devopsy.kinsta.cloud

Category: Cloud Computing

  • Understanding Cloud APIs

    Understanding Cloud APIs

    Six out of every 10 businesses had moved their workloads to the cloud as of last year. More steady traffic in the cloud and more direct cloud instances mean more to juggle–and the need for greater customization and integration. That’s where APIs come in. APIs are doing for the cloud what they did for on-premises resources: Segregating, simplifying, specializing and integrating services across a network–only this time, it’s future-proofing the architecture by building it in the cloud. As one cybersecurity researcher quipped, “Everyone wants to use APIs to build innovative solutions, but no one wants to invest in building them.” Cloud APIs will soon spell the difference between companies that can keep up with digitization and those that can’t.

    Ultimately, it will come down to which organizations take the time to understand them and use them to their best benefit.

    What is a Cloud API?

    Simply put, “a cloud API is a software interface that allows developers to link cloud computing services together.” APIs allow services to share functionality and information for other programs to use, and nowhere is this more important than in the interconnected ecosystem of the cloud.

    Because APIs act as a waiter in a restaurant, the better the waiter, the simpler ordering will be for you and for your customers. The best cloud API connects backend cloud components, services and platforms and does all the busy work so all your users see on the front end is a seamless customer experience. Since most of the work of tomorrow will be in the cloud (and is moving there in ever-increasing numbers), to not build up this functionality now is to take a deliberate risk.

    Types of Cloud APIs

    There are several types of cloud APIs based on the four major areas where cloud computing will require integration–either with another platform or another cloud provider.

    ● PaaS APIs (Service level): These platform-as-a-service APIs are designed to integrate with databases, portals, storage components and messaging systems, providing access and functionality in the cloud.
    ● SaaS APIs (Service level): Referred to as software-as-a-service APIs, these connect application layer resources with the cloud and underlying IT infrastructure. A CRM is a good example of where these types of APIs can be used to create a “cloud application extension” for your ecosystem.
    ● IaaS APIs (Service level): Infrastructure-as-a-service APIs control the distribution and deployment of specific cloud resources, performing such tasks as rapid provisioning and de-provisioning, network configurations and workload (VM) management in the cloud.
    ● Cross-platform APIs: Because many environments use multiple cloud providers and platforms, cross-platform compatibility is required. Cross-platform APIs streamline data management by allowing tenants to access their cloud-based resources and workloads from any of their cloud providers, not just their primary one. They provide a higher level of abstraction by translating a specific provider’s API calls into a more generic form, making accessibility, manageability and interoperability in the cloud easier.

    Cloud API Security

    In many respects, the security threats faced by cloud APIs are the same that any cloud-based resource would encounter and present the same challenges. That being said, malicious API traffic surged over 117% last year, from an average of 12 million to over 26 million calls, according to one industry report. The same study revealed that over a third of respondents experienced authentication and data exposure-related incidents with their APIs, and 54% delayed rolling out a new application because of API security concerns.

    Unguarded, a cloud API could present the keys to the kingdom and allow an attacker to access the hub of not just one but several cloud providers and their workloads, resources and assets. Protecting cloud-based APIs with standard authentication and authorization practices such as OAuth2.0 and OpenID Connect can reduce the chance of a lateral attack.

    Why Cloud APIs

    Cloud usage is not set to slow down and will soon become ubiquitous. Companies that aren’t on it are migrating their workloads in greater numbers and entrepreneurial ventures are spinning up as cloud-native. Cloud APIs are simply the logical response to the complexities that come with the terrain. Without cloud APIs, the user experience will be negatively impacted and will only become more apparent when compared with companies that do use cloud APIs.

    For companies looking to innovate in 2023 and release new applications, APIs are the way to cut-and-paste the process safely. Similarly, as more cloud-based services become API-reliant, application reusability is another factor requiring cloud APIs. And lastly, the complexity of cloud products, platforms and ecosystems (even within one organization) makes cloud APIs a necessity for smooth interoperability. And they help with scalability–multiple components of a cloud-based service can be parsed out, updated and patched individually and refined separately so that one area can develop without being dragged down by the whole. Cloud APIs allow for that granular level of customization that can help an organization refine its edge.

    Simply put, organizations will need these APIs to not only compete but stay relevant as businesses continue to take their fight to the cloud.

  • DevOps Done Right: How to Succeed in DevOps From Day One

    DevOps Done Right: How to Succeed in DevOps From Day One

    Whether you are diving into DevOps for the first time or trying to do it right this time, DevOps Onramp provides all the information you need for a successful DevOps journey. Join us on May 4, 2023, to learn how to get started with DevOps—and how to do it right.

    You’ll learn:

    • DevOps best practices, processes and the tools you’ll need
    • Tips and tricks from experienced practitioners sharing their real-world experiences
    • The basics of DevOps
    • How to choose the right technologies
    • Common challenges and how to overcome them
    • The benefits of DevOps as a methodology and a career
    • And more!

    As organizations face increasing pressure to adapt faster and become more agile, getting DevOps right and adopting a DevOps mindset is crucial to deliver high-quality software faster and increase business value. And that opens up great opportunities for anyone looking to start a new career or pivot from an existing role in technology.

    DevOps has become the standard of software development methodologies and it’s critical for successful digital transformation. Adopting DevOps—and doing it the right way—can benefit not only your organization but also your customers and end users by fostering innovation, reducing time to market, increasing development productivity, improving product quality and reliability and reducing costs.

    That’s why many big companies, such as Amazon, Netflix, Facebook and many others have adopted a DevOps mindset—and DevOps practices. And as more businesses are looking to move to the cloud and transition into DevOps, there are plenty of job opportunities available for DevOps engineers at various levels.

    DevOps is a growing field with a lot of opportunities for professional growth. According to a research study by Global Market Insights Inc., the DevOps market is expected to surpass $30 billion by 2028. Due to the high demand for skilled DevOps engineers and the shortage of qualified DevOps professionals in the industry, there is no better time to upskill than now.

    Whether you are diving into DevOps for the first time or trying to do it right this time, DevOps Onramp provides all the information you need for a successful DevOps journey.

    At DevOps Onramp Virtual Summit, taking place on May 4, 2023, you’ll get all the knowledge you need to get started with DevOps —and how to do it right. Join us to learn about DevOps best practices, processes and tools and get tips and tricks from experienced practitioners sharing their real-world experiences. We’ll go through the basics of DevOps, how to choose the right technologies, common challenges and the benefits of DevOps.

