AIOps and Digital Transformation
AIOps is the use of artificial intelligence for IT operations management and is a critical component of any organization’s digital transformation efforts. Successful digital transformation depends on AIOps tools to keep pace with business.
AIOps refers to the way data is managed by IT teams. According to Gartner: “AIOps platforms utilize big data, modern machine learning, and other advanced analytics technologies to directly and indirectly enhance IT operations (monitoring, automation and service desk) functions with proactive, personal, and dynamic insight. AIOps platforms enable the concurrent use of multiple data sources [and] data collection methods.”
To effectively monitor and manage complex, distributed environments, IT operations teams are using artificial intelligence and machine learning to automate the identification and resolution of enterprise IT service performance issues. Using large data sets generated by data silos — physical and virtual IT systems, performance monitoring tools, and more — IT can easily and reliably monitor assets, obtain visibility into all applications and system dependencies, and reduce mean time to repair (MTTR) in the event of incidents.
AIOps has tremendous value when its benefits and use cases are extended to the automation environment. In our fast-paced world, organizations and devops teams require flexible IT infrastructures that can quickly adapt to dynamic, real-time demands.
With ActiveBatch Workload Automation, users can easily optimize the distribution of workloads to improve the likelihood of on-time, successful job completions while reducing idle machines by leveraging machine learning and predictive analytics. ActiveBatch provides information on the execution of jobs and accelerates troubleshooting by isolating root causes more quickly.
Locate Optimal Resources for Completing Jobs
As IT environments scale, it becomes ineffective and time-consuming for IT to manually search for machines capable of completing certain jobs. With ActiveBatch Dynamic Queue Characteristics, users can instruct ActiveBatch to evaluate multiple machines at runtime. Machines can be evaluated for characteristics like available disk space, registry values, or the presence of an active service. Characteristic values can also be returned from PowerShell scripts, resulting in nearly limitless flexibility. No matter how many servers IT operations automates, ActiveBatch quickly and reliably submits each job to the optimal machine depending on real-time data. For highly available environments, this includes distributing workloads to only active machines, preventing delays and downtime.
ActiveBatch Smart Queue Reduces Idle Machine Resources
ActiveBatch offers several flexible, scalable tools that help IT monitor and manage virtual machines and private- and public-cloud resources for optimal usage and reduced downtimes. For example, ActiveBatch Smart Queue facilities will dynamically scale resources up or down based on real-time demands. By enabling Smart Queue, IT teams can optimize their virtual and cloud environments while eliminating spend on idle machine resources.
Machine Learning Drives Seamless IT Operations Management
ActiveBatch Heuristic Queue Allocation (HQA) allows for seamless machine scalability by bringing the power of machine learning to workload automation. HQA analyzes historical data and predicts the optimal allocation of systems across on-prem, private-cloud, and public-cloud resources. By optimizing how jobs are placed in a hybrid IT environment, HQA improves job performance and can sharply reduce slack time. This maximizes resource usage while minimizing the costs associated with cloud computing. This scalable approach allows IT to operate with more agility and speed when responding to business requests and changing demands.