AWS Cloud Operations Blog

Improving Mergers & Acquisitions IT Integration with AWS Application Discovery Service

The purpose of this post is to provide high-level guidance for Mergers & Acquisitions (M&A) stakeholders on how to incorporate AWS Application Discovery Service as part of integration planning and integration data discovery. This post is part of a series of technical content on how M&A integration teams can utilize Amazon Web Services (AWS) to help improve M&A processes.

Throughout M&A transactions, challenges may arise that can be blockers to IT integration success. These challenges could be related to resource alignment and management, task prioritization, people skillsets, end-state and future planning, meeting corporate governance and compliance requirements, or having insufficient data to effectively plan for and execute an IT integration. To solve for these challenges, it is a best practice for M&A Information Technology (IT) Integration Teams to strategize and plan for IT integration using data-driven evidence in addition to an effective data collection strategy. This data and evidence will be used by M&A IT Integration Teams to prioritize and plan for cloud strategy on each potential workload, application, or infrastructure stack that needs to be integrated.

Planning & analysis process for M&A IT Integration Teams

After working backwards from the deal rationale, M&A stakeholders will determine the M&A IT integration pattern based on potential synergies, frequently falling into the holding, preservation, symbiosis, or absorption categories. Following a decision on the holding pattern, the IT Integration Teams will conduct a series of planning steps such as aligning on M&A synergy targets, creating technology and cloud strategy, defining their cloud operating model, and conducting cloud financial economic modeling. A required step throughout the IT integration planning process is for the M&A IT Integration Teams to execute technology analysis on existing and target IT environments and applications to help provide data to develop work-streams for IT integration. This technology analysis includes technical debt assessments, IT security and compliance evaluations, workload migration readiness evaluations, governance and risk management evaluations, optimization and licensing assessments, well architected reviews, among a variety of additional evaluations. To provide context for these evaluations, IT Integration Teams will require information about the target acquisition’s IT estate such as application and operating system dependency mapping, server utilization, and network infrastructure. The evidence gathered from the technology analysis is critical to data-driven execution and success of the M&A IT integration.

The data gathered will be used by IT Integration Teams for a variety of factors throughout the integration process. This includes:

  • Defining IT synergies that are based on technology and business synergies for each existing workload in the IT environment.
  • Assessing the inventory of applications and infrastructure.
  • Prioritizing migration and modernization candidates to achieve cost and revenue synergies.

The following table gives common examples of business drivers, outcomes, metrics and technical guiding principles that M&A IT Integration Teams determine during integration planning [1].

Business Driver Outcome Metrics Technical Guiding Principle
Accelerate innovation Improved competitiveness, increased business agility Number of deployments per day or month, new features released per quarter, customer satisfaction scores, number of experiments Refactor differentiating applications by using microservices and the DevOps operating model to increase agility and speed to market of new features.
Reduce operational and infrastructure costs Supply and demand matched, elastic cost base (pay for what you use) Variation of spend over time
  1. Rehost applications with resource right-sizing.
  2. Retire applications that have low or no utilization.
  3. Dynamically adjust amount of resources provisioned based on workload demand.
Increase operational resiliency Improved uptime, reduced mean time to recovery SLAs, number of incidents
  1. Replatform applications to the latest and best-supported operating system versions.
  2. Implement high availability architectures for critical applications.
Exit the data center Data-center closure by a date within 6-12 months Speed of server migrations
  1. Rehost applications by using AWS Application Migration Service.
  2. Relocate by using VMware Cloud on AWS.
Stay on premises, but increase agility and resiliency Improved competitiveness and uptime while remaining on premises Number of deployments per day or month, new features release per quarter, SLAs, number of incidents
  1. Modernize systems by extending their functionality into the cloud.
  2. Assess for rehosting or re-platforming to AWS Outposts.

[1] The original table can be found in the following documentation: Business Drivers & Technical Guiding Principles

Effective and complete workload and application data collection gives integration teams a portfolio-focused view of the target IT estate’s applications, infrastructure, and services. This data is used to make instrumental determinations on M&A integration strategy. To help make the gathering of this data easy and efficient for M&A stakeholders, it is a best practice to incorporate AWS Application Discovery Service for data collection for the technology analysis of M&A IT integration planning. This post will now discuss how AWS Application Discovery Service helps with the challenges and processes explained in this section.