    One thing is for sure: The future of DevOps is very promising. The rush has started—don’t be left behind!

    DevOps Onramp Virtual Summit - Submit your talk

    If you’re interested in becoming a sponsor, please contact us. For more information, please visit the DevOps Onramp website.

  • 5 Technologies Powering Cloud Optimization

    5 Technologies Powering Cloud Optimization

    Cloud optimization is the process of improving the performance, cost-effectiveness, and reliability of cloud computing resources and services. It involves identifying and addressing inefficiencies in cloud architecture, application design and resource utilization, as well as implementing best practices for security, scalability and disaster recovery. 

    The goal of cloud optimization is to maximize the benefits of cloud computing while minimizing the risks and costs associated with it. It is a multi-disciplinary process that may involve several key elements, such as:

    • Capacity planning: Evaluating the current and future resource requirements of your cloud environment and ensuring that the right amount of resources are available to meet your needs.
    • Resource utilization: Monitoring and managing the utilization of cloud resources to ensure that they are used efficiently and effectively.
    • Cost optimization: Minimizing the cost of cloud resources and services by identifying and addressing areas of inefficiency and waste, such as over-provisioning or underutilized resources.
    • Performance optimization: Improving the performance of cloud applications and services by optimizing network bandwidth, storage performance and database performance.
    • Security optimization: Enhancing the security of cloud resources and data by implementing best practices for access control, encryption and threat mitigation.
    • Scalability optimization: Making sure that your cloud environment can scale up or down as needed to meet changing resource demands.
    • Disaster recovery: Establishing a disaster recovery plan to ensure that your cloud environment can quickly recover from failures or outages.

    Why is Cloud Optimization Important?

    Optimization is important for several reasons, including:

    • Cost savings: By optimizing the use of cloud resources, organizations can reduce their cloud computing costs and ensure that they are only paying for what they actually need.
    • Improved performance: By optimizing cloud applications and services, organizations can improve the performance and responsiveness of their systems, leading to a better user experience and increased productivity.
    • Increased reliability: Optimization helps organizations to improve the reliability and availability of their cloud environment, reducing the risk of downtime and ensuring that their systems are always available when they are needed.
    • Better security: By implementing best practices for security, organizations can better protect their cloud resources and data, reducing the risk of data breaches and other security incidents.
    • Scalability: Cloud optimization enables organizations to scale their environment up or down as needed, ensuring that they always have the resources they need to support their growing business.
    • Improved agility: Organizations can be more agile and responsive to changing business needs, enabling them to quickly and effectively respond to new opportunities and challenges.

    5 Technologies Powering Cloud Optimization

    Cloud Cost Management

    Cloud cost management is a critical component of optimization that helps organizations to monitor and manage their cloud spend. The goal is to ensure that organizations are only paying for the cloud resources they actually need and that they are using those resources efficiently.

    Cloud cost management solutions (such as Azure Cost Management or AWS Trusted Advisor) provide detailed cost reports, usage analytics and cost optimization recommendations to help organizations understand their cloud spend and identify areas of inefficiency. For example, these solutions can:

    • Monitor and report on cloud resource usage: By monitoring the usage of resources, cloud cost management solutions can provide detailed cost reports that show how much an organization is spending on each resource, such as compute instances, storage and network traffic.
    • Automate cost optimization: Many cloud cost management solutions can automate cost optimization tasks, such as turning off unused resources or right-sizing instances to reduce waste.
    • Provide cost analysis and forecasting: These solutions can provide detailed cost analysis and forecasting to help organizations understand the impact of changes to their cloud environment and plan for future spending.

    Autoscaling

    Autoscaling is a technology that enables organizations to automatically scale their cloud resources up or down as needed to meet changing demands. The goal of autoscaling is to ensure that organizations always have the right amount of resources to support their workloads while minimizing costs and ensuring that their systems are always available when they are needed.

    Autoscaling works by monitoring the performance and usage of cloud resources, such as compute instances, storage and network traffic, and automatically adjusting the size of those resources to meet changing demand. For example, autoscaling can:

    • Add or remove instances as needed: If demand for a resource increases, autoscaling can automatically add more instances to meet that demand. If demand decreases, autoscaling can automatically remove instances to reduce waste.
    • Optimize resource utilization: Autoscaling can optimize the utilization of cloud resources by right-sizing instances to meet demand, ensuring that resources are not over-provisioned or underutilized.
    • Improve system availability: By automatically adding or removing resources as needed, autoscaling can ensure that systems are always available and responsive, reducing the risk of downtime and improving the user experience.

    API Gateway

    An API gateway is a server that acts as an intermediary between an application and one or more microservices. The API gateway is responsible for request routing, composition and protocol translation, which enables microservices to communicate with each other securely and efficiently.

    An API gateway can help with cloud optimization in several ways:

    • Load balancing: The API gateway can distribute incoming API requests to multiple microservices, improving the overall performance and reliability of the system. It can also provide features such as automatic failover and load balancing, which helps to ensure that the system remains available and responsive even in the face of high traffic volumes or other performance challenges.
    • Resource optimization: By acting as a single entry point for API requests, the API gateway can help to reduce the overall resource usage of the system. For example, it can cache frequently used data and serve it directly to clients without having to go back to the microservices, reducing the load on the underlying system.
    • Monitoring and management: API gateways provide a centralized management interface for the microservices, making it easier to monitor and manage the system as a whole. This includes features such as logging, monitoring and alerting, which can help to identify and resolve performance issues quickly and effectively.

    GitOps Platforms

    GitOps is a paradigm that helps automate application deployment and infrastructure provisioning. It typically involves using Git, an open source version control system, as a single source of truth for declarative infrastructure and applications. 

    GitOps aims to improve the developer experience by letting teams use their familiar software development tools to manage application and infrastructure changes.

    With GitOps, cloud optimization can be achieved by:

    • Automating the deployment process: GitOps automates the deployment process, reducing the risk of human error and speeding up the deployment process. This helps to ensure that resources are deployed consistently and correctly, improving the reliability and performance of the environment.
    • Managing cloud infrastructure as code: With GitOps, cloud infrastructure can be defined and managed as code, making version control and collaborating on configurations easier. This helps to optimize the use of resources and improve the overall efficiency of the cloud environment.
    • Improving collaboration and communication: GitOps enables teams to collaborate and communicate more effectively, reducing the risk of conflicts and delays. With Git, changes can be easily tracked, discussed and approved, improving the speed and quality of deployments.
    • Enforcing governance and compliance: GitOps provides a centralized and auditable record of all changes to the environment, making it easier to enforce governance and compliance requirements. This helps to improve the security and reliability of the cloud environment, reducing the risk of breaches and other security incidents.

