Your address will show here +12 34 56 78
2023 Blog, SWB Blog, Blog, Featured, Feature Blog

While there is rapid momentum for every enterprise in the world in consuming more Cloud Assets and Services, there is still lack of maturity in adopting an “Automation-First” approach to establish Self-Service models for Cloud consumptions due to fear of uncontrolled costs, security & governance risks and lack of standardized Service Catalogs of pre-approved Assets & Service Requests from Central IT groups. Lack of delegation and self-service has a direct impact on speed of innovation and productivity with higher operations costs.

Working closely with AWS Partnership we have now created a flexible platform for driving faster adoption of Self-Service Cloud Portals. The primary needs for such a Self-Service Cloud Portal are the following.

  • Adherence to Enterprise IT Standards
    • Common architecture
    • Governance and Cost Management
    • Deployment and license management
    • Identity and access management
  • Common Integration Architecture with existing platforms on ITSM and Cloud
    • Support for ServiceNow, Jira, Freshservice and Standard Cloud platforms like AWS
  • Ability to add specific custom functionality in the context of Enterprise Business needs
    • The flexibility to add business specific functionality is key to unlocking the power of self-service models outside the standard interfaces already provided by ITSM and Cloud platforms

A common way of identifying the need for a Self-Service Cloud portal is based on following needs.

  • Does your enterprise already have any Self-Service Portals?
  • Do you have a large user base internally or with external users requiring access to Cloud resources?
  • Does your internal IT have the bandwidth and expertise to manage current workloads without impacting end user response time expectations?
  • Does your enterprise have a proper security governance model for Cloud management?
  • Are there significant productivity gains by empowering end users with Self-Service models?

Working with AWS partnership and with our existing customer we see a growing need for Self-Service Cloud Portals in 2020 predominantly centred around two models.

  • Enterprises with existing ITSM investments and need to leverage that for extending to Cloud Management
  • Enterprises extending needs outside enterprise users with custom Cloud Portals

The roadmap to Self-Service Cloud portals is specific to every enterprise needs and needs to leverage the existing adoption and maturity of Cloud and ITSM platforms as explained below. With Relevance Lab RLCatalyst products we help enterprises achieve the maturity in a cost effective and expedited manner.


Examples of Self-Service Cloud Portals



Standard Needs Platform Benefits
Look-n-Feel of Modern Self-Service Portals Professional and responsive UI Design with multiple themes available, customizations allowed
Standards based Architecture & Governance Tightly Built On AWS products and AWS Well Architected with pre-built Reference Architecture based Products
Pre-built Minimum Viable Product Needs 80-20 Model – Pre-built vs Customizations based on key components of core functionality
Proprietary vs Open Source? Open-source foundation with source code made available built on MEAN Stack
Access Control, Security and Governance Standard Options Pre-built, easy extensions (SAML Based). Deployed with enterprise grade security and compliances
Rich Standard Pre-Build Catalog of Assets and Services Comes pre-built with 100+ catalog items covering all standard Asset and Services needs catering to 50% of any enterprise infrastructure, applications and service delivery needs


Explained below is a sample AWS Self-Service Cloud for driving Scientific Research.



Getting started
To make is easier for enterprises for experiencing the power of Self-Service Cloud Portals we are offering two options based on enterprise needs.

  • Hosted SAAS offering of using our Multi-tenant Cloud Portal with ability to connect to your existing Cloud Accounts and Service Catalogs
  • Self-Hosted RLCatalyst Cloud Portal product with option to engage us for professional services on customizations, training, initial setup & onboarding needs

Pricing for the SAAS offering is based on user based monthly subscription while for self-hosting model an enterprise support model pricing is available for the open source solution that allows enterprises the flexibility to use this solution without proprietary lock-ins.

The typical steps to get started are very simple covering the following.

  • Setup an organization and business units or projects aligned with your Cloud Accounts for easy billing and access control tracking
  • Setup users and roles
  • Setup Budgets and controls
  • Setup standard catalog of items for users to order
  • With the above enterprises are up to speed to use Self-Service Cloud Portals in less than 1-Day with inbuilt controls for tracking and compliance

Summary
Cloud Portals for Self-Service is a growing need in 2020 and we see the momentum continuing for next year as well. Different market segments have different needs for Self-Service Cloud portals as explained in this Blog.


