How can you scale your hybrid cloud architecture to meet changing demands?

In my experience , to scale your hybrid workloads for an organization requires diligence architecture review of your both public cloud & private cloud infrastructure. Here are the thinks one can do to scale hybrid cloud environment. 1. Look at the current usage of public & private cloud by monitoring, logging & analytics capabilities 2. Align with customer expectations to understand and see if we can scale the hybrid cloud 3. Measure the reliability, performance, scalability metrics with the current set up and identify the gap where we need to scale such as computer or network or databases or storage . 4. Identify the value proposition of scaling hybrid cloud by working closely with cross functional teams both internally &externally

In my experience, out of many reasons choosing a hybrid cloud is first step towards cloud computing specially if you already have an established infrastructure. By design, many Cloud vendors provide the critical functionality of elasticity at all levels pretty much in real time. It simplifies the over all planning process to Architects to focus on preparing their application to be Cloud ready for such changes. Having a baseline in terms of CPU, Storage and Network helps to set a starting point. Depending on the workload pattern lower and upper boundaries can be defined to optimize the costs.

Before you start, remember to ask yourself, why are we choosing hybrid cloud architecture in the first place ? Most often I the answer would be, "I Dont Know" and if your answer is also the same then just drop this idea and trust me there is nothing your will be missing out on. However If you are among the lucky few, who have exisitng infra or private cloud to be gatewayed with a service provider then consider the following: 1. Have a service mesh layer that can balance the load 2. Based on the ratio, define your auto-sclaing limits 3. Remeber no two vendor will sclae at the same speed or cost 4. Use analytics to assess your workload history based on peak load, performance and letency

Look at the SLAs needed for the end users and work backwards to assess resources. From there you can break down into smaller modules and pick your cloud tool based on compliance, governance and risk

Ideal scaling of infrastructure is mix of many things working in unison for example - no hard coding, use minimum application or OS packages if possible and remove unwanted modules, packages. Scaling up and down has to go with zero or near zero downtime. Application must not be monolithic which hampers user experience. Scaling must be automated using well tested tools. Try to use open source as much as possible.

Consider all options for scaling hybrid cloud architecture, including understanding business use cases to predict usage demands and automating scaling to meet those demands seamlessly

First of all slection crtiteria of which application goes to to which platform (onprem/offprem) is important. Design and Re-design of application is important to get a return of investment. Network architecture to be roboust to get maximum performace benefits with tracability.

Assessing your workload is must even if you are not planning to migrate/scale to cloud or no cloud. If you cant measure if then you cant fix it. Once you have a picture of your current landscape then only you can pick your load which is right candidate for cloud. Then you can decide on different factors which one to be put in cloud or not. As cloud provide flexibility but it comes at a cost. So choose your battle wisely.

Firstly, it enables informed decisions about workload distribution between public and private clouds. Different workloads have varying requirements Secondly, this assessment helps in resource allocation. It's not just about ensuring sufficient resources but also about optimizing them. A clear understanding of workload demands allows for a balanced and cost-effective resource distribution. Moreover, security and compliance are paramount. Each workload might have different regulatory requirements and risk profiles. Finally, this assessment is not a one-time task but an ongoing process. Workload requirements can change due to business growth, technological advancements, or evolving market dynamics.

Choose the right cloud service model Once you've assessed your workload requirements, choose the appropriate cloud service model for each workload. Consider factors like performance, security, compliance, and scalability. Public clouds offer flexibility and scalability, while private clouds provide control and security. Hybrid clouds combine both models to leverage benefits from each. Selecting the right model ensures efficient resource utilization and optimal performance for your workloads. Regularly review and adjust your model choices based on evolving workload needs and cloud advancements.

Identifying the correct scaling method—vertical (scaling up/down) or horizontal (scaling out/in)—is pivotal for hybrid cloud environments. Vertical scaling, which involves adding more power (CPU, RAM) to your existing machines, is simpler but has physical limits. Horizontal scaling, adding more machines to your pool, offers greater flexibility and aligns well with cloud-native designs. Considerations include the nature of the applications (stateless vs. stateful), the ease of implementation, and potential downtime. For hybrid environments, ensure compatibility and seamless integration between on-premises and cloud resources during scaling operations.

