT increases and reaches a state where the gap becomes a technology debt — a debt that only increases over time, even with most modern technologies in action.

Managing millions of batch jobs is complex. Cross and hierarchical dependencies, diversified holiday calendars due to geographic spread, and lack of automated performance metrics on the job scheduler contribute to this situation. It worsens when enterprises have multiple batch job scheduler solutions, and the need for business to stay relevant, agile, and creative in the competitive market introduces more than thousand changes to the profiles of the batch jobs each week. It is quite obvious how IT operations fall behind with the demanding nature of business driving changes in workload behavior and performance benchmark reports becoming irrelevant with increasing technology debt. A highly sophisticated service management office and effectively implemented problem management function also are not able to solve the practical problem with the latest technologies, focusing only on incident management and irrelevant performance guides.

With ignio implemented and consuming workload performances for past months, it drove the situation little differently to improve the behavior of workload management function as a whole. Beside the standard outcome such as prediction for tomorrow and real-time dashboards to help the workload administrator in day-to-day operations specifically driving the pro-active incident management and QoS associates with it; ignio also reported few major findings to improve the problem management functions:

  • There are sets of jobs which fail to meet basic requirements, such as start time, run time, end time, or even determining the success or failure of those jobs. The complex analyses led by ignio indicate the need to optimize or revisit the performance benchmark associates with those jobs.
  • The dynamic workload impact to business processes over days, weeks, months and years also have an impact to the behavior of batch jobs performance. While practically impossible to distinguish using conventional problem management analyses, ignio’s ability to analyze and propose for a dynamic performance benchmark is a key to bridge the technology gap.
  • It is said that “failure is the key to success”. This is very true in the world of ignio, where ignio also analyzes the failure patterns of the batch jobs and recommends the right adjustments to be made. It also brings in similar analyses to make recommendations to adjust individual batch jobs towards an aligned business need.

Given the ability to see through one’s complex batch management platform, with insightful indication as to review your performance benchmark practically forces the proactive problem management to align IT deliverables toward business outcome. ignio here; depending on complex machine learning algorithm initiates a question to the IT system administrators, Performance Manager and also IT Operations to derive the right metrics to be measured and it does not only stops there as continues to ask questions in regular manner forcing enterprise IT to align toward business driven KPIs.

by Arnab Ray

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