  • Scientific Research community is interested in a Research Gateway Solution
  • University IT looks for a University in a Box Self Service Cloud
  • Enterprises using ServiceNow want to extend the internal Self Service Portals
  • Enterprises are also developing Hybrid Cloud Orchestration Portals
  • Enterprises looking at building AIOps Portal needs monitoring, automation and service management
  • Enabling Virtual Training Labs with User and Workspace onboarding
  • Building an integrated Command Centre requires an Intelligent Monitoring portal
  • Enterprise Intelligent Automation Portal with ServiceNow Connector

We provide pre-build solutions for Self-Service Cloud Portals and a base platform that can be easily extended to add new functionality for customization and integration. A number of large enterprises and universities are leveraging our Self Service Cloud portal solutions using both existing ITSM tools (Servicenow, Jira, Freshservice) and RLCatalyst products.

To learn more about using AWS Cloud or ITSM solutions for Self-Service Cloud portals contact marketing@relevancelab.com



0

2023 Blog, AWS Platform, Blog, Featured, Feature Blog

Relevance Lab (RL) is a specialist company in helping customers adopt cloud “The Right Way” by focusing on an “Automation-First” and DevOps strategy. It covers the full lifecycle of migration, governance, security, monitoring, ITSM integration, app modernization, and DevOps maturity. Leveraging a combination of services and products for cloud adoption, we help customers on a “Plan-Build-Run” transformation that drives greater velocity of product innovation, global deployment scale, and cost optimization for new generation technology (SaaS) and enterprise companies.

In this blog, we will cover some common themes that we have been using to help our customers for cloud adoption as part of their maturity journey.


  • SaaS with multi-tenant architecture
  • Multi-Account Cloud Management for AWS
  • Microservices architecture with Docker and Kubernetes (AWS EKS)
  • Jenkins for CI/CD pipelines and focus on cloud agnostic tools
  • AWS Control Tower for Cloud Management & Governance solution (policy, security & governance)
  • DevOps maturity models
  • Cost optimization, agility, and automation needs
  • Standardization for M&A (Merger & Acquisitions) integrations and scale with multiple cloud provider management
  • Spectrum of AWS governance for optimum utilization, robust security, and reduction of budget
  • Automation/BOT landscape, how different strategies are appropriate at different levels, and the industry best practice adoption for the same
  • Reference enterprise strategy for structuring DevOps for engineering environment which has cloud native development and the products which are SaaS-based.

Relevance Lab Cloud and DevOps Credentials at a Glance

  • RL has been a cloud, DevOps, and automation specialist since inception in 2011 (10+ years)
  • Implemented 50+ successful customer cloud projects covering Plan-Build-Run lifecycle
  • Globally has 250+ cloud specialists with 100+ certifications
  • Cloud competencies cover infra, apps, data, and consulting
  • Provide deep consulting and technology in cloud and DevOps
  • RL products available on AWS and ServiceNow marketplace recognized globally as a specialist in “Infrastructure Automation”
  • Deep Architecture know-how on DevOps with microservices, containers, Well-Architected principals
  • Large enterprise customers with 10+M$ multi-year engagements successfully managed
  • Actively managing 7000+ cloud instances, 300+ Applications, annual 5.0+M$ cloud consumption, 20K+ annual tickets, 100+ automation BOTs, etc.

Need for a Comprehensive Approach to Cloud Adoption
Most enterprises today have their applications in the cloud or are aggressively migrating new ones for achieving the digital transformation of their business. However, the approach requires customers to think about the “Day-After” Cloud in order to avoid surprises on costs, security, and additional operations complexities. Having the right Cloud Management not only helps eliminate unwanted costs and compliance, but it also helps in optimal use of resources, ensuring “The Right Way” to use the cloud. Our “Automation- First Approach” helps minimize the manual intervention thereby, reducing manual prone errors and costs.

RL’s matured DevOps framework helps in ensuring the application development is done with accuracy, agility, and scale. Finally, to ensure this whole framework of Cloud Management, Automation and DevOps are continued in a seamless manner, you would need the right AIOps-driven Service Delivery Model. Hence, for any matured organizations, the below 4 themes become the foundation for using Cloud Management, Automation, DevOps, and AIOps.


Cloud Management
RL offers a unique methodology covering Plan-Build-Run lifecycle for Cloud Management, as explained in the diagram below.


