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2021 Blog, Blog, Feature Blog, Featured

Many AWS customers either integrate ServiceNow into their existing AWS services or set up both ServiceNow and AWS services for simultaneous use. Customers need a near real-time view of their infrastructure and applications spread across their distributed accounts.

Commonly referred to as the “Dynamic Application Configuration Management Database (CMDB) or Dynamic Assets” view, it allows customers to gain integrated visibility into their infrastructures to break down silos and facilitate better decision making. From an end-user perspective as well, there is a need for an “Application Centric” view rather than an “Infrastructure/Assets” view as better visibility ultimately enhances their experience.

An “Application Centric” View provides the following insights.

  • Application master for the enterprise
  • Application linked infrastructure currently deployed and in use
  • Cost allocation at application levels (useful for chargebacks)
  • Current health, issues, and vulnerability with application context for better management
  • Better aligned with existing enterprise context of business units, projects, costs codes for budget planning and tracking

Use Case benefits for ServiceNow customers
Near real-time view of AWS applications & Infrastructure workloads across multiple AWS accounts in ServiceNow. Customer is enabling self-service for their Managed Service Provider (MSP) and their Developers to:

  • Maintain established ITSM policies & processes
  • Enforce Consistency
  • Ensure Compliance
  • Ensure Security
  • Eliminate IAM access to underlying services

Use Case benefits for AWS customers
Enabling application self-service for general & technical Users. The customer would like service owners (e.g. HR, Finance, Security & Facilities) to view AWS infrastructure-enabled applications via self-service while ensuring:

  • Compliance
  • Security
  • Reduce application onboarding time
  • Optical consistency across all businesses

RLCatalyst SmartView Solution – Built on AppRegistry
Working closely with AWS partnership groups in addressing the key needs of customers, RLCatalyst SmartView Solution provides a “Dynamic CMDB” solution that is Application Centric with the following highlights:

  • Built on “AWS AppRegistry” and tightly integrated with AWS products
  • Combines information from the following Data Sources:
    • AWS AppRegistry
    • AWS Accounts
      • Design time Data (Definitions – Resources, Templates, Costs, Health, etc.)
      • Run time Data (Dynamic Information – Resources, Templates, Costs, Health, etc.)
    • SmartView Additional Functionality
      • Service Registry Insights
      • Aggregated Data (Lake) with Dynamic CMDB/Asset View
      • UI Interaction Engine with appropriate backend logic


A well-defined Dynamic Application CMDB is mandatory in cloud infrastructure to track assets effectively and serves as the basis for effective Governance360.

AWS recently released a new feature called AppRegistry to help customers natively build an AWS resources inventory that has insights into uses across applications. AWS Service Catalog AppRegistry allows creating a repository of your applications and associated resources. Customers can define and manage their application metadata. This allows understanding the context of their applications and resources across their environments. These capabilities enable enterprise stakeholders to obtain the information they require for informed strategic and tactical decisions about cloud resources. Using AppRegisty as a base product, we have created a Dynamic Application CMDB solution SmartView to benefit AWS and ServiceNow customers as explained in the figure below.



Modeling a common customer use case
Most customers have multiple applications deployed in different regions constituting sub-applications, underlying web services, and related infrastructure as explained in the figure below. The dynamic nature of cloud assets and automated provisioning with Infrastructure as a Code makes the discovery process and keeping CMDB up to date a non-trivial problem.



As explained above, a typical customer setup would consist of different business units deploying applications in different market regions across a complex and hybrid infrastructure. Most existing CMDB applications provide a static assets view that is incomplete and not well aligned to growing needs for real-time application-centric analysis, costs allocation, and application health insights. This problem has been solved by the SmartView solution leveraging existing investments of customers on ITSM licenses of ServiceNow and pre-existing solutions from AWS like ServiceManagement connector that are available for no additional costs. The missing piece till recently was an Application-centric meta data linking applications to infrastructure templates.

