As we covered in this previous blog on ITSMA, our latest release of Micro Focus ITSMA 2017.07 release enables our customers and partners to deploy the suite on Amazon Web Services (AWS). In this blog, I will do a deep dive into the details of this deployment on AWS including architecture and installation, I will also explore some concepts like using Auto scaling groups, and I will provide a high level summary of the current roadmap.
The latest ITSMA suite release leverages our new Docker container and Kubernetes-based architecture. Docker provides a Container runtime environment, and Kubernetes provides Container orchestration and scaling for the suite. More details on this architecture can be found here.
ITSMA suite on AWS uses many native AWS services like Amazon EC2, EBS, EFS, RDS (for both PostgreSQL and Oracle DB), IAM, AWS VPC and Route 53 for DNS. The usage of these native AWS services has allowed us to support a highly-scalable, performance architecture which can support hundreds of thousands of users to the suite. In addition, we also support multiple Kubernetes master and worker nodes to provide a highly available (HA) architecture. This support allows us to fail-over to a working set of nodes in case of an unplanned/planned outage with the master or worker nodes. In addition, for certain on-premises integrations like LDAP, we also enable this AWS VPC in the public cloud to be integrated to a customer’s on-premises infrastructure using VPN. Below is an architecture diagram of our suite deployed on AWS.
The suite deployment on AWS is seamless since we provide a set of Packer and Terraform scripts that automate the installation of the CDF on a cluster of nodes. Packer scripts automate the creation of machine images, which are used to create the Amazon machine image (AMI) instances. Terraform eases the deployment of the CDF and ITSMA suite images on AWS by provisioning the required infrastructure based on pre-configured set of parameters. Then the infrastructure is deployed on the CDF platform, followed by the ITSMA suite in an automated fashion. More information on this cloud-based deployment of the suite is available here.
In the future, we are planning to enhance this deployment process to use AWS CloudFormation templates which will further simplify this deployment process of the suite on AWS.
Roadmap: We have a very robust roadmap to continue to enhance the capabilities of the suite deployment on public clouds, including AWS. Some of the things that we are planning to accomplish are:
Using AWS auto-scaling group: Customers want to be able to scale-up and scale-down based on demand from their end users. These shifts in demand could be a result of seasonality, or even sudden unexpected events. Our suite will allow customers to configure AWS auto-scaling groups at both the node-level and the pod-level, which will adjust (either increase or decrease) the number of nodes in case of changes to user traffic. This will be based on the monitoring of both parameters: CPU and memory usage of the nodes. Internally, the pods are separated into two groups: State-full or non-scalable pods. The non-scalable pods are deployed to fixed worker nodes, not in auto-scaling groups. Scalable stateless pods are deployed to dynamic worker nodes managed by auto-scaling group. (Note: This feature may be disabled by customers if needed.)
ITSMA suite on AWS marketplace: AWS Marketplace is an online store which helps customers to find, buy and use software and services on AWS. With ITSMA suite availability on AWS marketplace, customers can leverage “single-click” installation of the suite on AWS, and start the suite usage for their end users. We are also looking at numerous licensing options including Bring Your Own License (BYOL), trial licenses, SaaS model and a paid model on AWS.
ITSMA on Azure and Google Cloud Platform (GCP): Azure and GCP are rapidly becoming the cloud platforms of choice for customers. We intend to expand the capability of public cloud deployments of the suite to Azure and GCP—which would include automated deployments, using native cloud services, and also using cloud marketplaces for both Azure and GCP.