Following are the basic steps for Cloud Management:

Step-1: Leverage
Built on best practices offered from native cloud providers and popular solution frameworks, RL methodology leverages the following for Cloud Management:

  • AWS Well-Architected Framework
  • AWS Management & Governance Lens
  • AWS Control Tower for large scale multi-account management
  • AWS Service Catalog for template-driven organization standard product deployments
  • Terraform for Infra as Code automation
  • AWS CloudFormation Templates
  • AWS Security Hub

Step-2: Augment
The basic Cloud Management best practices are augmented with unique products & frameworks built by RL based on our 50+ successful customer implementations covering the following:

  • Quickstart automation templates
  • AppInsights and ServiceOne – built on ITSM
  • RLCatalyst cloud portals – built on Service Catalog
  • Governance360 – built on Control Tower
  • RLCatalyst BOTS Automation Server

Step-3: Instill
Instill ongoing maturity and optimization using the following themes:

  • Four level compliance maturity model
  • Key Organization metrics across assets, cost, health, governance, and compliance
  • Industry-proven methodologies like HIPAA, SOC2, GDPR, NIST, etc.

For Cloud Management and Governance, RL has Solutions like Governance360, AWS Management and Governance lens, Cloud Migration using CloudEndure. Similarly, we have methodologies like “The Right Way” to use the cloud, and finally Product & Platform offerings like RLCatalyst AppInsights.

Automation
RL promotes an “Automation-First” approach for cloud adoption, covering all stages of the Plan-Build-Run lifecycle. We offer a mature automation framework called RLCatalyst BOTs and self-service cloud portals that allow full lifecycle automation.

In terms of deciding how to get started with automation, we help with an initial assessment model on “What Can Be Automated” (WCBA) that analyses the existing setup of cloud assets, applications portfolio, IT service management tickets (previous 12 months), and Governance/Security/Compliance models.


For the Automation theme, RL has Solutions like Automation Factory, University in a Box, Scientific Research on Cloud, 100+ BOTs library, custom solutions on Service WorkBench for AWS. Similarly, we have methodologies like Automation-First Approach, and finally Product & Platform offerings like RL BOTs automation Engine, Research Gateway, ServiceNow BOTs Connector, UiPath BOTs connector for RPA.

The following blogs explain in more detail our offerings on automation.



DevOps and Microservices
DevOps and microservices with containers are a key part of all modern architecture for scalability, re-use, and cost-effectiveness. RL, as a DevOps specialist, has been working on re-architecting applications and cloud migration across different segments covering education, pharma & life sciences, insurance, and ISVs. The adoption of containers is a key building block for driving faster product deliveries leveraging Continuous Integration and Continuous Delivery (CI/CD) models. Some of the key considerations followed by our teams cover the following for CI/CD with Containers and Kubernetes:


  • Role-based deployments
  • Explicit declarations
  • Environment dependent attributes for better configuration management
  • Order of execution and well-defined structure
  • Application blueprints
  • Repeatable and re-usable resources and components
  • Self contained artifacts for easy portability

The following diagram shows a standard blueprint we follow for DevOps:


For the DevOps & Microservices theme, RL has Solutions like CI/CD Cockpit solution, Cloud orchestration Portal, ServiceNow/AWS/Azure DevOps, AWS/Azure EKS. Similarly, we have methodologies like WOW DevOps, DevOps-driven Engineering, DevOps-driven Operations, and finally Product & Platform offerings like RL BOTs Connector.

AIOps and Service Delivery
RL brings in unique strengths across AIOps with IT Service Delivery Management on platforms like ServiceNow, Jira ServiceDesk and FreshService. By leveraging a platform-based approach that combines intelligent monitoring, service delivery management, and automation, we offer a mature architecture for achieving AIOps in a prescriptive manner with a combination of technology, tools, and methodologies. Customers have been able to deploy our AIOps solutions in 3 months and benefit from achieving 70% automation of inbound requests, reduction of noise on proactive monitoring by 80%, 3x faster fulfillment of Tickets & SLAs with a shift to a proactive DevOps-led organization structure.


For the AIOps & Service Delivery theme, RL has Solutions like AIOps Blueprint, ServiceNow++, End to End Automated Patch Management, Asset Management NOC & ServiceDesk. Similarly, we have methodologies like ServiceOne and finally Product & Platform offerings like ServiceOne with ServiceNow, ServiceOne with FreshService, RLCommand Center.

Summary
RL offers a combination of Solutions, Methodologies, and Product & Platform offerings covering the 360 spectrum of an enterprise Cloud & DevOps adoption across 4 different tracks covering Cloud Management, Automation, DevOps, and AIOps. The benefits of a technology-driven approach that leverages an “Automation-First” model has helped our customer reduce their IT spends by 30% over a period of 3 years with 3x faster product deliveries and real-time security & compliance.