    APM

    Cloud application performance management (cloud APM) is the practice of monitoring, analyzing and optimizing the performance of cloud-based applications. The goal is to ensure that these applications are available, responsive and performing optimally to meet the needs of users.

    These solutions provide a range of tools and capabilities for monitoring and optimizing cloud-based applications, including:

    • Performance monitoring: Cloud APM solutions monitor application performance in real-time, providing visibility into performance metrics such as response time, resource utilization and error rates.
    • Root cause analysis: These solutions can help identify the root cause of performance issues by providing detailed performance data and tracing information.
    • Capacity planning: Cloud APM solutions can provide detailed performance data and analytics to help organizations plan and optimize their application deployments.
    • Performance optimization: These solutions can provide recommendations for performance optimization, such as right-sizing cloud resources, reducing resource utilization and improving application design.
    • Continuous performance monitoring: These solutions can continuously monitor application performance, providing real-time visibility and alerting to help organizations quickly identify and resolve performance issues.

    Conclusion

    In conclusion, cloud optimization is critical for organizations looking to maximize the value and minimize the costs of their cloud deployments. By using technologies such as cloud cost management, autoscaling, API gateways, GitOps platforms and APM, organizations can improve availability, performance and efficiency while reducing costs and ensuring that they are always prepared to meet changing business needs. 

    These technologies provide the tools and capabilities necessary to monitor, analyze and optimize cloud-based applications, helping organizations to ensure that their deployments are always delivering the best possible value. By embracing optimization, organizations can stay ahead of the curve and ensure that their cloud deployments are always aligned with their business goals and objectives.

  • Deploying a Service Mesh: Challenges and Solutions

    Deploying a Service Mesh: Challenges and Solutions

    A service mesh is a dedicated infrastructure layer that enables communication between microservices in a distributed application. It helps to abstract the underlying communication infrastructure, such as network protocols and load balancing and provides a consistent interface for microservices to communicate with each other.

    In a service mesh, each microservice in the application is represented as a “service” within the mesh, and the mesh acts as a proxy between the services, routing requests and responses between them. It also includes a control plane, which manages and configures the proxy instances and provides observability and monitoring capabilities for the services.

    Some of the benefits include:

    • Improved observability: A service mesh provides visibility into the communication between microservices, which can be helpful for debugging and monitoring the performance of the application.
    • Resilience: It can help to make microservices more resilient by providing features such as retries, timeouts and circuit breakers, which can help to prevent cascading failures.
    • Security: A service mesh can help to secure communication between microservices by providing features such as mutual TLS (mTLS) and rate limiting.
    • Ease of use: It provides a consistent interface for microservices to communicate with each other, which can make it easier for developers to build and manage distributed applications.

    Why is a Service Mesh Important?

    A service mesh is important because it helps to abstract the underlying communication infrastructure for microservices-based applications, which can make it easier for DevOps teams to build and manage these types of applications.

    One of the main benefits is that it provides a consistent interface for microservices to communicate with each other, which can make it easier for developers to build and manage distributed applications. For example, a service mesh can provide features such as load balancing, retries and timeouts, which can be useful for ensuring the resilience and reliability of the application.

    It can also help to improve observability for microservices-based applications by providing visibility into the communication between the microservices. This can be helpful for debugging and monitoring the performance of the application, which can, in turn, make it easier for DevOps teams to identify and resolve issues more quickly.

    What are the Main Deployment Challenges?

    There are several key challenges that a DevOps organization may face:

    • Complexity: Service meshes can be complex to set up and manage, especially in large, distributed applications. This can make it challenging for organizations to implement and ensure that it is configured correctly.
    • Operational overhead: Service meshes can add a layer of complexity to the application stack, which can increase the operational overhead of managing the application. This can be especially challenging for organizations that are not used to operating one.
    • Cost: While service mesh software is typically open source, the need to run a sidecar container alongside every container can increase the compute capacity needed to run a Kubernetes cluster.
    • Integration with existing systems: They can be difficult to integrate with existing systems and processes, especially if the organization has not previously used a service mesh. This can make it challenging to get the full benefits.
    • Lack of expertise: Service meshes are relatively new technologies, and there may be a lack of expertise within the organization. This can make it challenging for organizations to get started and ensure it is used effectively.
    • Performance overhead: Service meshes can add a performance overhead to the application as they introduce an additional layer of proxying between microservices. This can make it challenging to ensure that the application is performant and scalable.

    Best Practices to Ease Service Mesh Pain

    Here are a few best practices that can turn service mesh deployments from a pain to a pleasure.

    Use Configuration-as-Code in a GitOps Approach

    Using configuration as code in a GitOps approach is a best practice to improve reliability and maintainability.

    GitOps is a way of managing infrastructure and applications using Git as the single source of truth. In a GitOps approach, all configuration and deployment information is stored in Git, and changes to the infrastructure and applications are made by committing code changes to Git.

    Using configuration-as-code in a GitOps approach can help to improve the reliability and maintainability of the service mesh in several ways:

    • Version control: Storing configuration-as-code in Git allows you to track changes to the service mesh configuration and roll back changes if necessary. This can help improve the service mesh’s reliability by making it easier to identify and fix issues.
    • Collaboration: Git allows multiple people to work on configuration at the same time, which can make it easier to collaborate on changes and ensure that the service mesh is properly maintained.
    • Automation: GitOps approaches often use automation tools, such as continuous integration and delivery (CI/CD) pipelines, to automatically deploy changes to the service mesh when code is committed to Git. This can make it easier to manage and maintain and can help to improve its reliability by reducing the risk of errors.

    Focus On Developer Experience

    Ensuring a positive developer experience is a best practice because it can help to improve the productivity and effectiveness of the development team. A positive developer experience means that developers can easily understand and use the service mesh, and that it does not introduce unnecessary complexity or frustration into their workflow.

    Here are some ways to improve the developer experience:

    • Provide clear documentation: It is important to provide clear and comprehensive documentation on how to use the service mesh, including examples and tutorials. This will make it easier for developers to understand and use.
    • Integrate with existing tools and processes: The service mesh should integrate seamlessly with the existing tools and processes that the development team is already using. This will help to minimize disruptions to the team’s workflow and make it easier for them to work with.
    • Provide support and training: It is important to provide support and training to help developers understand and use the service mesh effectively. This can include online resources, in-person training, and support from the vendor.