Customers need to be able to see the information across their AWS accounts with details of Application, Infrastructure, and Costs in a simple and elegant manner, as shown below. The basic KPIs tracked in the dashboard are following:

  • Dashboard per AWS Account provided (later aggregated information across accounts to be also added)
  • Ability to track an Application View with Active Application Instances, AWS Active Resources and Associated Costs
  • Trend Charts for Application, Infrastructure and Cost Details
  • Drill-down ability to view all applications and associated active instances what are updated dynamically using a period sync option or on-demand use based

The ability to get a Dynamic Application CMDB is possible by leveraging the AWS Well Architected best practices of “Infrastructure as a Code” relying on AWS Service Catalog, AWS Service Management Connector, AWS CloudFormation Templates, AWS Costs & Budgets, AWS AppRegistry. The application is built as a scoped application inside ServiceNow and leverages the standard ITSM licenses making it easy for customers to adopt and share this solution to business users without the need for having AWS Console access.



Workflow steps for adoption of RLCatalyst SmartView are explained below. The solution provided is based on standard AWS and ServiceNow products commonly use in enterprises and build on existing best practices, processes and collaboration models.


Step-1 Define AppRegistry Data Use AppRegistry
Step-2 Link App to Infra Templates – CloudFormation Template (CFT) / Service Catalog (SC) AWS Accounts Asset Definitions
Step-3 Ensure all Assets Provisioned have App and Service Tagging (Enforce with Guard Rails) AWS Accounts Asset Runtime Data
Step-4 Register Application Services – Service Registry Service Registry
Step-5 SmartView Data Lake refresh with static and dynamic Updates (Aggregated across accounts) RLCatalyst SmartView
Step-6 Asset, Cost, Health Dashboard RLCatalyst SmartView


A typical implementation of RLCatalyst SmartView can be rolled out for a new customer in 4-6 weeks and can provide significant business benefits for multiple groups enabling better Operations support, Self-service requests, application specific diagnostics, asset usage and cost management. The base solution is built on a flexible architecture allowing for more advanced customization to extend with real time health and vulnerability mappings and achieve AIOps maturity. In future there are plans to extend the Application Centric views to cover more granular “Services” tracking for support of Microservice architectures, container based deployments and integration with other PAAS/SAAS based Service integrations.

Summary
Cloud-based dynamic assets create great flexibility but add complexity for near real-time asset and CMDB tracking. While existing solutions using Discovery tools and Service Management connectors provided a partial solution to an Infrastructure centric view of CMDB, a robust Application Centric Dynamic CMDB was a missing solution that is now addressed with RLCatalyst SmartView built on AppRegistry as explained in the above blog.

For more information, feel free to contact marketing@relevancelab.com

References
Governance360 – Are you using your AWS Cloud “The Right Way”
ServiceNow CMDB
Increase application visibility and governance using AWS Service Catalog AppRegistry
AWS Security Governance for Enterprises “The Right Way”
Configuration Management in Cloud Environments



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2021 Blog, Blog, Feature Blog, Featured

In large enterprises with complex systems, covering new generation cloud-based platforms while continuing with stable legacy back-end infrastructure usually results in high-friction points at integration layers. These incompatible systems can also slow down enterprise automation efforts to free up humans and have BOTs take over repetitive tasks. Now with RLCatalyst BOTs Server and leveraging common platforms of ServiceNow and UiPath, we provide an intelligent and scalable solution that can also cover legacy infrastructure like AS/400 with terminal interfaces. These applications are commonly found in the supply chain, logistics, warehousing enterprise domains supporting needs for temporary/flexi-staff onboarding & offboarding needs based on the volume of transactions in industries that see spikes in demand across special events powering the need for more automation first solutions.

Integration of a cloud-based ticketing system with a terminal-based system would always require a support engineer, especially with labor-intensive industries. This is true for any legacy system that does not provide an external API for integration. There are diverse issues that occur slowing down business without compromising on security and governance aspects related to such workflows.