To know more about our Cloud Centre of Excellence and how we can help you adopt Cloud “The Right Way” with best practices leveraging Cloud Management, Automation, DevOps, and AIOps, feel free to write to marketing@relevancelab.com

Reference Links
Considerations for AWS AMI Factory Design



0

2022 Blog, Digital Blog, Blog, Featured, Feature Blog

In our increasingly digitized world, companies across industries are embarking on digital transformation journeys to transform their infrastructure, application architecture and footprint to a more modern technology stack, one that allows them to be nimble and agile when it comes to maintainability, scalability, easier deployment (smaller units can be deployed frequently).

Old infrastructure and the traditional ways of building applications are inhibiting growth for large enterprises, mid-sized and small businesses. Rapid innovation is needed to rollout new business models, optimize business processes, and respond to new regulations. Business leaders and employees understand the need for this agility – everyone wants to be able to connect to their Line of Business (LOB) systems through mobile devices or remotely in a secure and efficient manner, no matter how old or new these systems are, and this is where Application Modernization comes in to picture.

A very interesting use case was shared with us by our large Financial Asset management customer. They had a legacy application, which was 15+ years old and having challenges like tightly coupled business modules, code base/solution maintainability, complexity in implementing lighter version of workflow, modular way of deploying key features, legacy technology stack based application, etc. To solve this problem we had a solid envisioning phase for future state application by considering the next generation solution architecture approach, latest technology stack, value add for the business – lighter version of workflow engine, responsive design & End–to–End (E2E) DevOps solution.

Legacy Application Modernizations/Platform Re-Engineering
Legacy application modernization projects intend to create new business value from existing, aging applications by updating or replacing them with modern technologies, features and capabilities. By migrating the legacy applications, business can include the latest functionalities that better align with where business needs transformation & success.

These initiatives are typically designed and executed with phased rollouts that will replace certain functional feature sets of the legacy application with each successive rollout, eventually evolving into a complete, new, agile, modern application that is feature-rich, flexible, configurable, scalable and maintainable in future.

Monolithic Architecture Vs Microservices Architecture – The Big Picture

Monolithic Architecture

  • Traditional way of building applications
  • An application is built as one large system and is usually one codebase
  • Application is tightly coupled and gets entangled as the application evolves
  • Difficult to isolate services for purposes such as independent scaling or code maintainability
  • Usually deployed on a set of identical servers behind a load balancer
  • Difficult to scale parts of the application selectively
  • Usually have one large code base and lack modularity. If developers community wants to update or change something, they access the same code base. So, they make changes in the whole stack at once

The following diagram depicts an application built using Monolithic Architecture

Microservices Architecture

  • Modern way of building applications
  • A microservice application typically consists of many services
  • Each service has multiple runtime instances
  • Each service instance needs to be configured, deployed, scaled, and monitored

Microservices Architecture – Tenets
The Microservices Architecture breaks the Monolithic application into a collection of smaller, independent units. Some of the salient features of Microservices are

  • Highly maintainable and testable
  • Autonomous and Loosely coupled
  • Independently deployable
  • Independently scalable
  • Organized around domain or business capabilities (context boundaries)
  • Owned by a small team
  • Owning their related domain data model and domain logic (sovereignty and decentralized data management) and could be based on different data storage technologies (SQL, NoSQL) and different programming languages

The following diagram depicts an enterprise application built using Microservices Architecture by leveraging Microsoft technology stack.


Benefits of Microservices Architecture

  • Easier Development & Deployment – Enables frequent deployment of smaller units. The microservices architecture enables the rapid, frequent, and reliable delivery of large, complex applications
  • Technology adoption/evolution – Enables an organization to evolve its technology stack
  • Process Isolation/Fault tolerance – Each service runs in its own process and communicates with other processes using standard protocols such as HTTP/HTTPS, Web Sockets, AMQP (Advanced Message Queuing Protocol)

Today the Enterprise customers across the globe like eBay, GE Healthcare, Samsung, BMW, Boeing, etc. has been adopted Microsoft Azure platform for developing their Digital solutions. We at Relevance Lab also delivered numerous Digital transformational initiatives to our global customers by leveraging Azure platform and Agile scrum delivery methodology.