    Reduce Costs With Low-Cost Cloud CPUs

    A service mesh increases the cost of an entire containerized deployment because it requires deploying a sidecar container alongside all workloads. When running on Amazon Web Services (AWS), the AWS Graviton processor can ease deployment by reducing the cost of running service mesh infrastructure.

    The AWS Graviton processor is a custom ARM-based processor designed specifically for use in Amazon Web Services (AWS) environments. It is optimized for running workloads commonly found in the cloud, such as microservices and containerized applications.

    Using the AWS Graviton processor can help to reduce the compute costs of a service mesh because it is generally less expensive than other processors, such as Intel Xeon processors. The AWS Graviton processor is also designed to be highly efficient, which can help reduce the service mesh’s overall resource utilization. This can help to reduce the cost.

    Choose the Right Control Plane

    Choosing the right control plane is a best practice because it can impact the performance, reliability and maintainability of the service mesh. The control plane is the central component that manages the proxy servers and enables you to configure and monitor the communication between microservices.

    Here are some considerations for choosing a good control plane:

    • Compatibility: It is important to choose a control plane that is compatible with the rest of the service mesh infrastructure, as well as with the application and its dependencies. This will ensure that the control plane can operate effectively and support the needs of the application.
    • Performance: The control plane should be able to handle the traffic and data volumes of the application without experiencing performance degradation. It should also be able to scale up or down as needed to meet changing demand.
    • Reliability: The control plane should be reliable and able to withstand failures without interruption. It should also provide features such as circuit breaking, retries and timeouts to help improve the resiliency of the service mesh.
    • Ease of use: The control plane should be easy to use and understand, with clear documentation and a user-friendly interface. This will make it easier for the DevOps team to manage and maintain the service mesh.
    • Integration with other tools: It is important to consider how the control plane integrates with other tools and systems, such as monitoring and logging tools, and how it can be integrated into the overall workflow of the organization.

    Conclusion

    In conclusion, deploying a service mesh can be a complex and challenging process, but it can also provide significant benefits in terms of improving the reliability, scalability and security of a cloud-native application. 

    Some of the key challenges include complexity, operational overhead, integration with existing systems, lack of expertise and performance overhead. 

    To overcome these challenges, it is important to choose the right service mesh, use configuration-as-code in a GitOps approach, choose the right control plane and ensure a positive developer experience. By addressing these challenges and following best practices, organizations can successfully deploy a service mesh and reap the many benefits it has to offer.

    Image Source 

     

  • Migration to AWS Cloud: Do’s and Don’ts

    Migration to AWS Cloud: Do’s and Don’ts

    What if one day, you were told to migrate to AWS/GCP/Azure/any other cloud provider? It would likely introduce some stress, to understate the case, right?

    I’ve seen quite a few migrations in my career and, in this article, I would like to share some thoughts to help DevOps engineers, architects, managers or anyone else who might be involved in such a migration. I’m focusing on migration to AWS mostly because I have a specialization in this subject and because it’s fairly common, but I think these experiences are applicable to any other cloud provider. For this case, I’ll cover migration from on-premises to a cloud because these types of migrations are much more difficult (in my opinion) than cloud-to-cloud migrations. I’ll also share my own perspective on do’s and don’ts of migrations. But please filter them through critical thinking and remember that everybody has their own background and experience. What’s good for one might be bad for another.

    What is a Cloud Migration?

    Let’s start by defining what migration is. How do you know if it’s a migration? I like the following definition: 

    Cloud migration is the process of moving a data center’s capabilities to IaaS providers.

    “Capabilities” is the keyword. A business does not migrate servers/virtual machines/hardware/data or anything else. A business migrates its capabilities.

    There are seven migration strategies (or seven Rs): Relocate, rehost (lift and shift), replatform, repurchase, rearchitect/refactor, retain and retire.

    For example, I’ve seen situations when a company was creating a new product that would replace an existing one hosted on-premises. The product would be designed to be well-architected and cloud-native from the very beginning and would be hosted in a cloud. Is that migration or greenfield project type? From the definition above, we can say that this is a migration. This is a rearchitecture strategy for migration.

    What if we buy a new SaaS subscription instead of some software we host on-premises? This is also a migration: A repurchase strategy.

    Let’s dig deeper into these seven migration strategies:

    Relocate. We host some workloads on-premises and move them as-is into the cloud. This is possible in limited cases; when we migrate cloud-agnostic workloads or when a platform might be cloud-native. Examples: Relocate a Kubernetes cluster from on-premises to the cloud or relocate VMware machines with VMware Cloud.

    Rehost. This is also known as lift and shift. We convert virtual machines/servers on-premises to virtual machines in the cloud. Examples: Migrate on-premises virtual machines to EC2 instances with CloudEndure, or create EC2 instances, install the software and apply the same configuration as on-premises.

    Replatform. We migrate a workload to a cloud-native platform without rearchitecting systems. Examples: Migrating PostgreSQL to AWS RDS or Docker containers to ECS.

    Repurchase. We replace some custom/legacy system with SaaS. Example: Replace custom on-premises email system with SendGrid or replace CRM with Salesforce.

    Rearchitect. We change your application’s architecture to be cloud-native and use managed cloud services. That also includes rebuilding from scratch. Examples: Change the code of your apps to upload files to S3 instead of local storage or containerize an app and migrate to ECS.

    Retire. We decide that a piece of the workload will be eliminated if not needed. Example: Log servers are no longer needed as migrated applications use CloudWatch.

    Retain. We leave it on-premises. Usually, it’s temporary. Example: A huge Oracle database with a lot of functions and customization is not moved to AWS because of the massive costs. It’s decided to keep it on-premises until it’s replaced with another solution in the modernization phase.

    I like the following picture that shows the high-level stages of each migration type. It may be a helpful cheat sheet to approach migrations.

    Picture Source: https://aws.amazon.com/blogs/enterprise-strategy/new-possibilities-seven-strategies-to-accelerate-your-application-migration-to-aws/

    Migration Don’ts

    I don’t recommend the following when performing a migration:

    Migrating too quickly. A business might think, “We have to migrate to the cloud quickly. Let’s do lift and shift right now!” It’s better not to start right away. Are you sure that rehosting strategy will really be faster than some other strategy? In one instance, we tried to migrate on-premises VMs, but it didn’t work because the OS on the machines was quite old. Some OS-level dependencies didn’t work on AWS’ hardware. This required a lot of configuration work, custom code and testing and the initial estimates were completely wrong. It eventually turned out to be a replatform. 