With the lack of a stable API system to interface with the AS/400 legacy system, we decided to rely on BOTs simulating the same User behavior as humans dealing with terminal interfaces. RLCatalyst BOTs was extensively used as an IT Automation solutioning platform for ServiceNow, and the same concept was extended to interact with Terminal interfaces commonly used in Robotic Process Automation (RPA) use cases with UiPath. RLCatalyst acts as in “Automation Service Bus” and manages the integration between ServiceNow ITSM platform and UiPath Terminal Interface engine. The solution is extendable and can be used to solve other common problems especially bringing integration between IT and business systems.



Using UiPath to automate processes in legacy systems
Leveraging the capabilities of UiPath to automate terminal-based legacy systems, RLCatalyst interfaces with the service portal to get all the required information to help UiPath’s UiRobot to execute steps defined in the workflow. RLCatalyst’s BOT framework would provide the necessary tools to run/schedule BOT’s with governance, audit trails functionality.

Case Study – Onboarding an AS400 system user
The legacy AS400 system’s user onboarding process used to be multi-staged, with each stage representing a server with its ACL tables. A common profile name would be the link between the servers and, in some cases, independent logins required. A process definition document is the only governing document that helps a CS executive complete the onboarding process.

The design used to automate the process was:

  • Build individual workflows for each stage in the User interaction processes using UiPath.
  • Build an RLCatalyst BOT which:
    • Refers to a template that includes a reference to the stages to run based on the type of user that needs to be onboarded.
    • Based on the template, it would maintain a document of profile names allotted.
    • Validates the profile availability (this helps onboarding outside the automation)
    • Executes the UiPath workflow for each stage in the sequence defined in the template.
    • Once the execution is complete, a summary of the execution with user login details is sent back to the ITSM system.
    • Logs for each stage are maintained for analysis and error corrections.
  • Build the Service Portal approval workflow, which would finally create a task for the automation process for fulfillment.
    • The service portal form captures all the necessary information for onboarding a new user.
    • Based on the account template selected that depicts a work department, a template reference is captured and included in the submitted form.
    • The service portal is used by the SOX compliance team to trace approval and provisioning details.
    • The process trail becomes critical during off-boarding to confirm, access revocation has occurred without delay.

Advantages of Using UI Automation Over Standard API

  • Some of the AS400 servers used for Invoicing/Billing are over 20 years old, and the processes are as old as the servers themselves. The challenge multiplies when the application code is only understood by a small set of IT professionals.
  • UI automation eliminates system testing costs, since it just mimics a user. All user flows would have already been tested.
  • The time taken to build an end-to-end automation would be significantly lesser compared to getting a highly demanding IT professional building the API interface to get it automated.
  • Total automation investment would also significantly be reduced, and ROI’s would be quicker.

Getting Started
Based on our previous experience in integrating and automating processes, our pre-built libraries and BOTs should provide a head start to your automation needs. The framework would ensure that it meets all the necessary security and compliance needs.

For more details, please feel free to reach out to marketing@relevancelab.com.



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2021 Blog, Blog, Feature Blog, Featured

Major advances are happening with the leverage of Cloud Technologies and large Open Data sets in the areas of Healthcare informatics that includes sub-disciplines like Bioinformatics and Clinical Informatics; rapidly being adopted by Life Sciences and Healthcare institutions in commercial and public sector space. This domain has deep investments in scientific research and data analytics focusing on information, computation needs, and data acquisition techniques to optimize the acquisition, storage, retrieval, obfuscation, and secure use of information in health and biomedicine for evidence-based medicine and disease management.

In recent years, genomics and genetic data have emerged as an innovative area of research that could potentially transform healthcare. The emerging trends are for personalized medicine, or precision medicine leveraging genomics. Early diagnosis of a disease can significantly increase the chances of successful treatment, and genomics can detect disease long before symptoms present themselves. Many diseases, including cancers, are caused by alterations in our genes. Genomics can identify these alterations and search for them using an ever-growing number of genetic tests.

With AWS, genomics customers can dedicate more time and resources to science, speeding time to insights, achieving breakthrough research faster, and bringing life-saving products to market. AWS enables customers to innovate by making genomics data more accessible and useful. AWS delivers the breadth and depth of services to reduce the time between sequencing and interpretation, with secure and frictionless collaboration capabilities across multi-modal datasets. Plus, you can choose the right tool for the job to get the best cost and performance at a global scale— accelerating the modern study of genomics.