Sobre AWS A escala vertical deve considerar custos sobre o aumento da performance do servidor ou até mesmo, um upgrade de máquina. Outra premissa seria se a arquitetura possui licenças legadas ou vitalícias. A escala horizontal possui dois aliados inseparáveis, o primeiro, o Auto Scaling que, quando bem arquitetado, mantém latência, escalabilidade e agilidade. E o segundo, o Load Balancer, que distribui as solicitações entre os servidores do Auto Scaling de forma ordenada evitando sobrecarga. Outra equipe bacana dos bastidores são o SNS e o SQS. A mensageria de filas e mensagens, respectivamente, que vai manter o fluxo de solicitações na fila, e manter a comunicação de alertas e status da orquestração.

Optimize workload distribution Effectively distribute workloads between the public and private cloud based on their specific requirements and characteristics. Critical, sensitive, or resource-intensive workloads may be better suited for a private cloud, while scalable, non-sensitive workloads can benefit from the elasticity of public cloud resources. Regularly reassess and adjust workload distribution to align with evolving business needs, ensuring optimal performance, security, and cost efficiency across the hybrid cloud environment.

When scaling your hybrid cloud, consider two primary methods: vertical and horizontal. Vertical scaling adjusts resources within a single server swiftly but has limitations. Horizontal scaling adds or removes servers for flexibility but demands more configuration. Choose the method aligning with your workload's characteristics and objectives.

In my opinion, two things. Scaling is an art, and software architecture is the primary key. You should consider that scaling is possible with infrastructure tools because, basically, there are many options. Imagine, it's not only possible to scale based on CPU or RAM. Therefore, the more you know about your workloads and software design, the better the balance between performance, availability, and costs will be.

In general, metrics should be gathered first based on minimum, average, peak load. The architectural component with weakest performance can then be looked at and improved upon. Following that, the need for scaling and type (vertical or horizontal) can then be determined. Scaling type depends on various factors be it location, bandwidth availability or cost. For e.g. the cloud components can be auto scaled but it would not be helpful if the access is from a remote location with low network bandwidth. Hence when it comes to hybrid, practical limitations like the above needs to be considered.

First thing choose the right scaling method for your infrastructure, Vertical Scaling(Upgrading individual resources), Horizontal Scaling (adding more instances), Autoscaling, Scheduled scaling ( predefined actions on a schedule), application layer scaling, database scaling.

In my experience I have learn that the scaling method relies on the shared responsability policies that the Hyperscalers offer and should be evaluated before the placement or movement of the workloads to the Public Cloud, avoiding hidden or not planned costs and compliance issues.

Scaling of an application or platform needs thorough review and understanding of the underline infrastructure. A well designed and flexible platform architecture can help to meet applications (de)scaling demand while keeping the operations and OPEX cost under control.

Make sure to put the right tasks in the right clouds! ✅If the job needs extra protection or is super important, use a private cloud. ✅But if it's something that can grow and doesn't need super protection, use the public cloud. ✅Keep checking and changing where tasks go to match how your business is growing. ✅ This way, everything works great, stays safe, and doesn't cost too much in your mix of clouds! 🌨️

The beauty of cloud native is that it provides scaling features in the most agile way possible. Scaling such as horizontal pod autoscalers and lifecycle through auto schedulers are just few of the available avenues that can help organizations to reduce OPEX overtime. Private cloud might become a new trend soon however network dependencies such as on egress might pose as a threat to actualize the real benefits that it can provide overtime unless selective workload repatriation becomes the way forward.