    Assess what you have, analyze it, analyze the business case and think. During this engineering work, we should stop and think before we act.

    Migration without clear goals. Why are you migrating? If you can’t answer the question, you probably need to find an answer or not migrate at all. I’ve seen situations in which businesses wanted to migrate to the cloud to reduce costs. They performed a lift and shift migration without doing a total cost analysis. Only after the fact did they realize that it cost even more than when it was in a colocation center. This migration may be considered unsuccessful.

    To avoid this, define clear goals and define the target state that achieves these goals. If costs are the driver, make the proper calculations and plan accordingly. Cost optimization will be the main thing to focus on when you plan your target state. If availability is the reason, measure it, define SLOs and focus on availability in a target state.

    Black box migration. Don’t treat workloads you migrate as a black box. It is almost critically important to know what exactly you migrate, what technical requirements it has, what dependencies it has and how it works. That information will allow you to choose the correct strategy for workload migration. If this is not done, there’s an excellent chance that you will choose the wrong path and the result will be lost. Capturing information about your systems is the most important task when planning.

    Don’t forget well-architected. Migration is stressful. That’s why people tend to try to get through it quickly on as short of a timeline as possible, focusing on the only thing: Making everything work in the cloud ASAP. They set aside well-architected framework pillars. This is a mistake; it may lead to a different set of negative consequences of different severity. Don’t ignore well-architected framework pillars, or you won’t know about potential issues or risks until they have already materialized.

    Use one strategy for everything. It’s always easy to make a single overarching plan for all workloads. For example, deciding to do lift and shift and then rehost everything in the cloud. But what if some things could be retired? What if some components could be replatformed with little effort? You will save significant resources by analyzing all of the components of the workload and deciding on a unique strategy for every component. Collect data and plan properly.

    Cloud Migration Do’s

    Do perform a cloud readiness assessment. If a person migrates to a new location, they should consider:

    • That the climate will be different
    • The language spoken in that location may be different
    • The cost of living will be different and perhaps more expensive

     When a company migrates to the cloud, they should ask questions including: 

    • Have we considered all the risks?
    • What must be changed for the workload to actually work?
    • Do we have the human resources required for all of the work?
    • Do I have the budget to accomplish everything? 

    The Cloud Readiness Assessment is a long-form interview about varying processes in the company that identifies whether a company is ready to live in the cloud. It’s based on the Cloud Adoption Framework (CAF) and allows us to understand what should be changed so that a migration can happen successfully. AWS has a Cloud Readiness Assessment tool which covers all of that. I urge you to use this process for all workloads being considered for migration.

    Create a business case. Describe the business case. What are the drivers for migration? What problems are resolved with the migration? What goals should be achieved by migration? Be specific: If you intend to reduce costs, then calculate the sum. If you intend to improve availability, then define a clear SLO. That will be the number-one driving force for your migration. All other artifacts should be based on it.

    Automate discovery and data collection. Use automated data collection and analysis to create a portfolio for migration. Don’t base your analysis on manually created spreadsheets or verbal communication. Let machines collect data about inventory and usage statistics. The more precise data you collect, the better. AWS Migration Evaluator service will help you to do this in most cases.

    Portfolio management. Create a portfolio of workloads to migrate, analyze them, choose a migration strategy for each, choose some for the pilot/first wave migration, estimate timelines and prepare a plan. Execute the migration after that. Repeat until the migration portfolio is empty. Share the data. Have subject matter experts review it.

    Found CCoE. This is part of the CAF mentioned above. Before executing the migration, create a team of people responsible for creating a strategy and defining a good standard of living in the cloud: A Cloud Center of Excellence. It should include people with a variety of subject matter expertise including management, operations, platform, people, finance and every other department consuming or providing services in the cloud. These people will prepare a landing zone for the migration, hard policies and softer policies for the landing zone; preparing the company for life in the cloud. Don’t fear revisiting any policy.  Make changes as new factors come to light.

    Well-architected framework reviews (WAFR). Do well-architected framework reviews (WAFRs) during migration waves and after them. That will allow you to avoid foolish mistakes and to get a high-level overview of what you have done from a technical perspective.

    Modernization plan. Prepare a modernization plan along with the migration plan. Migration isn’t the end of the story. To be effective in the cloud, you will have to do well-architected reviews and modernization. The first modernization is likely to be huge; that’s why it’s important to plan and to schedule budgets and resources in advance. Failure to do this will lead to increased operational costs, not decreased costs.

    Summary

    If we want to reduce the risk associated with a migration and to make it less stressful, we do not rush. You may believe that rushing will save you time and other problems will be managed later, but experience tells us that this is wrong. The most likely outcome is that you will end up resolving all the issues encountered in the most stressful situation possible: When you cut over to your new cloud-based production environment. Instead of resolving issues in a well-considered way and in advance, the choices will be made under duress. Proper engineering principles must be applied here. Picking up a bridge and moving it from one river and plunking it down on another never works. The key to migration success is to measure, analyze, design and plan. Migrating a workload to the cloud is hard, but I hope these do’s and don’ts will make it easier.

  • AWS re:Invent — Top 4 Things We Learned This Week

    AWS re:Invent — Top 4 Things We Learned This Week

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

    This week, four things that caught my eye from Amazon Web Services’ re:Invent 2022 conference: SnapStart turbocharges Lambda, Graviton3E ARM HPC SoC, AWS continues hiring, and AWS’s origin story. (more…)

  • CNCF Survey Predicts Growing Wasm Momentum

    CNCF Survey Predicts Growing Wasm Momentum

    A survey of 93 developers conducted by the Cloud Native Computing Foundation (CNCF) found more than a quarter (28%) are using WebAssembly (Wasm) in their cloud-native development projects, with another 36% planning to do so during the next six to 12 months.

    The survey, published this week, is effectively a straw poll that found 42% of respondents are developing or plan to develop server-side applications using Wasm, while 48% are combining or planning to combine server-side workloads with client-side applications.

    Originally developed five years ago by the World Wide Web Consortium (W3C) to create a common format for browsers executing JavaScript code, Wasm is now starting to be used to rapidly build lighter-weight applications that can be deployed on any server platform. The promise of being able to write an application once and deploy it anywhere is finally being realized some 25 years after the initial introduction of the Java programming language.

