Posted by An effective cloud migration journey requires more than typical monitoring and observability. With the meaning of observability and monitoring often misunderstood, it’s important to know the key differences. Monitoring is the practice of gathering, analyzing, and using data to track cloud transition’s progress toward reaching its objectives. In short, monitoring focuses on inspecting specific metrics.
However, modern cloud environments are dynamic, constantly varying in scale and complexity. This fails organizations to monitor or even identify most of the problems that emerge during cloud migration. Observability empowers businesses to understand or identify what is hindering cloud migration and what steps to follow for improving performance. With an AI-powered cloud migration solution in place, observability becomes easier for organizations. Teams can get real-time alerts when issues arise so they can effectively resolve them before they impact end-users. Observability is essential for ensuring a successful migration and establishing a well-architected cloud environment.
AI Cloud Migration Solutions – Observing More than Metrics, Logs, and Traces
As stated before, timely observability has become a vital component of modern cloud migration. But most monitoring tools and technology can’t provide the performance required to make that happen. That’s because these tools usually rely on metrics, traces, and logs gathered by businesses and deliver basic recommendations related to migration.
Whereas, AI cloud migration solutions rely on telemetry derived from the services and endpoints in cloud computing environments. In these environments, every hardware/software component, container, open-source tool, and microservices produces records of every activity. With these records, AI migration solutions understand what’s happening in the cloud infrastructure and generate feedback. Accordingly, business users can detect and resolve issues quickly and optimize cloud performance.
Mastering Observability: Key Challenges and How AI Migration Solutions Eliminates Them
Resource Consumption
Around 59% of IT leaders say traditional cloud migration solutions make assessing resource consumption and optimizing infrastructure performance difficult. Slow, labor-intensive manual efforts, combined with traditional cloud migration tools, waste precious IT time better spent on value-added activities like operations automation.
In contrast, AI-supported cloud migration tools improve observability capabilities and reduce blind spots across ever-expanding cloud environments. This is possible through a key functionality ‘Automatic Topology Mapping’ that constantly discovers and maps cloud components and services, and dynamically varying relationships between several interdependencies. This auto-mapping prevents inaccuracies during migration and supports easy analysis of resource consumption. The no-code approach of AI migration solutions frees up developers to focus on other priorities.
Real-Time Monitoring of Serverless Technologies and Containers
During migration, containers, like those managed in Kubernetes, and event-driven cloud environment introduces problems to scaling observability. With these technologies constantly spinning up and down during migration, businesses lack real-time visibility. Meaning that, IT teams can’t understand the relationships between upstream and downstream cloud components.
With AI cloud migration tools, businesses can simplify the discovery of containers — and understand every sub-component running within each container. This also helps in identifying the origin of anomalies in containers, making them easy to isolate in the early stage. The outcome is full-stack transparency of applications and services and a better understanding of containers and serverless component’s impact.
Assess Volume and Variety of Data Alerts
When migrating to multi-cloud environments, businesses might generate a massive amount of telemetry data. The result is data alert storms that overwhelm business users when they try to observe and make sense of every data point. Through manual assessment, users may not be able to effectively define and redefine constantly varying data alerts. And a manual approach won’t be helpful in identifying issues. As a result, multiple data alerts send mixed signals and multiply data silos while wasting precious time of IT workers.
AI-powered solutions deliver the critical capabilities required to achieve observability during on-premise to cloud migration. That includes automatic threshold baselining for categorizing data points and detecting anomalies. The tool intelligently groups associated anomalies into a single problem, thereby simplifying root cause analysis and minimizing redundant work across teams. Besides, this tool helps in processing multiple dependencies during migration through a fault-tree analysis approach and offers instant resolutions.
Minimizing Sprawl and Improving Collaboration
Many organizations think that data sprawl is inevitable during cloud migration. However, through appropriate monitoring, reporting, and analysis, businesses can overcome the sprawl. When businesses fail to address and eliminate sprawl. This makes teams work with disparate data models and isolated production environments. Consequently, bogging down cloud efficiency and innovation.
With AI cloud migration solutions, teams can collaborate more efficiently by having a single data model and balance observability across all layers of cloud environments. The tool gives business users a shared context that eases cross-team collaboration. It also lets users perform analytics across their infrastructure, operations, applications, and business data and effectively consolidate the entire lifecycle, from feature development to testing, security, and optimization.
Gain a Holistic Perspective of End-user Experience
The whole point of cloud migration is to help businesses drive agility. However, customers, partners, and employees are real success drivers of an organization. When the applications these end-users rely on don’t meet their usability and performance expectations, it impacts productivity at large. What’s required is an intelligent observability methodology that seamlessly connects digital experiences hosted via cloud infrastructure.
AI cloud solutions gives businesses comprehensive insights into a workload’s impact on the delivered digital experiences and performance indicators like revenue, feature adoption, and conversions. That observability and monitoring across different platforms help organizations gain a wide understanding of the end-user (customers, partners, and employees) experience.
Also Explore: How Cloud Consulting Solutions Providers Drive a Culture of Transformation
Five Ways AI Cloud Solutions Empowers Business Agility
- Minimizes Guesswork on Cloud Resource Requirements – AI technology helps businesses quickly scale up and down cloud resources during and after migration automatically, reducing wasted cloud capacity.
- Simulate Post-migration Scenarios – With AI migration tools, IT teams can cost-effectively simulate their post-transition cloud environment on demand.
- Automates Tasks to Make Cloud Experimentation Easier – With the help of a recognized cloud migration services provider, organizations can leverage the right AI tool and use automation to create and replicate cloud workloads while reducing costs for experimenting and optimization efforts.
- Ensures Support for Various Cloud Architectures – The AI cloud migration tools help in testing cloud infrastructure (multi or hybrid) migration scenarios. This reduces the risks of impacts from architecture changes and can take advantage of innovations.
- Collects Data to Implement Architectural Enhancements – By collecting data on how architectural changes influence the behavior of cloud workload, AI migration tools can inform business upgrades and make continuous improvements over time.
Wrapping Up
Using AI cloud migration solutions is an effective way to achieve cloud success. These solutions strengthen the supportive pillars of cloud migration efforts by offering cloud infrastructure intelligence and end-to-end observability, all within a single environment. Businesses can also eliminate siloed scenarios and embrace an efficient way to optimize cloud performance and costs.