In my experience the low hanging fruit of having hybrid cloud is automating basic scaling/descaling activities. If your operation is mostly 9-5, M-F, scaling back resources during non-work times is a must to manage costs effectively and take advantage of cloud's pricing model. Automating scaling processes is the best way to gain these benefits consistently and net the financial benefits over time. Also, one less thing your Cloud Ops team has to remember ;~)

Leverage predictive scaling Implement predictive scaling strategies to anticipate changes in workload demand and proactively adjust resources. Analyze historical data, user patterns, and seasonal trends to forecast future resource needs. By leveraging predictive scaling, you can dynamically scale resources before a surge in demand, ensuring optimal performance and minimizing response time. This approach enhances efficiency, reduces costs, and provides a seamless experience for users by preventing resource constraints during peak periods.

Automation in scaling is a key enabler for responsiveness and efficiency in hybrid cloud architectures. Implement tools like AWS Auto Scaling, Azure Scale Sets, or third-party solutions to automatically adjust resources based on predefined policies and real-time analytics. This automation should encompass both cloud and on-premises resources, maintaining a balance and ensuring uninterrupted service. The automation strategy should include triggers based on specific metrics (like CPU utilization, memory usage, or network traffic), time-based scaling for predictable workload changes, and the ability to scale down to optimize costs.

Hybrid cloud's key advantage lies in its on-demand scalability without manual intervention. To achieve this, automate scaling processes using tools like cloud orchestration, automation, and cloud-native services. Cloud orchestration coordinates scaling across public and private resources, automation executes tasks efficiently, and cloud-native services leverage the built-in scaling features of your cloud provider. This automation ensures a dynamic and responsive hybrid cloud infrastructure.

It is also essential to consider the scaling latency requirements of your workloads to determine if an auto scaling solution scales fast enough to meet the rate of growth. In certain usecases like migrating an existing high scale system to another, pre-scaling during the migration maybe a more measured approach to minimize surprises. It is a reasonable two way door as you can always enable autoscaling post-migration once the runtime demands and behavior of the new system is clearer. On the other hand if you’re launching a new system and expect organic growth, starting out with a suitable auto scaling setting based on load tests of forecasted demands would be sufficient - assuming the autoscaling system’s latency suits your usecase.

Make your system always the right size by using automation to scale it up or down based on your needs. ✅Coud service providers, like Amazon Web Services , GCP and Azure provides tools for this – like Auto Scaling and Virtual Machine Scale Sets. ✅If you prefer open-source options then go for Kubernetes or Docker Swarm which can also help you in automating the scaling process. Hence, you don't have to worry about modifying your infrastructure manually, making things more efficient and convenient. 🔄

Less often considered is the anomalous scaling requirements which can occur resultant of: - Campaigns - Cyber attacks - Unexpected immediate demand (e.g. Covid against health services) Make sure you have the means to adapt to the pressure, in some cases the average monthly elasticity requirements may be met easily through good autoscaling design - but when these anomalous events take place limitations can be found causing service disruption. Think about resource and subscription quota limits, ultimate service capacity and redundancy design considerations. Cloud capability is not "just a checkbox" good service design is continuous so make sure that continuous review and improvement is built into your teams modus operandi

In my experience, one of the most challenging tasks is change the perception of cloud (and generally any IT) automation as a tool that eliminates jobs, it is not oriented to replace people, the main purpose is to augment human capabilities. Automation actually frees up our IT staff to focus on larger key business areas. This is the great paradox of automation: while you become efficient with automation, human engagement becomes more important but infrequent, and a high quality IT staff is required. With cloud automation we improve efficiency and productivity while we are preserving resources to work on strategic goals like increasing customer satisfaction.

Automating the scaling process can help you ensure that your infrastructure is always scaled to meet your needs. There are a number of tools that can help you automate the scaling process. Most cloud providers offer tools that can help you automate the scaling process. For example, Amazon Web Services offers Auto Scaling and Azure offers Virtual Machine Scale Sets. There are also a number of open-source tools that can help you automate the scaling process, such as Kubernetes and Docker Swarm.

Network performance can be a bottleneck when scaling hybrid cloud architecture. You need to make sure that your network is able to handle the increased traffic. There are a number of things you can do to optimize network performance A high-bandwidth network will be able to handle the increased traffic without experiencing performance problems. A low-latency network will ensure that there is minimal delay between your on-premises resources and your cloud resources. A reliable network will ensure that your infrastructure is always available. In a perfect world, you would have all three.

