Infrastructure operations in the era of complex multi-cloud technologies have emerged as challenging and resource-intensive tasks for an average customer of cloud computing seeking to optimize cloud investments and resource performance. Organizations upgrade their infrastructure to operate at scale while reducing expenses, performance and security issues. However, the diagnostics, resolution and optimization of the infrastructure has emerged as a challenge considering the vast, dynamic and interconnected nature of the underlying hardware resources. Predictive analytics aims to modernize and simplify the complexity of infrastructure operations by leveraging the vast deluge of data associated with IT operations.
Predictive Analytics refers to the practice of using data to determine future patterns and behavior of a system. Predictive analytics tools may use advanced machine learning algorithms and statistical analysis techniques to identify a system model with high accuracy. The prediction models applied to historical and present data can unveil insights into future trends of the system behavior. Using this information, decisions can be made regarding the system proactively. Predictive analytics can help identify correlations between behaviors that otherwise may be overlooked or perceived as isolated. Algorithmic filtering also reduces the noise data and false alarms that may keep IT Ops teams rifling through vast data to identify the most useful insights. This capability offers immense opportunities in the IT infrastructure operations segment, where an isolated anomalous network traffic behavior can translate into a large-scale data leak and remain hidden from sight until it’s too late to react. Predictive analytics constitute the building blocks of a modern AIOps architecture, which includes data ingestion, auto-discovery, correlation, visualization and automated actions.
For cloud operations in particular, predictive analytics has a key role to play in the following domain applications:
Optimizing the Cloud Infrastructure
Many organizations use multiple cloud environments and possibly, a range of siloed infrastructure monitoring, management and troubleshooting solutions. In order to gain visibility into the hybrid multi-cloud environment, Cloud Ops teams using traditional analytics practices may rely on manual capabilities and overlook the correlating information pieces across the infrastructure environment. Data applications and IT workloads are increasingly dynamic in nature and the unpredictable changes in network traffic, infrastructure performance and scalability requirements impact IT operations decisions in real-time. Making the right decisions proactively requires Cloud Ops to collect the necessary information from various sources and being able to correlate the information across the siloed IT environments. Predictive analytics allows users to focus on the knowledge gleaned from data instead of collecting, processing and analyzing information from multiple cloud environments independently. Regardless of the complexity of the cloud network, advanced machine learning algorithms that power predictive analytics capabilities provide the necessary abstraction between the complex underlying infrastructure and data analysis. Cloud Ops are ultimately able to use the collective insights to proactively make the right decisions regarding resource provisioning, storage capacity, server instance selection and load balancing, among other key cloud operations decisions.
Application Assurance and Uptime
Software applications are increasingly an integral component of business processes. When the apps and IT services go down, business processes risk interruption. For this reason, IT shops continuously monitor an array of application and IT network performance metrics that correlate with business process performance. Any anomaly identified in the IT performance impacts business operations. With predictive analytics solutions in place, IT can proactively prepare for possible downtime or infrastructure performance issues. The organization can establish pre-defined policies and measures to apply corrective actions and policies automatically well before application assurance and uptime is potentially compromised. As a result, the organization reduces its dependence upon IT to troubleshoot issues or to improve their Mean-Time-To-Detect (MTTD) and Mean-Time-To-Repair (MTTR) capabilities. Predictive analytics algorithms further cut through the noise to ensure that only the impactful metrics threshold cause the organization to adapt its business processes when needed.
Application Discovery and Insights
Enterprise networks are typically distributed across regions and contain diverse infrastructure components, often operating in disparate silos. A holistic knowledge of the infrastructure and application discovery requires organizations to understand how those components interact and relate with each other, especially since network performance issues can spread across dependencies that are otherwise hidden from view. With the predictive analytics solutions in place, organizations can collect data from across the network, analyze multiple data sources and understand how one infrastructure system can impact the other. In hybrid IT environments, application and infrastructure discovery is a greater challenge considering the limited visibility and control available to customers of cloud-based services. Any lack of automated correlation between network incidents can limit the ability of organizations to steer cloud operations in real-time while responding to potential application and infrastructure performance issues.
Audit, Compliance and Security
Strictly regulated industries are often required to comply with regulations associated with application uptime, assurance, MTTR, end-user experience and satisfaction, among other parameters. Compliance becomes increasingly complex when these organizations have limited visibility and control over their IT network. Performing the audit activities at scale may require organizations to invest greater resources on IT. The regular business may not justify the increased operational overhead and organizations may be forced to cut the corners in audit, compliance and security of sensitive data, apps and IT network. Organizations using advanced artificial intelligence technologies can automate these functions and glean insightful knowledge that can translate into regulatory compliance of hybrid cloud IT environments without breaking the bank.
Security is another key enabler of regulatory compliance and requires more than automation solutions to accurately identify the root-cause of network traffic anomalies. Security infringements in the form of data leak tend to remain under the radar until unauthorized data transfers or network behavior is identified. In that case, it may be too late for organizations to respond without incurring data loss, non-compliance and potentially, the ability to operate in security sensitive industry segments such as healthcare, defense and finance. In complex cloud infrastructure environments, the role of predictive analytics is to unify the knowledge from the diverse, disparate and distributed networks and empower organizations to make better, faster and well-informed decisions.