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Forgotten cloud scaling tricks | InfoWorld

I’m noticing a pattern in my work with cloud architects young and old. Familiar cloud scaling techniques used years ago are rarely used today. Yes, I understand why, since it’s 2023 and not 1993, but cloud architects still know some nifty tricks that are relevant today.

Until recently, we provisioned more cloud services to address scaling issues. That approach usually results in sky-high cloud bills. The best tactic is to spend more quality time on initial design and implementation rather than haphazardly allocating post-deployment resources and increasing costs.

Let’s look at the design process for cloud systems that scale, and learn some of the lesser-known architecture tricks that help cloud computing systems scale efficiently.

Automatic scaling with predictive analytics

Predictive analytics can forecast user demand and scale resources to optimize utilization and minimize costs. Today’s new tools can also implement advanced analytics and artificial intelligence. I don’t see these tactics being applied as much as they should be.

Autoscaling with predictive analytics is a technology that enables cloud-based applications and infrastructure to automatically scale up or down based on anticipated demand patterns. It combines the benefits of autoscaling, which automatically adjusts resources based on current demand monitoring, with predictive analytics, which uses historical data and machine learning models to forecast demand patterns.

This combination of old and new is making a comeback because powerful tools are available to automate the process. This approach to architecture and technology is especially beneficial for applications with highly variable traffic patterns, such as e-commerce websites or sales order entry systems, where sudden spikes in traffic can cause performance issues if the infrastructure can’t handle it. scale fast enough to meet demand. Autoscaling with predictive analytics results in a better user experience and reduced costs by using resources only when needed.

resource fragmentation

Chunking is a widespread existing technique that involves dividing large data sets into smaller, more manageable subsets called chunks. Fragmenting data or other resources improves your ability to scale.

In this approach, a large amount of resources, such as a database, storage, or processing power, is divided across multiple nodes in the public cloud, allowing multiple clients to access them simultaneously. Each shard is assigned to a specific node, and the nodes work together to serve client requests.

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As you may have guessed, resource sharding can improve performance and availability by spreading the load across multiple cloud servers. This reduces the amount of data each server needs to manage, allowing for faster response times and better resource utilization.

cache invalidation

I’ve been teaching cache invalidation on whiteboards ever since cloud computing became a thing, and yet it’s still not well understood. Cache invalidation involves removing “stale data” from the cache to free up resources, thereby reducing the amount of data that needs to be processed. Systems can scale and perform much better by reducing the time and resources required to access that data at its source.

As with all of these cheats, you have to watch out for some unwanted side effects. For example, if the original data changes, the cached data becomes stale and can lead to incorrect results or outdated information being presented to users. Cache invalidation, if done correctly, should resolve this issue by updating or deleting cached data when changes to the original data occur.

Various ways to invalidate a cache include time-based expiration, event-based invalidation, and manual invalidation. Time-based expiration involves setting a fixed time limit for how long data can remain in the cache. Event-based invalidation triggers cache invalidation based on specific events, such as changes to the original data or other external factors. Finally, manual invalidation involves manually updating or deleting cached data based on user or system actions.

None of this is secret, but these tips are often no longer taught in advanced cloud architecture courses, including certification courses. These approaches provide better optimization and overall efficiency for your cloud-based solutions, but there is no penalty for not using them. In fact, all of these problems can be solved by throwing money at them, which usually works. However, it can cost you 10 times more than an optimized solution that takes advantage of these or other architectural techniques.

I would rather do this well (optimized) rather than fast (poorly optimized). Who’s with me?

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