Let’s rewind a bit. The cloud emerged somewhere in the mid-2000s. Before its development, enterprises relied on their own infrastructure to house everything they needed for software, small business apps, or programs. Engineers were required to manage the hardware and software.
Put yourself in the engineers’ shoes, other than development, you still need to mind the integrity of your infrastructure. This includes servers, networks, storage, services, and application. Managing the hardware, let alone the software, is an expensive process that requires skilled technicians.
Serverless architecture removes that need. It should hardly come as a surprise then that Gartner predicts that by the end of this year, 20% of global enterprises will have deployed serverless computing technologies. In the next couple of paragraphs, we will look at how serverless architecture will evolve in the coming decade. But first, what is serverless architecture, anyway?
The term serverless does not necessarily mean the absence of servers. Well, at the least, it is the absence of servers that you need to worry about. Let’s differentiate it from ‘the cloud’ to give you a better picture. Serverless computing is a type of cloud computing but it does not mean the same the other way.
Cloud computing simply means a rented computer you can access through the internet. You pay for computing power, database storage, apps for small businesses, and other resources through the cloud.
With serverless architecture, a third-party provider will manage your servers. This service is aimed at companies that require servers but want to remove the cost of buying, maintaining, and upgrading them. Serverless architecture is a FaaS or Function as a Service protocol.
Tech organizations, especially startups or SaaS (software as a service) companies, take advantage of everything they can in terms of technological advancement. It’s better to use cost-effective means to operate, such as cloud computing, free business software, lean startup methods, DevOps, and many more.
The clear benefit of serverless architecture is that it’s cost-effective and scalable. Sounds good, right? However, any system has its pros and cons. As you continue in this article, you will learn some concerns about serverless computing and how companies are evolving to mitigate these problems. Let’s review some of the trends in a serverless architecture.
#cloud #server #serverless
Companies need to be thinking long-term before even starting a software development project. These needs are solved at the level of architecture: business owners want to assure agility, scalability, and performance.
The top contenders for scalable solutions are serverless and microservices. Both architectures prioritize security but approach it in their own ways. Let’s take a look at how businesses can benefit from the adoption of serverless architecture vs microservices, examine their differences, advantages, and use cases.
#serverless #microservices #architecture #software-architecture #serverless-architecture #microservice-architecture #serverless-vs-microservices #hackernoon-top-story
Any business when thinking of scaling business applications in a cost-effective way goes for a cloud computing approach. Even leading technology companies like Quora, Facebook, LinkedIn, Pinterest, and Spotify are also getting benefits offered by cloud computing infrastructures.
In this article, we are going to deeply understand the concept of serverless and how it works and why it is useful for your business.
#Serverless Architecture #What is Serverless Architecture #Serverless
By this point most enterprises, including those running on legacy infrastructures, are familiar with the benefits of serverless computing:
The benefits of agility and cost reduction are especially relevant in the current macroeconomic environment when customer behavior is changing, end-user needs are difficult to predict, and development teams are under pressure to do more with less.
So serverless is a no-brainer, right?
Not exactly. Serverless might be relatively painless for a new generation of cloud-native software companies that grew up in a world of APIs and microservices, but it creates headaches for the many organizations that still rely heavily on legacy infrastructure.
In particular, enterprises running mainframe CICS programs are likely to encounter frustrating stumbling blocks on the path to launching Functions as a Service (FaaS). This population includes global enterprises that depend on CICS applications to effectively manage high-volume transactional processing requirements – particularly in the banking, financial services, and insurance industries.
These organizations stand to achieve time and cost savings through a modern approach to managing legacy infrastructure, as opposed to launching serverless applications on a brittle foundation. Here are three of the biggest obstacles they face and how to overcome them.
Middleware that introduces complexity, technical debt, and latency. Many organizations looking to integrate CICS applications into a microservices or serverless architecture rely on middleware (e.g., an ESB or SOA) to access data from the underlying applications. This strategy introduces significant runtime performance challenges and creates what one bank’s chief architect referred to as a “lasagna architecture,” making DevOps impossible.
#serverless architecture #serverless functions #serverless benefits #mainframes #serverless api #serverless integration
Serverless Computing is the most promising trend for the future of Cloud Computing. As of 2020, all major cloud providers offer a wide variety of serverless services. Some of the FaaS offerings provided withing different cloud providers are AWS Lambda, Google Cloud Functions, Google Cloud Run, Azure Functions, and IBM Cloud Functions. If you want to use your current infrastructure, you could also use the open-source alternatives like OpenFaaS, IronFunctions, Apache OpenWhisk, Kubeless, Fission, OpenLambda, and Knative.
In a previous article, I iterated the most important autoscaling patterns used in major cloud services, along with their pros/cons. In this post, I will go through the process of predicting key performance characteristics and the cost of scale-per-request serverless platforms (like AWS Lambda, IBM Cloud Functions, Azure Functions, and Google Cloud Functions) with different workload intensities (in terms of requests per second) using a performance model. I will also include a link to a simulator that can generate more detailed insights at the end.
A performance model is “A model created to define the significant aspects of the way in which a proposed or actual system operates in terms of resources consumed, contention for resources, and delays introduced by processing or physical limitations” [source]. So using a performance model, you can “predict” how different characteristics of your service will change in different settings without needing to perform costly experiments for them.
The performance model we will be using today is from one of my recent papers called “Performance Modeling of Serverless Computing Platforms”. You can try an interactive version of my model to see what kind of information you can expect from it.
The input properties that need to be provided by the user to the performance model along with some default values.
The only system property you need to provide is the “idle expiration time” which is the amount of time the serverless platform will keep your function instance around after your last request before terminating it and freeing its resources (to know more about this, you are going to have to read my paper, especially the system description section). The good news is, this is a fixed value for all workloads which you don’t need to think about and is 10 minutes for AWS Lambda, Google Cloud Function, and IBM Cloud Functions and 20 minutes for Azure Functions.
The next thing you need is the cold/warm response time of your function. The only way you can get this value, for now, is by actually running your code on the platform and measuring the response times. Of course, there are tools that can help you with that, but I haven’t used them, so, I would be glad if you could tell me in the comments about how they were. Tools like the AWS Lambda Power Tuning can also tell you the response time for different memory settings, so you can check which one fits your QoS guarantees.
#serverless-computing #performance #serverless-architecture #serverless #serverless-apps
Happy Serverless September 2020! We at Coding Sans love working with serverless technology. This is why we decided to publish a report with the latest serverless trends this year. We partnered up with nine other companies who share our love to make it happen.
The idea was to gather insight from the community into the current serverless trends and to learn how others implement this technology. The excitement of the community and our partners exceeded our expectations.
We owe a big thank you to every participant who shared their insight with us, so we can in turn pass it on to you.
This blog post highlights 5exciting serverless trends, but it’s only a taste of all the data we’ve compiled.
Or take a deep dive, and download the full State of Serverless 2020 report to get data on all the 20+ serverless trends we’ve researched. It contains the most popular frameworks, FaaS products, container services, tooling, cloud security, and much more.
In this blog post, we cover the following:
These numbers speak for themselves. Amazon Web Services is miles ahead of everyone else in popularity among cloud providers. Google Cloud Functions and Microsoft Azure Functions have asserted a significant lead over the rest of the field, but they may not be out of reach just yet.
#serverless #nodejs #aws #cloud-computing #cloudservices #serverless-adoption #serverless-architecture #serverless-top-story