Based on the experience of some implementations, the architecture must be designed and implemented knowing that it is possible that in certain scenarios an instant response to each request will not be obtained. This delay or latency could be milliseconds or seconds, so make sure applications and platforms consider these scenarios

Optimizing network performance is critical in scaling hybrid cloud architectures. This involves ensuring sufficient bandwidth and low latency between on-premises data centers and cloud services. Utilize advanced networking services offered by cloud providers, like AWS Direct Connect or Azure ExpressRoute, to establish dedicated, private connections to the cloud. Implementing robust content delivery networks (CDNs) can also reduce latency and improve user experience by caching content closer to users. Regularly review network configurations and rules to ensure they are optimized for both current and scaled states, considering aspects like load balancing, network security, and traffic management.

Regularly review and optimize your scaling strategy To ensure the ongoing effectiveness of your hybrid cloud scaling, conduct regular reviews and optimizations. Analyze performance metrics, user feedback, and cost data to identify areas for improvement. Adjust your scaling policies, configurations, and resource allocations based on evolving workload patterns and business requirements. This iterative approach allows you to fine-tune your hybrid cloud scaling strategy, improving efficiency, minimizing costs, and enhancing overall performance.

Spot on, what Dave mentioned. Adding my perspective to it - If you choose cloud centric IaaS networking as your hybrid cloud networking approach, then instead of becoming a bottleneck, the network can actually become a facilitator of your hybrid cloud scaling.

Keep checking and making your hybrid cloud work efficiently. ✅ Look at how well it's doing, what users say, and how much it costs. ✅Find ways to make it even better by changing how it scales, sets up, and uses resources. ✅Keep doing this as your work changes, so your hybrid cloud always works really well, also doesn't cost too much and does a great job. It's like giving your hybrid cloud a regular tune-up to keep it running smoothly! 🔄💻

Um die Netzwerkleistung in einer Hybrid-Cloud zu optimieren, ist Cloud Load Balancing für eine effiziente Verteilung des Datenverkehrs entscheidend. Ein Cloud VPN sichert die Netzwerkkommunikation zwischen Cloud-Umgebungen und dem Unternehmensnetzwerk. Cloud CDN verbessert die Zugriffsgeschwindigkeit, indem Inhalte näher an Endbenutzern gespeichert werden. Wichtig sind kontinuierliches Netzwerk-Monitoring und -Analyse zur frühzeitigen Erkennung und Anpassung der Netzwerkkapazität. Bandbreitenmanagement und regelmäßige Überprüfungen der Netzwerkarchitektur tragen zur optimalen Leistung bei. Diese Maßnahmen gewährleisten eine hohe Netzwerkleistung und -effizienz.

Network performance is also an important generator of hidden costs and should be evaluated before moving the workloads to the Public Cloud or implementing an Hybrid Cloud operating model. The velocity, availability and reliability of networking will increase, also the traffic and should be analyzed. Network security is also an important variable to understand and cost.

This is the same as the first point, you need to monitor the things that will affect your ability to scale and those that affect the way the attributes you care about most change. Network in many cases is one of them, but I/O pressure on disk, memory usage are also up there. I don't understand why you are picking out network in particular as it is one of the things that I would keep an eye as part of any hybrid cloud environment.

For on premise servers always try to host the cloud counterpart (if any) in a datacenter which is geographically closest. Also try to localise the data by hosting multiple replica of your cloud service in various locations. For example if you have operation in Australia and in US. It is better to keep two replicas of your service in two places. Another option can be CDN in case you have static data.

Continuous testing and monitoring are essential for successful scaling in hybrid cloud environments. Implement comprehensive monitoring solutions that provide real-time visibility into both cloud and on-premises resources. This should include tracking key performance indicators (KPIs) like response times, error rates, and resource utilization. Regularly conduct load testing and simulations to validate the effectiveness of scaling strategies under various conditions. Use these insights to refine scaling policies and thresholds. Monitoring should also extend to cost management, ensuring that the scaling strategy is cost-effective and aligns with budgetary constraints.

