Assess and Analyze
- Capture metadata of your applications and infrastructure
- Analyze dependencies and risks
- Workload analysis and migration recommendations
Businesses listening to needs of the customer start assessing ways to improve their performance and being more agile. Most often it is the dictat from top management to identify the bottleneck(s). The effort to identify bottlenecks / gaps is more manual (e.g. interviewing stakeholders, etc) and time consuming.
- non-intrusively captures application(s)’ metadata within a day. It is an automated process. All we need is a read-only SSH access to the node which needs to be transformed to Cloud
- analyzes dependencies (including workloads) and provides recommendation(s)
- intelligently recommends ways to transform an application. Recommendations are based on multiple parameters and past learnings
- finishes deep analysis within minutes!
Based on the initial findings and recommendations, our customers’ confidence increases in the product, this allows us to probe nuances of the application(s). We recommend running a regression test cycle to capture more data which is useful while containerizing an application.
This has turned out to be a very useful tool, for our customers, during a scoping exercise. Analyzing application(s), its workloads, and dependencies enable Enterprises to identify which application(s) and processes can/ cannot be moved to a cloud environment.
Refactor and Migrate
- Automated process of containerizing user services
- Map local environment / variables
- Lift and Shift Applications or Nodes to Cloud
To seek maximum benefits of Cloud infrastructure, an application(s) needs to be refactored before deploying onto Cloud. Ideally, an application needs to follow the “12 factors app methodology” to be labelled Cloud-native. Refactoring an application and/or its underlying services can be done in two ways: Code refactoring and Application services refactoring.
- automates the process of containerizing application services, enables tweaking of Docker files before converting them to Docker images
- enables Lift and Shift (or Rehost) of applications to Cloud by an easy to use step-by-step process (wizard driven)
- moves Nodes to Cloud via a guided process
CloudHedge has automated refactoring of an application’s underlying services. Containerizing set of user created services (different from system services) is done in minutes. For complex environments it may need tweaking a Docker file, and we enable that feature as part of our Transform tool.
Secure and Deploy
- Establish a secured application blueprint
- Deploy refactored application via an existing deployment pipeline/ method
- Speed up and simplify provisioning and configuration systems
To deploy a refactored application we need a secured blueprint to operate and scale an application deployed on cloud. The application needs to connect with cloud’s underlying elements (e.g. cache, storage, network, bus, management, etc). Moving application(s) to Cloud helps in eliminating servicing and support of physical infrastructure.
- allows application deployment in a Hybrid / Private / Public cloud environments
- enables you to set up Infrastructure as Code definitions
- enables optimization of CI/ CD pipeline
However, managing and provisioning/ deprovisioning needs to be a standardized process (Infrastructure as Code (IaC)). Based on IaC definitions, Cloud dynamically provisions and deprovisions environments.
Also to avoid getting tied to a specific Cloud provider, Enterprises are looking to move their application(s) from one Cloud to another.
Optimize and Monitor
- Right size resources and optimize (nodes, compute, storage, etc)
- Intelligent Operations dashboard
- Integrated workflow for infrastructure management
Cloud provides flexibility and agility, however, if the resources are not optimized on an ongoing basis we lose the cost savings advantage. Cloud environments being distributed and heterogeneous, the parameters (e.g. nodes, compute, storage, etc) need to be right sized on regular intervals.
- compares real-time performance with dynamically calculated baselines and receive alters to deviations from expected service levels (AI driven proactive monitoring)
This also links to performance of the application(s). It becomes difficult to pinpoint the cause of a problem, hence proactive monitoring and its solutions need to planned.