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Rapid Elasticity in Cloud Computing
Let us say you have an online store and most of the time, your website is getting moderate traffic but during an important sale for example Black Friday. This is where thousands of people enter into the website stream of sharing the same link and the excess traffic overwhelms the system, causing the website to crash or at least slow it down for consumers, even leading to the losses incurred.
Imagine the website would add resources automatically whenever more power was required and take them out when traffic has slowed down. This is where rapid elasticity in cloud computing comes in. It enables businesses to have computing resources scaled for higher or lower usage in real-time, enabling smooth functioning without incurring costs without necessity.
In this chapter, we are going to see the whole overview, not only of what rapid elasticity means, and how it works, but also the different types of elasticities it has, real-life examples, and its benefits.
What is Rapid Elasticity?
Rapid elasticity is the capability of the cloud to dynamically provision computational resources (processing power, memory, storage, etc.) on-demand- applications remain available to, and perform effectively for users, in the absence of human intervention, automatic by the system.
Some Examples are −
- A food delivery app gets orders around lunch and dinner time. It just utilizes the rapid elasticity tower of its cloud to automatically expand its services at peak times and reduce it in the time of slack.
- A university website suffers an upsurge every admission season and remains mostly idle for the rest of the year. Rapid elasticity makes sure it gets a boost when it needs it, with the corresponding increase in power.
This basically takes care of wasting resources or paying for capacities that have not been utilized, making cloud computing good at efficiency and cost.
How Does the Rapid Elasticity Works?
Dynamically, when it is about varying the size of the resource according to the load, it is essentially the auto-scaling technology that can be supplied by cloud providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).
Here's how it works
- Monitoring System Load: Cloud platforms keep continuous track of usage metrics like CPU and memory usage and memory usage against available resources.
- Scaling Up (Adding Resources): If there is increased traffic, the new servers, memory, or storage will be instantly added.
- Scaling Down (Removing Resources): It follows that when demand declines, the extra resources are removed to save costs.
- Load Balancing: It makes sure that traffic flows even in several servers while avoiding failure.
- Pay-As-You-Go Model: You will pay as you go almost like an electricity or water bill.
Example
Think of it like a ride-sharing app (Uber, Lyft) with more drivers activated when demand requires more rides, dynamism diminishes with under-demand and thus saves costs while at maximum efficiency.
What Are the Types of Rapid Elasticity in Cloud Computing?
There are three quick forms of elasticity:
1. Vertical Elasticity (Scaling Up/Down)
It is the addition or removal of resources within one virtual machine (virtually no virtual machines added).
For example:
- A gaming server goes lagging due to high traffic - just get more RAM and the CPU is added instantly. And then when traffic decreases, extra power gets removed, and spend as little as possible.
- Similar to that - instead of buying a phone which is new to buy-through-it, upgrade your phone with its RAM to get it to the next level.
2. Horizontal Elasticity (Scaling Out/In)
Horizontal scaling deals with adding or taking off multiple instances, whether it be servers or VMs, into or out of a "pool," depending on demand and load. From the perspective of the user, one doesn't actually upgrade one machine and add it on.
For example:
- A streaming service similar to Netflix deploys more servers for millions of users who binge-watch a new show, then completely shuts them down when the demand dips.
- Online ticket booking increases bandwidth capabilities whenever the ticket for a popular concert goes live.
It is like hiring extra workers during peak hours at a restaurant and sending them home when business slows down.
3. Burstable Elasticity
Bursting elasticity is the extent to which systems can consume resources above their averages for a time without a permanent upgrade.
For example:
- Supplementary capacity is borrowed temporarily during a breaking news event because the news website suddenly experiences a spike in traffic.
- The online portal has a huge number of students logging into it simultaneously and then makes use of some more additional resources before going normal again.
This means that one would borrow extra chairs for the dinner party instead of buying them.
Some Real-Life Applications of Rapid Elasticity
E-commerce and Online Stores
Amazon, Flipkart, and eBay are known to ramp up during sales periods and scale down during their down period so that they do not waste money. Payment gateways like PayPal and Razorpay are currently managing a lot of extra transactions during festive, special days.
Video Streaming Platforms
Netflix, Disney+, and YouTube suddenly increased cloud resources when a new movie or live stream is released. Cutting back on resources usually occurs when fewer viewers watch.
Online Gaming
During events, millions of players’ log into PUBG, Fortnite, and BGMI, causing the addition of extra gaming servers by these companies. The servers are then decommissioned once the event closes.
SaaS (Software as a Service) Platforms
Google Docs, Zoom, and Slack facilitate different levels of user growth under conditions of rapid elasticity. When demand grows, more virtual machines are added; when users leave for the day, they drop them again.
Benefits of Rapid Elasticity
- Cost efficient
- Enhanced operation
- Enhanced availability
- Automatic scaling
- Resource optimization
Rapid Elasticity Application to Cloud Service Providers
Here are the top cloud platforms for rapid elasticity −
1. Amazon's own services (AWS)
- AWS Auto Scaling technique: rescaling of EC2 instance based on actual demands at tint times.
- AWS Elastic Load Balancer: Load balances traffic between instances across the board.
2. Microsoft Azure
- Azure Virtual Machine Scale Sets: The automated scaling of virtual machines enabling load balancing.
- Azure Monitor Reports on scale recommendations of real-time insights and usage.
3. Google Cloud Platform
- Compute Engine Autoscaler for dynamically scaling resources with virtual machines.
- The Kubernetes Engine enables efficient scaled containerized applications.
Future of Rapid Elasticity
AI-powered auto-scaling, Serverless Computing, Edge Computing, etc. are the future aspects to bring more elasticity to cloud computing. Even now, anticipating changes in demand almost before it happens with the help of machine learning approaches, the new feature of AI enables planning for resources automatically based on past usage forecasting.
With rapid elasticity, any company can develop exciting new innovations without having to spend resources on developing or managing the IT infrastructure. Like everything else, as AI and automation progress, so will the cloud, and it will become even smarter through elastic computing, making cloud computing as a whole more efficient and reliable.