AIOps and its impact
The term artificial intelligence for IT operations, commonly known as AIOps was first coined and defined by Gartner as a process of managing data and information efficiently through a team of IT professionals. As per Wall Street Journal, with each passing year, application and service environment is becoming more complex, since organizations varying from medium to large sizes are implementing (on an average) eight different cloud providers. AIOps can enhance the capacity of IT infrastructure management by integrating insights, information and capabilities of existing tools and processes with the horizon of new opportunities and use cases. Hence, the optimum utilization of AI and ML, in the sphere of IT environment, prove befitting and worthy.
Components of AIOps
Broadly speaking, AIOps include the following components in the digital revolution.
- Machine learning
- Base lining of performance
- Anomaly detection
- Root cause analysis automation
- Predictive insights and information
Utility of AIOps platform in resource utilization
Let’s try to analyze the use and importance of AIOps platform in resource optimization.
· Storage management: First of all, AIOps improves the ability to store historical data. It can capture and analyze real-time data as well as data from log files. Computation of metrics and any form of data flow can be captured and analyzed. AIOps also increases the ability to identify any structured pattern and automate data correlation. AIOps can take storage management to a different level where storage network can be monitored and reconfigured using automation. This storage network helps in resource optimization.
· Capacity planning: AIOps is used in capacity planning to correctly map the workload to the appropriate server and virtual machine, reducing human intervention hence, cost.
· Responsive and predictive scaling: AIOps improves resource utilization through, both, responsive and predictive scaling, where resources are either utilized, based on parameters or based on historical data through projected application.
· Anomaly detection: Detecting anomalies is one of the areas where usage of AIOps tops. It can not only prevent potential outages but can also perform a root-cause analysis to detect the source of anomaly. Data sources like DNS, NetFlow and application event log can be combined with malicious IPs and domains to identify potential threat.
· Cloud: Empowering cloud with capabilities to deliver without access to remote components is definitely a cost-effective solution through resource optimization.
Problems AIOps can resolve:
Performance issues in IT operations can be overcome through implementation of AIOps. Gartner rightfully predicted that by end of 2019, 25% of global enterprises will implement AIOps to support at least two major IT operations which can optimize enterprise performance.
· Usage of AIOps reduces application complexity, especially in the field of virtual infrastructure where raw data can be correlated and analyzed to gain meaningful insights.
· AIOps offers flexibility to automate complex and repetitive tasks. Routine requests and non-critical IT system alerts can be automated using AIOps platform.
· AIOps can help access data faster through data visualization solution which in turn can improve decision making.
· GAVel – the flagship product of GAVS Technologies, is a platform of predictive analytics which uses intelligent and insightful data to cater to proactive and predictive risk management. This predictive algorithm correlates with events to detect potential downtime alerts thus, reducing outage issues and costs associated with it. Also, it can correlate large volumes of both, structured and unstructured data with precision. Hence, it optimizes resource utilization with accuracy.
Competency of AIOps in a nutshell
AIOps is a technology that improved the process of identification and detection of the best possible solution and this created a huge impact in both, data enterprise and cloud infrastructure management. AIOps platform can simulate human intelligence and continuously update, as more data is added to the data pool. For example, if an application under virtual machine needs alteration, AIOps can facilitate the same through automation. Hence, it can be concluded that, in order to optimize resource utilization, AIOps can boost the performance of both analysts and tools, garnering desirable outputs.