Relevance Lab Research@Scale Architecture Blueprint
Working closely with AWS Healthcare and Clinical Informatics teams, Relevance Lab is bringing a scalable, secure, and compliant solution for enterprises to pursue Research@Scale on Cloud for intramural and extramural needs. The diagram below shows the architecture blueprint for Research@Scale. The solution offered on the AWS platform covers technology, solutions, and integrated services to help large enterprises manage research across global locations.


Leveraging AWS Biotech Blueprint with our RLCatalyst Research Gateway
Use case with AWS Biotech Blueprint that provides a Core template for deploying a preclinical, cloud-based research infrastructure and optional informatics software on AWS.

This Quick Start sets up the following:

  • A highly available architecture that spans two availability zones
  • A preclinical virtual private cloud (VPC) configured with public and private subnets according to AWS best practices to provide you with your own virtual network on AWS. This is where informatics and research applications will run
  • A management VPC configured with public and private subnets to support the future addition of IT-centric workloads such as active directory, security appliances, and virtual desktop interfaces
  • Redundant, managed NAT gateways to allow outbound internet access for resources in the private subnets
  • Certificate-based virtual private network (VPN) services through the use of AWS Client VPN endpoints
  • Private, split-horizon Domain Name System (DNS) with Amazon Route 53
  • Best-practice AWS Identity and Access Management (IAM) groups and policies based on the separation of duties, designed to follow the U.S. National Institute of Standards and Technology (NIST) guidelines
  • A set of automated checks and alerts to notify you when AWS Config detects insecure configurations
  • Account-level logging, audit, and storage mechanisms are designed to follow NIST guidelines
  • A secure way to remotely join the preclinical VPC network by using the AWS Client VPN endpoint
  • A prepopulated set of AWS Systems Manager Parameter Store key/value pairs for common resource IDs
  • (Optional) An AWS Service Catalog portfolio of common informatics software that can be easily deployed into your preclinical VPC

Using the Quickstart templates, the products were added to AWS Service Catalog and imported into RLCatalyst Research Gateway.



Using the standard products, the Nextflow Workflow Orchestration engine was launched for Genomics pipeline analysis. Nextflow helps to create and orchestrate analysis workflows and AWS Batch to run the workflow processes.

Nextflow is an open-source workflow framework and domain-specific language (DSL) for Linux, developed by the Comparative Bioinformatics group at the Barcelona Centre for Genomic Regulation (CRG). The tool enables you to create complex, data-intensive workflow pipeline scripts and simplifies the implementation and deployment of genomics analysis workflows in the cloud.

This Quick Start sets up the following environment in a preclinical VPC:

  • In the public subnet, an optional Jupyter notebook in Amazon SageMaker is integrated with an AWS Batch environment.
  • In the private application subnets, an AWS Batch compute environment for managing Nextflow job definitions and queues and for running Nextflow jobs. AWS Batch containers have Nextflow installed and configured in an Auto Scaling group.
  • Because there are no databases required for Nextflow, this Quick Start does not deploy anything into the private database (DB) subnets created by the Biotech Blueprint core Quick Start.
  • An Amazon Simple Storage Service (Amazon S3) bucket to store your Nextflow workflow scripts, input and output files, and working directory.

RStudio for Scientific Research
RStudio is a popular IDE, licensed either commercially or under AGPLv3, for working with R. RStudio is available in a desktop version or a server version that allows you to access R via a web browser.

After you’ve analyzed your results, you may want to visualize them. Shiny is a great R package, licensed either commercially or under AGPLv3, that you can use to create interactive dashboards. Shiny provides a web application framework for R. It turns your analyses into interactive web applications; no HTML, CSS, or JavaScript knowledge is required. Shiny Server can deliver your R visualization to your customers via a web browser and execute R functions, including database queries, in the background.

RStudio is provided as a standard catalog item in RLCatalyst Research Gateway for 1-Click deployment and use. AWS provides a number of tools like AWS Athena, AWG Glue, and others to connect to datasets for research analysis.

