Serverless computing

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Serverless computing is a cloud computing execution model in which the cloud provider allocates machine resources on demand, taking care of the servers on behalf of their customers. "Serverless" is a misnomer in the sense that servers are still used by cloud service providers to execute code for developers. However, developers of serverless applications are not concerned with capacity planning, configuration, management, maintenance, fault tolerance, or scaling of containers, VMs, or physical servers. Serverless computing does not hold resources in volatile memory; computing is rather done in short bursts with the results persisted to storage. When an app is not in use, there are no computing resources allocated to the app. Pricing is based on the actual amount of resources consumed by an application. [1] It can be a form of utility computing.

Contents

Serverless computing can simplify the process of deploying code into production. Serverless code can be used in conjunction with code deployed in traditional styles, such as microservices or monoliths. Alternatively, applications can be written to be purely serverless and use no provisioned servers at all. [2] This should not be confused with computing or networking models that do not require an actual server to function, such as peer-to-peer (P2P).

Serverless runtimes

Serverless vendors offer compute runtimes, also known as Function as a Service (FaaS) platforms, which execute application logic but do not store data. Common languages supported by serverless runtimes are Java, Python and PHP. Generally, the functions run within isolation boundaries, such as, Linux containers.

Commercial offerings

The first "pay as you go" code execution platform was Zimki, released in 2006, but it was not commercially successful. [3] In 2008, Google released Google App Engine, which featured metered billing for applications that used a custom Python framework, but could not execute arbitrary code. [4] PiCloud, released in 2010, offered FaaS support for Python. [5]

Knative and Fission are two Open Source FaaS platforms which run with Kubernetes.

Google App Engine, introduced in 2008, was the first abstract serverless computing offering. [6] App Engine included HTTP functions with a 60-second timeout, and a blob store and data store with their own timeouts. No in-memory persistence was allowed. All operations had to be executed within these limits, but this allowed apps built in App Engine to scale near-infinitely and was used to support early customers including Snapchat, as well as many external and internal Google apps. Language support was limited to Python using native Python modules, as well as a limited selection of Python modules in C that were chosen by Google. Like later serverless platforms, App Engine also used pay-for-what-you-use billing. [7]

AWS Lambda, introduced by Amazon in 2014, [8] popularized the abstract serverless computing model. It is supported by a number of additional AWS serverless tools such as AWS Serverless Application Model (AWS SAM) Amazon CloudWatch, and others.

Google Cloud Platform created a second serverless offering, Google Cloud Functions in 2016. [9]

Oracle Cloud Functions is a serverless platform offered on Oracle Cloud Infrastructure, and is based on the open source Fn Project so developers can create applications that can be ported to other cloud and on-premise environments. It supports code in Python, Go, Java, Ruby, and Node. [10]

Serverless databases

Several serverless databases have emerged in the last few years. These systems extend the serverless execution model to the RDBMS, eliminating the need to provision or scale virtualized or physical database hardware.

Nutanix offers a solution named Era which turns an existing RDBMS such as Oracle, MariaDB, PostgreSQL or Microsoft SQL Server into a serverless service. [11]

Amazon Aurora offers a serverless version of its databases, based on MySQL and PostgreSQL, providing on-demand, auto-scaling configurations. [12]

Azure Data Lake is a highly scalable data storage and analytics service. The service is hosted in Azure, Microsoft's public cloud. Azure Data Lake Analytics provides a distributed infrastructure that can dynamically allocate or de-allocate resources so customers pay for only the services they use.

Oracle Cloud offers a serverless version of its Oracle Autonomous Database, which is the Autonomous Transaction Processing service. The serverless service also includes a JSON edition. [13]

Firebase, also owned by Google, [14] includes a hierarchical database and is available via fixed and pay-as-you-go plans. [15]

Advantages

Cost

Serverless can be more cost-effective than renting or purchasing a fixed quantity of servers, [16] which generally involves significant periods of underutilization or idle time. [1] It can even be more cost-efficient than provisioning an autoscaling group, due to more efficient bin-packing of the underlying machine resources.

This can be described as pay-as-you-go computing [16] or bare-code [16] as you are charged based solely upon the time and memory allocated to run your code; without associated fees for idle time. [16] A useful analogy here is between rental car (traditional cloud Virtual Machines) versus ride share apps like Uber or Lyft (serverless computing). Immediate cost benefits are related to the lack of operating costs, including: licenses, installation, dependencies, and personnel cost for maintenance, support, or patching. [16]

Elasticity versus scalability

In addition, a serverless architecture means that developers and operators do not need to spend time setting up and tuning autoscaling policies or systems; the cloud provider is responsible for scaling the capacity to the demand. [1] [17] [16] As Google puts it: "from prototype to production to planet-scale." [16]

As cloud native systems inherently scale down as well as up, these systems are known as elastic rather than scalable.

Small teams of developers are able to run code themselves without the dependence upon teams of infrastructure and support engineers; more developers are becoming DevOps skilled and distinctions between being a software developer or hardware engineer are blurring. [16]

Productivity

With function as a service, the units of code exposed to the outside world are simple event driven functions. This means that typically, the programmer does not have to worry about multithreading or directly handling HTTP requests in their code, simplifying the task of back-end software development.