Define your workload requirements Before scaling your hybrid cloud, it's crucial to understand your workload requirements, considering factors like performance, availability, security, and compliance. Utilize cloud monitoring, analytics, and cost management tools to assess and optimize workload performance and costs. This evaluation guides decisions on which workloads are suitable for public or private cloud and determines the necessary resource allocations.

Whenever a cloud service is provisioned it is always a good idea to test it under maximum load and see how it behaves. You can also hit your service with loads from various geographical location and monitor its response time etc. After the service is deployed in prod it is absolutely critical that the service is giving out enough telemetry to keep an eye on how it is performing. Also please make sure to configure proper alerts so that in case of any emergencies the respective people are notified immediately.

Monitoring is the new testing! Put workloads into production with different infrastructure configurations. Observe the results and optimize

After modifying the hybrid cloud, it's important to check if things are working as expected. ✅ As a data engineer, testing and monitoring become your important tools. ✅ Use cloud testing to pretend different situations and see how your hybrid cloud responds – like checking how fast it works and if there are errors. ✅ Keep things safe and in line with rules by using cloud auditing. Also, pull feedback from users with cloud feedback to make sure they're happy. ✅ By doing this, you make sure your hybrid cloud not only meets your goals but also keeps your users satisfied and your data secure. 📊

You will always need to test, creating load tests is often overlooked but from a scaling perspective one of the most crucial. But as mentioned previously you need to understand two things 1) What are the expectations from the application (i.e. response times to concurrent users, max no. of concurrent users, what response means etc) 2) Monitor all the things that give you insight into (1) above and ensure you understand how those behaviours change under load

Beim Testen und Überwachen der Skalierung in Hybrid Clouds sind Herausforderungen wie Komplexität, dynamische Workloads, Tool-Integration, SLA-Einhaltung und Sicherheitsmanagement zu bewältigen. Effektive Maßnahmen umfassen den Einsatz automatisierter Überwachungstools für kontinuierliche Leistungsbeobachtung, Durchführung regelmäßiger Leistungstests unter verschiedenen Lastbedingungen, Entwicklung einer umfassenden Teststrategie, Einrichtung von Feedback-Schleifen zur Nutzerfeedback-Analyse, ständige Überwachung der SLA-Einhaltung und regelmäßige Sicherheitsaudits. Diese Schritte gewährleisten eine effiziente Skalierung und das Erreichen der Leistungsziele in der Hybrid Cloud.

Concluding the scaling process involves rigorous testing and continuous monitoring to validate that your hybrid cloud environment meets anticipated outcomes and complies with service level agreements (SLAs). Employ tools like cloud monitoring tools for simulating diverse load scenarios, measuring response time, throughput, and error rates, cloud auditing to verify compliance and security, and cloud feedback to collect and analyze user satisfaction and feedback, ensuring ongoing performance optimization and alignment with business objectives.

Also - consolidation of logs - third party for handling pieces - resistance to change - education and training - devops agile processes are Lacking in these scenarios so learning sessions are important

Also - consolidation of logs - third party for handling pieces - resistance to change - education and training - devops agile processes are Lacking in these scenarios so learning sessions are important

Whether its cloud/onprem/hybrid, build systems, applications following key architecture principles - loose coupling, modularity. These aspects gives ability to plug and play as demand changes both at the application and Infrastructure level. As systems grow, complexity in monitoring and maintaining those systems increases. The key is to have proper observability and monitoring coded, deployed as part of the delivery cycle , and processes to proactively address the issues as systems goes live.

Right sizing of workload is very important. Data owners and architects should know what data needs to be in cloud and what type of data should remain on pirm.