Improving M&A Due Diligence & IT Integration Planning with Application Discovery Service

The AWS Application Discovery Service helps M&A teams plan and strategize migration or integration tasks during an M&A IT Integration. AWS Application Discovery Service supports M&A IT Integration and Due Diligence teams by collecting and presenting configuration, usage, and server behavior within target IT environments. The collected data is retained in an encrypted format in an Application Discovery Service data store, which can then be exported as a CSV file to be analyzed by integration teams and M&A stakeholders. In addition to migration strategizing as part of IT integration, this data can be used to develop data center consolidation cost synergy opportunities with the cloud and estimate total cost of ownership by application. This data can be exported to and used within AWS Migration Hub, which is a service used by M&A teams to assess, plan, and track migration and modernization of applications and IT estates to AWS.

The AWS Application Discovery Service supports both agentless and agent-based data collection methods, in addition to file-based upload and import. The data gathered includes server dependencies, resource allocation and utilization, and server performance. The AWS Application Discovery Service is offered to AWS customers at no charge, and customers only pay for the AWS resources that are provisioned to store and analyze the data. AWS Application Discovery Service gives M&A stakeholders the ability to connect business and technology synergies to workload metrics that can be measured throughout the IT integration and migration planning process. This data is also used to provide context to evaluations and assessments conducted as part of the technology analysis step of M&A IT integration planning.

AWS Application Discovery Service implementation guide

By using either the Agentless Collector or Discovery Agent of the AWS Application Discovery Service, IT teams are able to gather data on the target IT estate such as server hostname, IP addresses, MAC addresses, disk resource allocations, running processes, time series performance information, networking inbound/outbound connections, CPU utilization, RAM utilization, and Disk I/O.

The Discovery Agent (agent-based discovery) works by deploying the AWS Application Discovery Agent on each of the target IT estate’s virtual machines and physical servers. This agent uploads to the AWS Application Discovery Service at 15-minute intervals to share configuration information. Data is transferred securely by the Discovery Agents to AWS Application Discovery Service using Transport Layer Security (TLS) encryption.

The Agentless Collector (agentless discovery) is to be used on VMware vCenter environments, and works by capturing system performance information and resource utilization for each virtual machine running in the vCenter, regardless of what operating system is in use. For use of the Agentless Collector, the collector will not gather data on running processes or time series performance information.

To gather the full-range of applicable data, it is recommended to use both the Agentless Collector and Discovery Agent to perform data discovery simultaneously. The data collected is used by M&A IT Integration teams to map IT assets, application and workload network dependencies, help determine the cost of running the workloads in the AWS cloud, and to develop workload and application migration strategy. Data Collected by Discovery Agent and Data Collected by Agentless Collector states the types of data that can be collected by the two methods of data discovery.

Prerequisites for AWS Application Discovery Service

To begin using Application Discovery Service, the M&A IT integration team must:

  1. Create an IAM user with the Identity and Access Management (IAM) Managed Policies AWSApplicationDiscoveryAgentlessCollectorAccess attached to the user for the Agentless Collector or AWSApplicationDiscoveryAgentAccess for the Discovery Agent.
  2. Set up an AWS Migration Hub home region. The home Region will be used to store the Migration Hub data. This data will be used for the planning, tracking, and discovery of your migration.

After the team has set up the IAM user and Migration Hub home region, they will need to decide if the Discovery Agent or the Agentless Collector is going to be used based on the business use case. If the team is looking to migrate physical on-premises servers, they will need to use the Discovery Agent as the Agentless Collector is only compatible for VMware vCenter workloads. It is a best practice to use both the Discovery Agent and the Agentless Collector simultaneously to provide a complete context of data.

Using the Discovery Agent

After following the prerequisites, to use the AWS Application Discovery Agent the M&A IT team must:

  1. Unix
    1. Confirm if any 1.x version of the agent is currently installed on any existing servers, it is removed before installing the latest version.
    2. If the workloads are Linux-based, verify that the host the agent is being installed on supports the Intel i686 CPU architecture (P6 micro architecture).
      1. Discovery Agent prerequisites
    3. Once the verification steps on the Discovery Agent prerequisites documentation are complete, the next step is to install the Discovery Agent on the servers that will be migrated over to AWS.
      1. Installing the Discovery Agent on Linux
      2. Installing the Discovery Agent on Windows
    4. After installing the Discovery Agent on the data center servers, the Migration Hub console or the AWS CLI can be used to make API calls to stop or stop data collection.
    5. Optionally, to clean up resources: the Discovery Agent can be uninstalled via the following commands:
      1. Linux
        1. yum package manager: rpm -e —nodeps aws-discovery-agent
        2. apt-get package manager: apt-get remove aws-discovery-agent:i386
        3. zypper package manager: zypper remove aws-discovery-agent
      2. Windows
        1. Open the Control Panel in Windows.
        2. Choose Programs.
        3. Choose Programs and Features.
        4. Select AWS Discovery Agent.
        5. Choose Uninstall.