The following diagram depicts an enterprise solution development life cycle leveraging Azure platform and it’s various components, which enables Agile scrum methodology for the E2E solution delivery


Conclusion
Monolithic Architecture does have its strengths like development and deployment simplicity, easier debugging and testing and fewer cross-cutting concerns and can be a good choice for certain situations, typically for smaller applications.However, for larger, business critical applications, the monolithic approach can bring up challenges like technological barriers, scalability, tight coupling (rigidity) and hence makes it difficult to make changes, and development teams find them difficult to understand.

By adopting Microservices architecture and Microsoft Azure Platform based solutions business could leverage below benefits

  • Easier, rapid development of enterprise solutions
  • Global team could be distributed to focus certain services development of the system
  • Organized around business capabilities, rapid infrastructure provisioning & application development – Technology team will be focused not just on technologies but also acquires business domain knowledge, organized around business capabilities and cloud infrastructure provisioning/ capacity planning knowledge
  • Offers modularizations for large enterprise applications, increases productivity and helps distributed team to focus on their specific modules and deliver them in speed and scale them based on the business growth

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



0

2022 Blog, Blog, Featured, Feature Blog

Our goal, at Relevance Lab (RL), is to make scientific research in the cloud ridiculously simple for researchers and principal investigators. Cloud is driving major advancements in both Healthcare and Higher Education sectors. Rapidly being adopted by various organizations across these sectors in both commercial and public sector segments, research on the cloud is improving day-to-day lives with drug discoveries, healthcare breakthroughs, innovation of sustainable solutions, development of smart and safe cities, etc.

Powering these innovations, AWS cloud provides an infrastructure with more accessible and useful research-specific products that speed time to insights. Customers get more secure and frictionless collaboration capabilities across large datasets. However, setting up and getting started with complex research workloads can be time-taking. Researchers often look for simple and efficient ways to run their workloads.

RL addresses this issue with Research Gateway, a self-service cloud portal that allows customers to run secure and scalable research on the AWS cloud without any heavy-lifting of set-ups. In this blog, we will explore different use cases that simplify their workloads and accelerate their outcomes with Research Gateway. We will also elaborate on two specific use cases from the healthcare and higher education sector for the adoption of Research Gateway Software as a Service (SaaS) model.

Who Needs Scientific Research in the Cloud?
The entire scientific community is trying to speed up research for better human lives. While scientists want to focus on “science” and not “infrastructure”, it is not always easy to have a collaborative, secure, self-service, cost-effective, and on-demand research environment. While most customers have traditionally used on-premise infrastructure for research, there is always a key constraint on scaling up with limited resources. Following are some common challenges we have heard our customers say:


  • We have tremendous growth of data for research and are not able to manage with existing on-premise storage.
  • Our ability to start new research programs despite securing grants is severely limited by a lack of scale with existing setups.
  • We have tried the cloud but especially with High Performance Computing (HPC) systems are not confident about total spends and budget controls to adopt the cloud.
  • We have ordered additional servers, but for months, we have been waiting for the hardware to be delivered.
  • We can easily try new cloud accounts but bringing together Large Datasets, Big Compute, Analytics Tools, and Orchestration workflows is a complex effort.
  • We have built on-premise systems for research with Slurm, Singularity Containers, Cromwell/Netflow, custom pipelines and do not have the bandwidth to migrate to the cloud with updated tools and architecture.
  • We want to provide researchers the ability to have their ephemeral research tools and environments with budget controls but do not know how to leverage the cloud.
  • We are scaling up online classrooms and training labs for a large set of students but do not know how to build secure and cost-effective self-service environments like on-premise training labs.
  • We are requiring a data portal for sharing research data across multiple institutions with the right governance and controls on the cloud.
  • We need an ability to run Genomics Secondary Analysis for multiple domains like Bacterial research and Precision Medicines at scale with cost-effective per sample runs without worrying about tools, infrastructure, software, and ongoing support.

Keeping the above common needs in perspective, Research Gateway is solving the problems for the following key customer segments:


  • Education Universities
  • Healthcare Providers
    • Hospitals and Academic Medical Centers for Genomics Research
  • Drug Discovery Companies
  • Not-for-Profit Companies
    • Primarily across health, education, and policy research
  • Public Sector Companies
    • Looking into Food Safety, National Supercomputing centers, etc.

