Explain what is the difference between elasticity and scalability in cloud computing
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Scalable environments only care about increasing capacity to accommodate an increasing workload. The key difference is, scalable systems don't necessarily mean they will scale up/down - it's only about being able to reach peak loads. Elastic workloads, however, will recognize dynamic demands and adapt to them, even if that means reducing capacity. An example being, you have a web app that needs to support 1,000 active users for one day, so you meet that demand by provisioning 10 servers in total - but after that one day, you only ever have 10 active users, thus you only need 1 server. In this case, a scalable system would be able to scale to meet the 1,000 active users with the additional 9 servers - so that's cool, our system scaled! But now we're left with an abundance of over-provisioned capacity and we have to deal with that. If our system was elastic for the above example, the process would have been automatic - our environment would have recognized the increasing workload, so it would have provisioned the necessary resources. Then when that day was over and the workload decreased significantly, our systems would have de-provisioned the servers as we didn't need them anymore. Hope this helps! Kindly let us know if the above helps or you need further assistance on this issue. Please do not forget to "Accept the answer” and “up-vote” wherever the information provided helps you, this can be beneficial to other community members. For many, the most attractive aspect of the cloud is its ability to expand the possibilities of what organizations — particularly those at the enterprise scale — can do. This extends to their data, the essential applications driving their operations, the development of new apps and much more. The central concept behind that notion of ever-expanding, ever-evolving capabilities in the cloud is sometimes referred to
as "cloud elasticity." Considering that the pace at which cloud computing and its possibilities are growing, it's critical for company leaders to develop a comprehensive understanding of this principle and what it can mean for their organizations. In a nutshell, cloud elasticity describes the ability of enterprises to add or remove cloud computing resources within their deployments as needed — based on shifting workload demands — without causing any downtime or other significant disruptions to the cloud service. Such resources include RAM, input/output bandwidth, CPU processing capability, and storage capacity. Automation built into the cloud platform drives elastic cloud computing. Elasticity is typically delineated into several categories:
Essentially, the difference between the two is adding more cloud instances as opposed to making the instances larger. Cloud elasticity vs. cloud scalabilityBecause these two terms describe similar occurrences, they are often used interchangeably. But they aren't interchangeable, and as such, shouldn't be considered synonymous with each other. What they are is intertwined — because an elastic cloud must simultaneously be scalable up and out.
Basically, scalability is about building up or down, like someone would with, say, a Lego set. Elasticity, meanwhile, entails stretching the boundaries of a cloud environment, like you would stretch a rubber band, to ensure end users can do everything they need, even in periods of immensely high traffic. When traffic subsides, you can release the resource — compare this to letting the rubber band go slack. Achieving cloud elasticity means you don't have to meticulously plan resource capacities or spend time engineering within the cloud environment to account for upscaling or downscaling. All of the modern major public cloud providers, including AWS, Google Cloud, and Microsoft Azure, offer elasticity as a key value proposition of their services. Typically, it's something that occurs automatically and in real time, so it's often called rapid elasticity. In the National Institute of Standards and Technology (NIST) formal definition of cloud computing, rapid elasticity is cited as an essential element of any cloud. How elasticity affects cloud spendThe capabilities of the cloud are invaluable to any enterprise. But at the scale required for even a "smaller" enterprise-level organization to make the most of its cloud system, the costs can add up quickly if you aren't mindful of them. Scaling up your cloud instances at a time when you don't really need to is an unnecessary expense that leaves you with idle resources. This is commonly called overprovisioning. Conversely, failing to scale up when business needs dictate that you should do so, known as underprovisioning, also becomes costly: If your infrastructure can't handle high levels of application traffic from cloud users, high latency and outages may occur. These issues will need IT mitigation, taking their attention away from more productive responsibilities. And if such failures become common, you might even lose customers. When you have true cloud elasticity, you can avoid underprovisioning and overprovisioning. Moreover, the efficiency you're able to achieve in everyday cloud operations helps stabilize costs. Cloud elasticity enables software as a service (SaaS) vendors to offer flexible cloud pricing plans, creating further convenience for your enterprise. Cloud elasticity in action: Major use casesElasticity is beneficial to any enterprise that experiences fluctuations in traffic and workload changes on a regular basis — in other words, most enterprises. But there are certain situations that illustrate it particularly well:
Increases in data sources, user requests and concurrency, and complexity of analytics demand cloud elasticity, and also require a data analytics platform that's just as capable of flexibility. Before blindly scaling out cloud resources, which increases cost, you can use Teradata Vantage for dynamic workload management to ensure critical requests get critical resources to meet demand. Leveraging effortless cloud elasticity alongside Vantage's effective workload management will give you the best of both and provide an efficient, cost-effective solution. Check out our blog to learn more about how Teradata elasticity can help you improve performance even in the midst of rapid operational expansion, or contact us to learn about everything Vantage has to offer. Learn more about Vantage What is the difference between elasticity and scalability in cloud computing?Elasticity is used just to meet the sudden up and down in the workload for a small period of time. Scalability is used to meet the static increase in the workload. Elasticity is used to meet dynamic changes, where the resources need can increase or decrease.
What is the difference between scalability and elasticity Mcq in cloud computing?Cloud elasticity is generally used by small enterprises whose workload expands only for a specific period. Cloud scalability is utilised by big enterprises. It is a short term event that is used to deal with an unplanned or sudden growth in demand.
What is the difference between scalability and elasticity Azure?The key difference is, scalable systems don't necessarily mean they will scale up/down - it's only about being able to reach peak loads. Elastic workloads, however, will recognize dynamic demands and adapt to them, even if that means reducing capacity.
What is the difference between scalability and elasticity pick one option?Scalability is the ability of the system to accommodate larger loads just by adding resources either making hardware stronger (scale up) or adding additional nodes (scale out). Elasticity is the ability to fit the resources needed to cope with loads dynamically usually in relation to scale out.
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