    The survey found 70% of respondents viewed Wasm runtimes as a disruptive emerging technology. The primary programming languages used in association with Wasm are Rust (59%), Go (47%) and JavaScript (46%), the survey found.

    Nearly two-thirds (63%) said they are both porting new applications using Wasm and migrating existing workloads, compared to 37% who said they were creating new applications only. The primary reason cited for migrating workloads to Wasm was application portability (78%), better security (77%) and improved runtime performance (70%).

    Security ranks high because existing approaches to building applications rely on the aggregation of software components that tend to lack distinct boundaries between them. As a result, it becomes relatively simple for malware to infect all the components of an application. Wasm code runs in a sandboxed environment that isolates execution environments in a way that eliminates the ability of malware to laterally move across an application environment.

    Target platforms for Wasm applications include serverless computing frameworks (63%), edge platforms (54%) and the web (52%), the survey found.

    The biggest barriers to adoption were tools (46%) and lack of standards (45%). Just over half (56%) of respondents said Wasm developer tools are missing features and are difficult to use. A full 81% said they would like to see vendors provide full-featured and stable Wasm tools and projects.

    Matt Butcher, CEO of Fermyon, a provider of a platform for building Wasm applications, said that while survey respondents most likely represented more advanced developers, it’s clear Wasm momentum is starting to build. Much like JavaScript before it, Wasm is about to jump from the client to server-side application environments, he noted.

    Less clear is whether Wasm represents the next era of cloud-native computing or is simply another type of software artifact that will be employed to build applications alongside other artifacts such as containers.

    Wasm isn’t necessarily going to replace existing approaches for building applications any time soon, but it does provide an alternative to serverless computing frameworks for developing lighter-weight applications that run faster. Existing serverless computing frameworks typically use containers that are much slower to start up than Wasm code running natively on a platform, which accounts for why developers are looking to embrace the format to improve overall application performance.

    One way or another, more Wasm code will soon be finding its way into DevOps pipelines. The issue is determining how to incorporate it alongside all the other code moving through those pipelines.

  • Introduction to Cloud Custodian

    Introduction to Cloud Custodian

    Organizations continue to increase their footprint in the cloud. Yet, applying governance and enforcing policies across multiple cloud environments at scale is a challenging endeavor. Organizations typically have governance policies written in English, but translating them into enforceable code has historically required some custom workarounds. In the same vein as infrastructure-as-code (IaC), DevOps engineers now seek a similar GitOps approach to implement shared policies across cloud-based workloads.

    Cloud Custodian is one such toolset that can manage and enforce cloud policies in a standardized format. Using Cloud Custodian, a Cloud Center of Excellence has the building blocks to create policies for security governance, development guardrails and cloud cost optimizations. The free, open source project, which has been steadily evolving for years, recently gained its incubation status with the Cloud Native Computing Foundation (CNCF) and to date boasts over 350 contributors on GitHub.

    I met with Kapil Thangavelu, Cloud Custodian creator and maintainer and CTO at Stacklet, to learn more about Cloud Custodian and what features we should anticipate from the project in the near future. Below, we’ll provide a comprehensive introduction to Cloud Custodian, exploring its history, how to use it and its core benefits. We’ll also analyze a few sample policies to showcase how Cloud Custodian functions in practice.

    What is Cloud Custodian?

    Cloud Custodian got its start as an internal tool developed within Capitol One toward the end of 2015, explains Thangavelu. At the time, engineers were grappling with the newfound realities of managing the cloud at scale and realized that having one-off ad-hoc policies across teams was causing friction. “It’s really hard to manage and enforce policies at scale,” says Thangavelu. So, engineers toyed with creating a more GitOps approach to policies around governance and security that could be run as an internal SaaS solution.

    Thus, Cloud Custodian was born. The YAML DSL tool allows you to easily define rules to manage cloud infrastructure. It can offer real-time enforcement and produce event-based responses. For example, you can create policies that require tags on resources or one that turns off instances during off-hours. Or, a policy could prevent a developer without the proper privileges from accidentally making an open load balancer. Cloud Custodian supports AWS, Azure, and GCP and policies are specified to a resource type, whether it’s EC2, ASG, Redshift, CosmosDB or a PubSub Topic.

    Thangavelu describes Cloud Custodian as a bunch of Lego bricks that developers can pick up to build guardrails in arbitrary ways. Instead of shipping with canned rulesets, “Cloud Custodian makes customization itself a first-class citizen,” he says. Capital One open sourced the project and contributed Cloud Custodian to CNCF in August 2020.

    Example Policies

    Cloud Custodian comes with a library of actions and filters. Let’s take a look at a few example policies across the three main cloud providers. But first, to install Cloud Custodian from PyPI, you can initiate the following command:

    $ python3 -m venv custodian
$ source custodian/bin/activate
(custodian) $ pip install c7n

    Let’s consider an example policy that would improve security hygiene on AWS. Below is one such policy that creates a CloudWatch Event to be triggered anytime a user logs in from an invalid IP address. This could be used to ping a security administrator to investigate. With this rule, it’s also possible to turn on an auto-remediation function.

    policies:

  - name: invalid-ip-address-login-detected
    resource: account
    description: |
      Notifies on invalid external IP console logins
    mode:
       type: cloudtrail
       events:
          - ConsoleLogin
    filters:
      - not:
          - type: event
            key: 'detail.sourceIPAddress'
            value: |
               '^((158\.103\.|142\.179\.|187\.39\.)([01]?[0-9]?[0-9]|2[0-4][0-9]|25[0-5])
               \.([01]?[0-9]?[0-9]|2[0-4][0-9]|25[0-5]))|(12\.([01]?[0-9]?[0-9]|2[0-4][0-9]|25[0-5])
               \.([01]?[0-9]?[0-9]|2[0-4][0-9]|25[0-5])\.([01]?[0-9]?[0-9]|2[0-4][0-9]|25[0-5]))$'
            op: regex
    actions:
      - type: notify
        template: default.html
        priority_header: 1
        subject: "Login From Invalid IP Detected - [custodian {{ account }} - {{ region }}]"
        violation_desc: "A User Has Logged In Externally From A Invalid IP Address Outside The Company's Range:"
        action_desc: |
            "Please investigate and revoke the invalid session along
            with any other restrictive actions if appropriate"
        to:
          - CloudAdmins@Company.com
          - SecurityTeam@Company.com
        transport:
          type: sqs
          queue: https://sqs.us-east-1.amazonaws.com/12345678900/cloud-custodian-mailer
          region: us-east-1

