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What advanced techniques can you use to scale your software with cloud deployment?

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Cloud deployment and scaling are essential skills for software engineers who want to deliver reliable, fast, and cost-effective applications. But how do you go beyond the basics and leverage advanced techniques to optimize your software performance and efficiency? In this article, we will explore some of the best practices and tools that can help you scale your software with cloud deployment.

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Kaan Turgut

Senior Cloud & Dev(Sec)Ops Engineer | Multi-Cloud Certified (Azure,Aws,Gcp,Oracle)| Tech-Blogger | AI Enthusiast | Muay…

1 Horizontal and vertical scaling

One of the first decisions you need to make when scaling your software is whether to use horizontal or vertical scaling, or a combination of both. Horizontal scaling means adding more servers or instances to handle the increased load, while vertical scaling means upgrading the resources of your existing servers or instances, such as CPU, memory, or disk space. Both methods have pros and cons, depending on your software architecture, workload, and budget. Horizontal scaling can improve fault tolerance, availability, and elasticity, but it also requires more management, coordination, and load balancing. Vertical scaling can boost performance and simplify maintenance, but it also has physical limits, higher costs, and potential downtime.

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Kaan Turgut

Senior Cloud & Dev(Sec)Ops Engineer | Multi-Cloud Certified (Azure,Aws,Gcp,Oracle)| Tech-Blogger | AI Enthusiast | Muay Thai Practitioner
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In cloud deployments, horizontal scaling adds more machines to your pool (like opening new checkout lines), while vertical scaling upgrades the machine's power (like a faster checkout line). Tools like Azure Autoscale help automate horizontal scaling. Meanwhile, selecting the right VM size in Azure can maximize vertical scaling benefits. Both strategies are key for managing varying workloads efficiently.

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2 Load balancing and auto-scaling

To distribute the traffic and workload among your servers or instances, you need to use load balancing and auto-scaling techniques. Load balancing is the process of routing the requests to the most appropriate server or instance, based on factors such as availability, capacity, and latency. Load balancing can improve responsiveness, throughput, and resilience of your software. Auto-scaling is the process of adjusting the number of servers or instances dynamically, based on the demand and predefined rules. Auto-scaling can optimize resource utilization, reduce costs, and handle spikes and drops in traffic. There are different types of load balancers and auto-scalers available in the cloud, such as application, network, or DNS-based load balancers, and threshold-based, schedule-based, or predictive auto-scalers.

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Kaan Turgut

Senior Cloud & Dev(Sec)Ops Engineer | Multi-Cloud Certified (Azure,Aws,Gcp,Oracle)| Tech-Blogger | AI Enthusiast | Muay Thai Practitioner
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During Black Friday, when online shopping hits peak levels, a retail website must run smoothly to capture maximum sales. This is where load balancers shine by spreading requests across several servers, ensuring no single server bears too much load and risking a crash. It's like having multiple cashiers in a store. For a social media app experiencing a viral event, say a celebrity endorsement, the user traffic can skyrocket unpredictably. Here, auto-scaling is critical—it automatically provisions extra resources, perhaps more virtual machines or computing power, to handle this influx, much like opening new lanes on a highway during rush hour. These tactics prevent revenue dips and uphold brand trust with reliable user experiences.

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3 Microservices and containers

Another advanced technique to scale your software with cloud deployment is to use microservices and containers. Microservices are small, independent, and loosely coupled components that perform specific functions and communicate via APIs. Containers are lightweight, isolated, and portable environments that run the microservices and their dependencies. By using microservices and containers, you can break down your software into manageable and scalable units, that can be deployed, updated, and scaled independently. Microservices and containers can also improve modularity, reliability, and agility of your software development and delivery. However, they also introduce complexity, overhead, and challenges in monitoring, testing, and debugging.

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Kaan Turgut

Senior Cloud & Dev(Sec)Ops Engineer | Multi-Cloud Certified (Azure,Aws,Gcp,Oracle)| Tech-Blogger | AI Enthusiast | Muay Thai Practitioner
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In the cloud, microservices architecture enables you to scale parts of your app independently. For example, an e-commerce site could scale its inventory service during a sale without affecting other services. Containers, like Docker, bundle each microservice with its own environment, making them easy to deploy and scale. Kubernetes then steps in to manage these containers, automatically adjusting resources in real-time, similar to hiring or reducing staff based on store traffic. This approach is key to maintaining performance during demand spikes or when conserving resources during downtimes.