The primary solutions these customers are seeking from Research Gateway have been mentioned below:

  1. Analytics Workbench with tools like RStudio and Sagemaker
  2. Bioinformatics Containers and Tools from the standard catalog and bring your own tools
  3. Genomics Secondary Analysis in Cloud with 1-Click models using open source orchestration engines like Nextflow, Cromwell and specialized tools like DRAGEN, Parabricks, and Sentieon
  4. Virtual Training Labs in Cloud
  5. High Performance Computing Infrastructure with specialized tools and large datasets
  6. Research and Collaboration Portal
  7. Training and Learning Quantum Computing

The figure below shows the customer segments and their top use cases.

How Research Gateway is Powering Frictionless Outcomes?
Research Gateway allows researchers to conduct just-in-time research with 1-click access to research-specific products, provision pipelines in a few steps, and take control of the budget. This helps in the acceleration of discoveries and enables a modern study environment with projects and virtual classrooms.

Case Study 1: Accelerating Virtual Cloud Labs for the Bioinformatics Department of Singapore-based Higher Education University
During interaction with the university, the following needs were highlighted to the RL team by the university’s bioinformatics department:

Classroom Needs: Primary use case to enable Student Classrooms and Groups for learning Analytics, Genomics Workloads, and Docker-based tools

Research Needs: Used by a small group of researchers pursuing higher degrees in Bioinformatics space

Addressing the Virtual Classroom and Research Needs with Research Gateway
The SaaS model of Research Gateway is used with a hub-and-spoke architecture that allows customers to configure their own AWS accounts for projects to control users, products, and budgets seamlessly.

The primary solution includes:


  • Professors set up classrooms and assign students for projects based on semester needs
  • Usage of basic tools like RStudio, EC2 with Docker, MySQL, Sagemaker
  • Special ask of forwarding and connecting port to shared data on cloud-based for local RStudio IDE was also successfully put to use
  • End-of-day automated reports to students and professors on server “still running” for cost optimization
  • Ability to create multiple projects in a single AWS Account + Region for flexibility
  • Ability to assign and enforce student-level budget controls to avoid overspending

Case Study 2: Driving Genomics Processing for Cancer Research of an Australian Academic Medical Center
While the existing research infrastructure is for on-premise setup due to security and privacy needs, the team is facing serious challenges with growing data and the influx of new genomics samples to be processed at scale. A team of researchers is taking the lead in evaluating AWS Cloud to solve the issues related to scale and drive faster research in the cloud with in-build security and governance guardrails.

Addressing Genomic Research Cloud Needs with Research Gateway
RL addressed the genomics workload migration needs of the hospital with the Research Gateway SaaS model using the hub-and-spoke architecture that allows the customer to have exclusive access to their data and research infrastructure by bringing their one AWS account. Also, the deployment of the software is in the Sydney region, complying with in-country data norms as per governance standards. Users can easily configure AWS accounts for genomics workload projects. They also get access to genomic research-related products in 1-click along with seamless budget tracking and pausing.

The following primary solution patterns were delivered:


  • Migration of existing HPC system using Slurm Workload Manager and Singularity Containers
  • Using Cromwell for Large Scale Genomic Samples Processing
  • Using complex pipelines with a mix of custom and public WDL pipelines like RNA-Seq
  • Large Sample and Reference Datasets
  • AWS Batch HPC leveraged for cost-effective and scalable computing
  • Specific Data and Security needs met with country-level data safeguards & compliance
  • Large set of custom tools and packages

The workload currently operates in an HPC environment on-premise, using slurm as the orchestrator and singularity containers. This involves converting singularity containers to docker containers so that they can be used with AWS Batch. The pipelines are based on Cromwell, which is one of the leading workflow orchestrator software available from the Broad Institute. The following picture shows the existing on-premise system and contrasts that with the target cloud-based system.


Conclusion
Relevance Lab, in partnership with AWS cloud, is driving frictionless outcomes by enabling secure and scalable research leveraging Research Gateway for various use-cases. By simplifying the setting up and running research workloads in a seamless manner in just 30 minutes with self-service access and cost control, the solution enables the creation of internal virtual labs and acceleration of complex genomic workloads.

To know more about virtual Cloud Analytics training labs and launching Genomics Research in less than 30 minutes explore the solution at https://research.rlcatalyst.com or feel free to write to marketing@relevancelab.com

References
Accelerating Genomics and High Performance Computing on AWS with Relevance Lab Research Gateway Solution
Enabling Frictionless Scientific Research in the Cloud with a 30 Minutes Countdown Now!



0