    Next, let’s consider a policy that would add some degree of governance for developers working on Azure. One simple yet popular policy involves tagging the ownership details to individual resources. This policy, therefore, will tag all resource groups with the creator’s email address:

    policies:
  - name: azure-auto-tag-creator-resource-groups
    resource: azure.resourcegroup
    description: |
      Tag all existing resource groups with the 'CreatorEmail' tag; looking up to 10 days prior.
    actions:
     - type: auto-tag-user
       tag: CreatorEmail
       days: 10

    Finally, let’s consider a policy that could aid in cost optimization on GCP. Below is a sample policy to enforce minimal CPU utilization for autoscalers on GCP. Implementing such a policy could help avoid underutilization to combat rising cloud costs.

    vars:
  min-utilization-target: &min-utilization-target 0.8
policies:
  - name: gcp-autoscalers-enforced
    resource: gcp.autoscaler
    mode:
      type: gcp-audit
      methods:
        - v1.compute.autoscalers.insert
    filters:
      - type: value
        key: autoscalingPolicy.cpuUtilization.utilizationTarget
        op: less-than
        value: *min-utilization-target
    actions:
      - type: set
        cpuUtilization:
          utilizationTarget: 0.8

    Benefits of Cloud Custodian

    By automating a lot of the tedious policy management away, Cloud Custodian could reduce risk and accidents through more streamlined cloud governance. “It solves the natural problems when infrastructure is in everyone’s head,” says Thangavelu. By aggregating ad-hoc scripts and unifying policies across an organization, you could immediately instigate new rules without manually reminding all members of an organization, which could take years.

    For those familiar with Open Policy Agent (OPA), you may notice some overlap in the objectives, as both are engines for enacting cloud-native policies. Compared to OPA, Cloud Custodian has some developer experience perks. For one, you don’t have to use Rego, as the policies are written in YAML, which is a familiar configuration language for DevOps engineers. Cloud Custodian uses abstractions on event runtimes for each cloud provider. Furthermore, compared to OPA, you don’t need to bind the engine for a particular problem domain, says Thangavelu, as Cloud Custodian is specifically bounded to cloud governance and management.

    Cloud Custodian is also battle-tested. Many big names are using it in production, including Capital One, HBO Max, Intuit Inc, JP Morgan Chase & Co, Siemens and Zapier. Across its adoption, the tool tends to either be utilized by a Center of Cloud Excellence, security teams to conduct reporting or reveal real-time incidents or the CFO to direct cost optimizations.

    Future of Cloud Custodian

    Cloud Custodian maintainers describe the project as a consistent firehouse. Since the project tracks changes in all cloud provider resources in real-time, it’s quite a dynamic toolset. In addition to monthly updates, users can expect many additional updates on the horizon, including support for Tencent Cloud as well as additional Kubernetes support. Additional work is being done around shift-left capabilities to meet the developer where they are, says Thangavelu.

    For more information, check out the Quickstart Guide to Cloud Custodian, or view the project roadmap here.

  • Why Object Storage is Best for Cloud-Native Apps

    Why Object Storage is Best for Cloud-Native Apps

    A crucial question that plagues cloud application developers is, “What kind of storage should we use for our app?” Unlike other choices like compute runtimes—Lambda/serverless, containers or virtual machines—data storage choice is highly sticky and makes future application improvements and migrations much harder.

    All three hyperscalers have storage services that present block, file and object-based data access. Each of these storage services are mature and offer different advantages, making the choice even harder. Though block and file-based storage has existed for multiple decades, in this article we will illustrate some key differentiators that should make object storage your default storage choice for new applications written in the cloud.

    Storage Type/Cloud Provider Google Cloud Amazon Web Services Microsoft Azure
    Block Persistent Disk Elastic Block Storage Disk Storage
    File Filestore Elastic File System Files
    Object Cloud Storage Simple Storage Service (S3) Blob Storage

    Easy Scaling

    Scalability is an important requirement for most cloud applications. It is expected that horizontally increasing the amount of compute power available to an application increases its ability to process requests, users, etc. to handle peak workload. Most cloud providers also make it easy to scale up compute resources to meet peak demand. As compute resources are scaled,
    block and file-based storage need to be mounted/attached to the new compute instances.

    However, a cursory search will show that these operations can fail or even hang indefinitely for multiple reasons. They also are often hard to debug. The other issue with using file and block storage solutions is that teardown of the compute instance may fail or hang for the same reasons. These issues immediately negate the application’s ability to scale freely as required. This is, however, not an issue with object-based storage, since there is no mount step involved. Your object storage is instantly accessible to the newly-created compute instances.

    Sharing and Consistency

    Data sharing and consistency are where object storage really shines compared to other storage types. In both block and file-based data storage, one instance of an application can end up seeing partial data written by another instance. Application developers end up having to use persistent locks to get around this issue. However, such schemes come with their own sets of challenges: Performance, correctness, etc. Persistent locks end up making an application severely complicated; I have seen even experienced storage engineers make mistakes while using persistent locks. Object storage avoids this problem by not exposing partially-written objects or objects actively being written. Also, note that objects are typically immutable, so once written they can only be overwritten as a whole and not in parts. This means updating data requires expensive read-modify-write cycles. However, most cloud providers help avoid these additional reads via special APIs that can create an object from portions of an existing object like GCS compose, Azure’s put page blob and AWS multipart upload.

    Data Protection

    Errors are bound to happen during application development or rollouts. These errors can end up impacting critical data and potentially disrupt normal application operations. This is why it is essential to have some sort of backup/snapshots configured on the storage that you use.

    Though most storage services have some form of backup/snapshot mechanism, most don’t make it very easy to configure or restore from them (that is, both require multiple steps or the involvement of a cloud administrator). All cloud object services support native data/object versioning capabilities which are extremely easy to enable. So, basically anytime an application updates and/or deletes, the object storage service preserves the older copy of the data. In case an older copy of the data needs to be restored, you can just read the old version and write it as the new object. The careful reader might see that if an application writes/deletes data often, there may be a lot of older versions of the data left behind. One might think these would be hard to identify and remove when not needed. However, all cloud providers support policy-based data life cycle management (see next section) so you can set up policies to delete unnecessary copies. Note that object versioning also provides an excellent defense against ransomware attacks.