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4 Serverless and event-driven architecture

Serverless and event-driven architecture are two related concepts that can help you scale your software with cloud deployment by reducing the need for servers and instances. Serverless means that you only pay for the execution time and resources of your code, rather than the provisioned servers or instances. Event-driven means that your code is triggered by events, such as HTTP requests, messages, or timers, rather than running continuously. By using serverless and event-driven architecture, you can delegate the scaling, load balancing, and fault tolerance to the cloud provider, and focus on the business logic and functionality of your software. Serverless and event-driven architecture can also enhance scalability, elasticity, and efficiency of your software. However, they also have limitations, such as cold starts, statelessness, and vendor lock-in.

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Kaan Turgut

Senior Cloud & Dev(Sec)Ops Engineer | Multi-Cloud Certified (Azure,Aws,Gcp,Oracle)| Tech-Blogger | AI Enthusiast | Muay Thai Practitioner
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In real-world scenarios, Netflix uses serverless architecture to handle billions of calls daily across its services. They utilize AWS Lambda for serverless operations, ensuring high availability and scalability without manual intervention. Similarly, The Guardian’s website leverages serverless to scale during high-traffic events, like elections, without downtime. Event-driven architecture plays a crucial role in enabling instant scale-up and scale-down, optimizing resource use and costs.

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5 Caching and content delivery network

Caching and content delivery network are two techniques that can help you scale your software with cloud deployment by improving the performance and availability of your data and content. Caching is the process of storing frequently accessed or computed data or content in a fast and nearby location, such as memory, disk, or edge servers. Content delivery network is a network of distributed servers that deliver data or content to the users based on their geographic location, origin server, and network conditions. By using caching and content delivery network, you can reduce the latency, bandwidth, and load of your software, and provide a better user experience. However, you also need to consider the consistency, freshness, and security of your data and content.

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Kaan Turgut

Senior Cloud & Dev(Sec)Ops Engineer | Multi-Cloud Certified (Azure,Aws,Gcp,Oracle)| Tech-Blogger | AI Enthusiast | Muay Thai Practitioner
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Scaling in the cloud is all about efficiency and speed. Implementing Amazon ElastiCache or Azure Cache for Redis can drastically reduce load on databases, especially for read-heavy applications. For content delivery, look at how Spotify uses Google Cloud CDN to serve music with low latency. Services like AWS CloudFront or Azure Frontdoor can distribute content across the globe, reducing load times—crucial for user experience. Netflix, for instance, uses CDNs to stream seamlessly worldwide. By caching content and offloading traffic to a CDN, you can ensure high availability and swift content delivery, even during demand spikes

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6 Monitoring and logging

The last advanced technique to scale your software with cloud deployment is to use monitoring and logging tools to track and analyze the performance, health, and behavior of your software. Monitoring is the process of collecting and visualizing metrics, such as CPU, memory, disk, network, response time, error rate, and availability of your software. Logging is the process of recording and storing events, such as requests, responses, errors, and exceptions of your software. By using monitoring and logging tools, you can identify and diagnose issues, optimize and troubleshoot your software, and gain insights and feedback. However, you also need to balance the granularity, frequency, and retention of your data, and choose the right tools for your needs.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Suresh Kumar Khemka

Head of Engineering @Atlassian | Platform Engineering | SRE | DevOps | DevEx | Cloud | Performance Engineering
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One needs to consider scaling requirements in details e.g. is workload increasing gradually, is there a trend or seasonality in workload which needs scaling, how fast workload can increase and random spikes in workload. Based upon your workload characteristics you need to choose the right approach. Also while the article deals with application servers technique, real world applications also need databases to store and server data and scaling them is not trivial. One needs to ensure that while designing application and infrastructure, data stores scaling are also considered as they can soon become bottlenecks. Databases which support partitioning with high availability should be chosen and that have bearing on the architecture one choose.

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What advanced techniques can you use to scale your software with cloud deployment?

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2

3

4

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1 Horizontal and vertical scaling

2 Load balancing and auto-scaling

3 Microservices and containers

4 Serverless and event-driven architecture

5 Caching and content delivery network

6 Monitoring and logging

7 Here’s what else to consider

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