Post-migration, success hinges on robust change management. 🌟 Embrace best practices with RapidScale and LogicWorks: assess impacts, communicate effectively, and train your team. Ensure seamless transition and maintain operational excellence. Change smart, not just fast! 💡 #ChangeManagement #PostMigration #RapidScale #LogicWorks #OperationalExcellence

To scale your hybrid cloud architecture effectively, start by assessing workload requirements, understanding performance, availability, security, and compliance needs. Choose the appropriate scaling method—vertical or horizontal—based on workload characteristics. Automate scaling processes using cloud orchestration and automation tools for efficient, on-demand adjustments. Optimize network performance with tools like cloud load balancing and VPN. Finally, test and monitor scaling results to ensure they meet expectations and adhere to service level agreements (SLAs).

It's not only about technology, it is about the transformation of work and team organization itself. Scope the change large enough, including *how* you use cloud technology. Without DevOps best practices like: 1. continuous integration, 2. service-scoped mixed teams ("you build it, you run it") with technology autonomy and 3. a truly agile development culture, the cloud loses much of its benefits. Therefore, the technology provides the foundation, but transforming the organization yields the return on investment. That requires – when scaling – to roll out appropriate cloud engineering skills across all teams which are often blocking adoption.

Hybrid cloud architecture is a popular approach to cloud computing that combines the benefits of public and private cloud environments. To build a hybrid cloud architecture, organizations must first determine their workload requirements, including factors like performance, security, and scalability. Once these requirements are established, the organization can then choose the right cloud providers and integration tools to ensure a seamless transition between cloud environments. Finally, developing a disaster recovery plan is critical to maintaining data integrity and workload availability. By carefully planning and considering these factors, organizations can create a hybrid cloud architecture that meets their unique needs.

The topic is around scaling hybrid cloud arch. While the principles for scaling in cloud are public information now; for a large enterprise, applications that are stable with a pretty decent tech stack, do not foresee volume increases / major feature development should be prioritized lower in the move from private to public cloud. Vertical scaling is not a bad approach to consider at all while the cloud apps scale better horizontally. For 'an' application deployed across public and private cloud, a common orchestration tool for the containers is THE BIGGEST factor in terms of scaling (e.g., use ROSA if you are private cloud on cloud red and public on AWS).

One key benefit of Hybrid design is found in assurity, reliability and redundancy of service. There will always be a trade-off between operational efficiency, performance, RPO, RTO, and cost, make sure that all aspects are considered against the value of availability/performance of the service pre-design. This begins with clear classification of the business value and criticality of each service, again strategy and planning is key to cloud and hybrid adoption.

Embrace cloud-native technologies & architectures, such as containers & microservices. With containerization & orchestration platforms like Kubernetes, you can efficiently deploy, manage, & scale your applications, ensuring optimal performance & resource utilization. Unlock greater scalability through hybrid cloud bursting. This enables you to leverage additional resources from public cloud providers during peak demand periods, without burdening your private cloud infrastructure. By dynamically allocating resources, you can handle sudden spikes in demand while avoiding overprovisioning. Hybrid cloud bursting empowers you to seamlessly scale your hybrid cloud architecture & ensure uninterrupted service delivery, even during peak usage.

10 Punkte für eine erfolgreiche Hybrid-Cloud Transformation Strategische Planung: Setzen Sie klare Ziele und erstellen Sie eine Roadmap. Workload-Analyse: Identifizieren Sie geeignete Workloads für die Cloud. Sicherheit und Compliance: Stellen Sie Datenschutz und Regelkonformität sicher. Robuste Netzwerkinfrastruktur: Gewährleisten Sie nahtlose Konnektivität. Skalierbarkeit und Flexibilität: Wählen Sie anpassungsfähige Lösungen. Kostenmanagement: Überwachen und kontrollieren Sie die Ausgaben. Mitarbeiterschulung: Bereiten Sie Ihr Team auf neue Technologien vor. Richtige Partnerwahl: Wählen Sie passende Cloud-Anbieter. Disaster Recovery-Pläne: Sichern Sie Betriebskontinuität. Innovation fördern: Nutzen Sie die Cloud für Geschäftsagilität.