Benefits of using AWS for Clinical Informatics

  • Data transfer and storage
  • The volume of genomics data poses challenges for transferring it from sequencers in a quick and controlled fashion, then finding storage resources that can accommodate the scale and performance at a price that is not cost-prohibitive. AWS enables researchers to manage large-scale data that has outpaced the capacity of on-premises infrastructure. By transferring data to the AWS Cloud, organizations can take advantage of high-throughput data ingestion, cost-effective storage options, secure access, and efficient searching to propel genomics research forward.

  • Workflow automation for secondary analysis
  • Genomics organizations can struggle with tracking the origins of data when performing secondary analyses and running reproducible and scalable workflows while minimizing IT overhead. AWS offers services for scalable, cost-effective data analysis and simplified orchestration for running and automating parallelizable workflows. Options for automating workflows enable reproducible research or clinical applications, while AWS native, partner (NVIDIA and DRAGEN), and open source solutions (Cromwell and Nextflow) provide flexible options for workflow orchestrators to help scale data analysis.

  • Data aggregation and governance
  • Successful genomics research and interpretation often depend on multiple, diverse, multi-modal datasets from large populations. AWS enables organizations to harmonize multi-omic datasets and govern robust data access controls and permissions across a global infrastructure to maintain data integrity as research involves more collaborators and stakeholders. AWS simplifies the ability to store, query, and analyze genomics data, and link with clinical information.

  • Interpretation and deep learning for tertiary analysis
  • Analysis requires integrated multi-modal datasets and knowledge bases, intensive computational power, big data analytics, and machine learning at scale, which, historically can take weeks or months, delaying time to insights. AWS accelerates the analysis of big genomics data by leveraging machine learning and high-performance computing. With AWS, researchers have access to greater computing efficiencies at scale, reproducible data processing, data integration capabilities to pull in multi-modal datasets, and public data for clinical annotation—all within a compliance-ready environment.

  • Clinical applications
  • Several hindrances impede the scale and adoption of genomics for clinical applications that include the speed of analysis, managing protected health information (PHI), and providing reproducible and interpretable results. By leveraging the capabilities of the AWS Cloud, organizations can establish a differentiated capability in genomics to advance their applications in precision medicine and patient practice. AWS services enable the use of genomics in the clinic by providing the data capture, compute, and storage capabilities needed to empower the modernized clinical lab to decrease the time to results, all while adhering to the most stringent patient privacy regulations.

  • Open datasets
  • As more life science researchers move to the cloud and develop cloud-native workflows, they bring reference datasets with them, often in their own personal buckets, leading to duplication, silos, and poor version documentation of commonly used datasets. The AWS Open Data Program (ODP) helps democratize data access by making it readily available in Amazon S3, providing the research community with a single documented source of truth. This increases study reproducibility, stimulates community collaboration, and reduces data duplication. The ODP also covers the cost of Amazon S3 storage, egress, and cross-region transfer for accepted datasets.

  • Cost optimization
  • Researchers utilize massive genomics datasets that require large-scale storage options and powerful computational processing, which can be cost-prohibitive. AWS presents cost-saving opportunities for genomics researchers across the data lifecycle—from storage to interpretation. AWS infrastructure and data services enable organizations to save time, money and devote more resources to science.

Summary
Relevance Lab is a specialist AWS partner working closely in Health Informatics and Genomics solutions leveraging AWS existing solutions and complementing it with its Self-Service Cloud Portal solutions, automation, and governance best practices.

To know more about how we can help standardize, scale, and speed up Scientific Research in Cloud, feel free to contact us at marketing@relevancelab.com.

References
AWS Whitepaper on Genomics Data Transfer, Analytics and Machine Learning
Genomics Workflows on AWS
HPC on AWS Video – Running Genomics Workflows with Nextflow
Workflow Orchestration with Nextflow on AWS Cloud
Biotech Blueprint on AWS Cloud
Running R on AWS
Advanced Bioinformatics Workshop



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