Using an Automation-First approach for cloud adoption has significant benefits. While enterprises love the concept, translating to a practical Automation Factory approach requires a proper blueprint.
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While helping our customers with the right way to use the cloud using an Automation-First approach, the primary focus from Relevance Lab is to enable significant automation (achieved 70%+ for large customers) of day-to-day tasks with benefits on the speed of delivery, quality improvements, and cost reduction. Large customers have complex organizational structures with different groups focussing on infrastructure automation, application deployment automation, and service delivery automation. In many cases, there is a missing common architecture in planning, building, and running a proper end-to-end automation program. To help enterprises adopt an Automation-First approach for cloud adoption covering all three aspects of infrastructure, applications, and service delivery, we help create a blueprint for an Automation Factory.
In this blog, we are sharing our approach for large customers with a complex landscape of infrastructure and applications. The focus of this blog is more on application deployment automation with custom and COTS (commercial off-the-shelf) products in Cloud.
Some of the most typical asks by customers with all their workloads in AWS Cloud is captured below:
Separation of roles between common infrastructure teams and multiple business units managing their own application needs
Infrastructure teams provide base AMI with CloudFormation stacks to provide basic OS-level compute workloads to application groups, who manage their own deployments
Application groups deal with a set of custom Java + .NET applications and COTS products, including Oracle Fusion Middleware stacks
Application groups manage the complete lifecycle of deployment and support in production environments
Application deployments are about 20% containerized and 80% direct installations in hybrid scenarios with legacy codebases
Different set of tools are used along with homegrown custom scripts
Primary pain points are to automate application and product (COTS) build and deploy lifecycle across different environments and upgrades
The solution is expected to leverage DevOps maturity and automation-led standardization for speed and flexibility
Key requirements from application groups are shared below based on the snapshot of products for which there is a need for automated installation and scalability at run-time. The shift needs to happen from “handcrafting” product installations to automated and easy deployment, preferably with immutable infrastructure.
COTS Products (High Priority)
COTS Products (Good to have)
Oracle E-Business Suite (Financial Portal)
Tomcat 7, 8, & 9
IBM Business Rules Engine
Oracle Siebel CRM
Microsoft SQL Server Reporting Service
Relevance Lab Approach for Hyperautomation with RLCatalyst and BOTs
Our teams have implemented 50+ engagements across customers and created a mature automation framework to help re-use and speed up the need for an Automation Factory using RLCatalyst BOTs and RLCatalyst Cloud Portals.
The figure below explains the RLCatalyst solutions for hyperautomation leveraging the Automation Service Bus (ASB) framework that allows easy integration with existing customer tools and cloud environments.
The key building block of automation depends on the concept of BOTs. So what are BOTs?
BOTs are automation codes managed by Automation Service Bus orchestration
Infrastructure creation, updation, deletion
Application deployment lifecycle
Operational services, tasks, and workflows – Check, Act, Sensors
Interacting with Cloud and On-prem systems with integration adapters in a secure and auditable manner
Targeting any repetitive Operations tasks managed by humans – frequently, complex (time-consuming), security/compliance related
What are types of BOTs?
Templates – CloudFormation, Terraform, Azure Resource Models, Service Catalog
Proposed Solution to Customers
There are different approaches to achieving end-to-end automation, and the right solution depends on a proper assessment of the context of customer needs. Relevance Lab follows a consultative approach that helps do a proper assessment of customer needs, priorities, and business goals to create the right foundation and suggest a maturity model for an Automation Factory. Also, different engagement models are offered to customers covering the entire phase of the Plan-Build-Run lifecycle of automation initiatives, including organization design and change management.
The following table helps plan the right approach and maturity model to be adopted for BOTs targeting different levels of complexity for automation.
Leveraging Relevance Lab Products and Solutions
Standard Cloud Resources Provisioning in a secure, multi-account covering compute, storage and data
EC2 Linux, EC2 Win, S3 Buckets, RDS, SageMaker, ALB, EMR, VPC, etc. with AWS Service Catalog
AWS Console and ITSM Portals
RLCatalyst Cloud Portal, BOTs Server
CI/CD Pipelines with BOTs APIs
Standard Applications deployment covering Middleware, Databases, Open Source Applications requiring single node setup. Single Node COTS setups can also be included though more complex
Tomcat, Apache, MySQL, NGINX – common Middleware and Database Stacks
Portal, CI/CD Pipeline, CLI
– Option-1 AMI Based (Preferred model for Immutable design)
– Option- 2 Docker Based
– Option- 3 Chef/Ansible Post Provision App Install & Configure (Mutable Design)
BUILD Phase – Covering Plan, Build, Test, Publish Lifecycle
CONSUME Phase – Production Deploy & Upgrade Lifecycle
Multi-tier Applications – 2-Tier, 3-Tier, N-Tier with Web + App + DB, etc. combinations
Required a combination of Infra, Apps, Post provision configurations, and orchestration. Complex Infra with ALB, PaaS Service Integrations
Orchestration engine and service discovery/registry
Docker and Kubernetes clusters
Complex Business Apps – ERP, Oracle EBS, COTS, HPC Clusters – not supporting standard Catalog Items.
Complex workflows with integration to multiple Third-Party systems
UI or System Driven
Custom Orchestration Flows and workflow modules
Event-driven and state management
Post provisioning complex integrations
Pre-built BOTs Library
Leveraging a combination of Relevance Lab products and solutions, we provide a mature Automation Factory blueprint to our customers, as shown below.
The above solution is built leveraging best practices from AWS Well-Architected framework and bringing in a combination of AWS tools and other third-party solutions like HashiCorp, Ansible, Docker, Kubernetes, etc. The key building blocks of the Automation Factory cover the following tools and concepts:
AWS AMI Builder Factory and Golden AMI concept
HashiCorp Packer Scripts
OS and Hardening with Ansible
Vulnerability Assessment and Patch Management
AWS Inspector, AWS Parameter Store, AMI Catalog publishing, Multi-Account AWS Best Practices
AWS Service Catalog, Multi-Account Governance, Master and Consumption accounts
Self-Service Cloud Portals with guard-rails and automated fulfilment
CI/CD Pipelines for non-user assisted workflows using RLCatalyst BOTs, Terraform Templates, Jenkins, Docker, and Kubernetes
Monitoring and diagnostics with Observability tools like RLCatalyst Command Center
Cloud Accounts, VPC Automation, AWS Control Tower, AWS Management, and Governance Lens Automation
The journey to adopting an Automation-First approach requires a strong foundation that our Automation Factory solution offers, saving at least 6 months of in-house efforts and about US$250K worth of savings for large customers annually. The BOTs deployed can scale up to provide productivity gains of 4-5 people full-time employees with other benefits of better fulfillment SLAs, quality, and compliance gains. In the case of COTS deployments, especially with Oracle stacks, our BOTs have reduced the time of deployments from a few weeks to a few hours.
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