Using the Agentless Collector

For the Agentless Collector, after the prerequisites have been fulfilled, the M&A IT team will complete the following steps:

  1. Set up the Agentless Collector by downloading and deploying the Agentless Collector Open Virtualization Archive (OVA) file on your VMware environment.
  2. After signing in to vCenter as a VMware user with permission to deploy a new virtual machine and to deploy the collector, the next step is to access the Agentless Collector console. This would be done by:
    1. Open a web browser, and then type the following URL in the address bar: https://<ip_address>/, where ip_address is the
    2. IP address of the collector from Step 3: Deploy Agentless Collector.
    3. Choose Get Started the first time you access Agentless Collector. Thereafter, you’ll be asked to Log in.
  3. Given this should be the first occurrence of accessing the Agentless Collector console, the M&A IT team will need to configure the Agentless Collector next. If this is not the first time, skip this step.
  4. Now it is time to set up a data collection module to collect the inventory, profile, and utilization data from the servers that have the collector deployed and configured.
  5. Finally, to view the data collected by the Agentless Collector, sign in to the Migration Hub console.

The steps above provide a detailed implementation guide on how M&A IT teams can use the AWS Application Discovery Service to collect inventory, server utilization, and system performance data with the Discovery Agent or Agentless Collector.

AWS Migration Hub and AWS Application Discovery Service

The AWS Application Discovery Service is natively integrated with AWS Migration Hub. AWS Migration Hub is a suite of tools and services that help M&A IT Integration teams collect and inventory target existing IT assets based on usage, application components, and infrastructure dependencies. AWS Migration Hub is then used by M&A IT integration teams to generate cloud cost forecasts, create migration strategies, and track the progress of application migration to AWS. This allows M&A teams to automate lift-and-shift migrations and fast-track application refactoring during IT integration to help meet technology cost and revenue synergies faster. After data is collected using the AWS Application Discovery Service, M&A IT teams will group the servers into applications, in which the information will then be configured in AWS Migration Hub to track the status of migrations to the AWS cloud across the target IT estate’s application portfolio.

Conclusion

This post discussed how the mergers & acquisitions IT integration process can be improved with the AWS Application Discovery Service (ADS), how to get started with AWS ADS, and how to leverage AWS ADS data with AWS Migration Hub during the M&A IT integration process. Please refer to the following webpages to learn more about AWS ADS: Getting Started with AWS Application Discovery Service, AWS Application Discovery Service Pricing, and AWS Application Discovery Service Resources.

About AWS Mergers & Acquisitions Advisory

The AWS Mergers & Acquisitions Advisory Team (AWS M&A Advise) is a group of subject matter experts at AWS that provide a suite of complimentary advisory engagements to AWS customers engaged in M&A transactions. If your organization is going through an M&A transaction and would like to learn more about how AWS supports customers throughout the transaction lifecycle, please reach out to AWS Mergers & Acquisitions Advisory Team through your organization’s aligned AWS Account Manager with this link.

About the authors:

Rohit Talluri

Rohit Talluri is the Global Solutions Lead for AWS Mergers & Acquisitions Advisory and an Enterprise Solutions Architect based in Austin, Texas. His domains of expertise include enterprise strategy, digital transformation, venture innovation, cloud solutions development, and mergers & acquisitions technology strategy. Rohit operates as a Strategic Advisor on global M&A transactions and digital transformation initiatives, partnering with customers’ senior executives to work backwards from their expected business and technology outcomes. Outside of work, he enjoys golfing, fishing, reading, and going on adventures with his labrador retriever Colt.

Marco Punio

Marco Punio is a Digital Native Solutions Architect based in Seattle, WA. His primary focuses are in AI/ML, enterprise modernization, and mergers & acquisitions technology strategy as an AWS M&A Advisory Ambassador Lead. These focuses enable him to help global customers align with their business goals to execute on strategic modernization efforts. He is a qualified technologist with a passion for traveling and spending time outdoors.