    Policy-Based Data Life Cycle Management

    The amount of data being generated by applications is only going to keep increasing with each passing year. This is the reason all the cloud providers support policy-based data life cycle management for their object storage services. Even if you don’t expect to use more than a few gigabytes of data, policy-based life cycle management can help you keep your storage costs in
    check and can help reduce your code complexity around handling application crashes. Policy-based life cycle management especially comes in handy when you or your cloud admin decide to enable features like object versioning and object holds for data protection and compliance reasons. These policies are very simple to set up and can easily be customized to the needs of
    the individual organizations/applications/developers requirements.

    Conclusion

    As one can see from the above, object storage services have been built to enable the development of simple and scalable applications. So, if you are writing a new application from scratch, choose object-based storage to keep your applications simple and easy to maintain.

  • How to Go Multi-Cloud: A Simple Three-Step Approach

    How to Go Multi-Cloud: A Simple Three-Step Approach

    Odds are your business already uses cloud technology for automation, cybersecurity, IT infrastructure updates and other purposes. But you don’t have to stick to a single cloud server or provider. In fact, you can take advantage of multi-cloud strategies for your organization and reap the benefits for years to come.

    That said, multi-cloud adoption and optimization can be tricky, especially if you don’t have a plan in mind from the get-go. Today, let’s break down a simplistic yet effective three-step approach to go multi-cloud without running into major problems.

    Step One: Map Your Cloud Zoning Policy

    Your first major step should be to map out your cloud zoning policy and plan. In a nutshell, the cloud zoning choices you make can impact your commitments, costs, and even the eventual functionality of the multi-cloud setup.

    By mapping out your clouds, you also map out which processes and applications will run on each individual cloud server or provider. In essence, you decide what gets locked into one cloud, what data has to be ported between clouds and what applications must run on multiple clouds simultaneously.

    Need an example? A cloud zoning policy might cover whether you keep your web surfing or data analytics on separate cloud providers or on the same cloud servers. 

    You should map your cloud zoning policy regardless of whether you do the setup or hire a service. In the latter case, offering a read-to-go zoning map will make the service’s job easier and minimize the possibility of mistakes and/or hiccups.

    How to Determine Optimal Cloud Zoning

    Naturally, figuring out how to optimize cloud zoning can be difficult. The best way to do this is to determine your special focus areas. Alternatively, consider how you want your multi-cloud strategy to best benefit your organization.

    Say that you want to guarantee 100% uptime for your customers or visitors, even if your service goes down or if you experience a data breach. In this case, you can set up your cloud zoning strategy to balance the data load across multiple clouds at the same time.

    Or say that you want to make sure your users have global availability 24/7/365. In that case, you can set your cloud zoning policies to ensure that users can access your data or websites from anywhere around the globe at any point.

    Essentially, figure out what matters most to your organization–and what you want the most from multi-cloud optimization–and zone your cloud applications and policies accordingly.

    Step Two: Architect the Multi-Cloud Environment

    The next major step is to architect your multi-cloud environment. This involves looking at the high-level design of the environment and creating a rock-solid foundation for multi-cloud servers.

    At this stage, you should have at least some idea of how your business will grow and how the multi-cloud environment will assist with its scalability and resource needs. You need to know:

    • Where your data science and machine learning applications need to be
    • Where your product application serves from
    • Where your data warehousing takes place
    • Where cloud security is run from
    • How each of those functions grows alongside the others

    In the architecture phase of a multi-cloud setup, you need to figure out which projects and apps are so-called “cloud agnostic.” Cloud agnostic projects and applications don’t have to be portable; because of this, they can lean on managed services or proprietary IT infrastructure from your organization.

    How to Set Up an Ideal Multi-Cloud Environment

    To reap the most benefits out of a multi-cloud environment, you should adopt a containerized and flexible approach. Not only is this cost advantageous, it also allows you to set up your multi-cloud environment with as much versatility as possible.

    If you architect or plan your multi-cloud environment so it is containerized and flexible, you can work with practically any infrastructure-as-a-service (IaaS) provider. Therefore, you can transition to different cloud hosts or service providers whenever necessary according to your budget or other factors.

    To do this, be sure to perform heavy forecasting. You need to know how much data storage your computing will require, the database utilization your organization will need, the number of computer nodes you’ll likely require and so on.

    As a side bonus, containerization in your multi-cloud setup means that if one server or process goes down, the others are less likely to fail from a cascade effect as well.

    Step Three: Prep for Contracts and Forecast Costs

    The last step in going multi-cloud for your organization is handling the financial aspect of the process. You need to prepare for contracts and commitments alongside forecasting costs.

    Forecasting is critical in this phase because you’ll prepare for contracts and select different cloud services. Because of this, you need to understand how flexible your budget is relative to your infrastructure requirements.

    Specifically, you’ll need to match the costs to every multi-cloud forecast and build your overall resource and money consumption budget. Basically, you need to know:

    • How much you’ll likely spend on multi-cloud processing and services
    • Whether you need certain features, such as website monitoring tools like Visualping
    • Whether your budget can take that forecasted strain

    If the answer to the third question is “no,” you may need to go with a more affordable service or adjust your multi-cloud environment architecture and zoning policies. By forecasting costs ahead of time, you won’t run into a disaster scenario where you already have your multi-caught environment up and running but can’t pay for it, forcing you to scramble as a result.

    Minimize Commitment Risk

    Luckily, there are ways to minimize commitment risk and avoid budgetary disasters. For example, you can use commitment buy-back guarantees, or you can use flexible commitment options like that from AWS. These include computing savings plans, which consume cloud resources in any region and which result in very low savings rates.

    Of course, you can and should also practice very careful budgeting and accounting. Once you ensure that your commitment costs and savings are attributed to the right services, server resources, applications, etc., you’ll have a better idea of how much money and other resources you really need and avoid over-committing to a provider that is too expensive.

    Then you can further avoid commitment risk when planning your migrations of applications between different vendors carefully. Budgetary costs can skyrocket if migrating applications and data between vendors take longer or result in unexpected complications. Therefore, you should make sure that your migrations go quickly and easily or that a cloud service provider offers assistance during this phase (possibly as part of a deal to get you to sign with them in the first place).

    Wrapping Up

    As you can see, you can transition your organization to multi-cloud in a matter of months. But even if you follow the above steps perfectly, remember that you won’t optimize your commitments, performance and costs up to 100%. Still, you can prep your organization for long-term success and multi-cloud benefits with the right preparation and planning.

    With the right cloud services provider, you’ll gain additional assistance and support throughout this process and be able to take advantage of the extra resources from a multi-cloud setup quickly and easily.