Disadvantages

Performance

Infrequently-used serverless code may suffer from greater response latency than code that is continuously running on a dedicated server, virtual machine, or container. This is because, unlike with autoscaling, the cloud provider typically "spins down" the serverless code completely when not in use. This means that if the runtime (for example, the Java runtime) requires a significant amount of time to start up, it will create additional latency. [18] This is referred to as "cold start" in serverless computing.

Resource limits

Serverless computing is not suited to some computing workloads, such as high-performance computing, because of the resource limits imposed by cloud providers, and also because it would likely be cheaper to bulk-provision the number of servers believed to be required at any given point in time. [19] This makes it challenging to deploy complex applications (such as those with a Directed Acyclic Graph or DAG of functions); serverless computing out of the box is most suited for execution of individual stateless functions. Some commercial offerings like AWS Step Functions from Amazon and Azure Durable Functions from Microsoft are meant to ease this challenge.

Monitoring and debugging

Diagnosing performance or excessive resource usage problems with serverless code may be more difficult than with traditional server code, because although entire functions can be timed, [2] there is typically no ability to dig into more detail by attaching profilers, debuggers or APM tools. [20] Furthermore, the environment in which the code runs is typically not open source, so its performance characteristics cannot be precisely replicated in a local environment.

Security

Serverless is sometimes mistakenly considered as more secure than traditional architectures. While this is true to some extent because OS vulnerabilities are taken care of by the cloud provider, the total attack surface is significantly larger as there are many more components to the application compared to traditional architectures and each component is an entry point to the serverless application. Moreover, the security solutions customers used to have to protect their cloud workloads become irrelevant as customers cannot control and install anything on the endpoint and network level such as an intrusion detection/prevention system (IDS/IPS). [21]

This is intensified by the mono-culture properties of the entire server network. (A single flaw can be applied globally.) According to Protego, the "solution to secure serverless apps is close partnership between developers, DevOps, and AppSec, also known as DevSecOps. Find the balance where developers don't own security, but they aren't absolved from responsibility either. Take steps to make it everyone's problem. Create cross-functional teams and work towards tight integration between security specialists and development teams. Collaborate so your organization can resolve security risks at the speed of serverless." [22]

Privacy

Many serverless function environments are based on proprietary public cloud environments. Here, some privacy implications have to be considered, such as shared resources and access by external employees. However, serverless computing can also be done on private cloud environment or even on-premises, using for example the Kubernetes platform. This gives companies full control over privacy mechanisms, just as with hosting in traditional server setups.

Standards

Serverless computing is covered by International Data Center Authority (IDCA) in their Framework AE360. [23] However, the part related to portability can be an issue when moving business logic from one public cloud to another for which the Docker solution was created. Cloud Native Computing Foundation (CNCF) is also working on developing a specification with Oracle. [24]

Vendor lock-in

Serverless computing is provided as a third-party service. Applications and software that run in the serverless environment are by default locked to a specific cloud vendor. This issue is exacerbated in serverless computing, as with its increased level of abstraction, public vendors only allow customers to upload code to a FaaS platform without the authority to configure underlying environments. More importantly, when considering a more complex workflow that includes Backend-as-a-Service (BaaS), a BaaS offering can typically only natively trigger a FaaS offering from the same provider. This makes the workload migration in serverless computing virtually impossible. Therefore, considering how to design and deploy serverless workflows from a multi-cloud perspective seems promising and is starting to prevail. [25] [26] [27]

Uses/functions

Serverless functions can be used for: [28]

See also

Related Research Articles

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<span class="mw-page-title-main">Cloud computing</span> Form of shared Internet-based computing

Cloud computing is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each of which is a data center. Cloud computing relies on sharing of resources to achieve coherence and typically uses a pay-as-you-go model, which can help in reducing capital expenses but may also lead to unexpected operating expenses for users.

<span class="mw-page-title-main">AppScale</span> American cloud infrastructure software company

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Autoscaling, also spelled auto scaling or auto-scaling, and sometimes also called automatic scaling, is a method used in cloud computing that dynamically adjusts the amount of computational resources in a server farm - typically measured by the number of active servers - automatically based on the load on the farm. For example, the number of servers running behind a web application may be increased or decreased automatically based on the number of active users on the site. Since such metrics may change dramatically throughout the course of the day, and servers are a limited resource that cost money to run even while idle, there is often an incentive to run "just enough" servers to support the current load while still being able to support sudden and large spikes in activity. Autoscaling is helpful for such needs, as it can reduce the number of active servers when activity is low, and launch new servers when activity is high. Autoscaling is closely related to, and builds upon, the idea of load balancing.

<span class="mw-page-title-main">AWS Lambda</span> Serverless computing platform

AWS Lambda is an event-driven, serverless computing platform provided by Amazon as a part of Amazon Web Services. It is designed to enable developers to run code without provisioning or managing servers. It executes code in response to events and automatically manages the computing resources required by that code. It was introduced on November 13, 2014.

The Serverless Framework is a free and open-source web framework written using Node.js. Serverless is the first framework developed for building applications on AWS Lambda, a serverless computing platform provided by Amazon as a part of Amazon Web Services. Currently, applications developed with Serverless can be deployed to other function as a service providers, including Microsoft Azure with Azure Functions, IBM Bluemix with IBM Cloud Functions based on Apache OpenWhisk, Google Cloud using Google Cloud Functions, Oracle Cloud using Oracle Fn, Kubeless based on Kubernetes, Spotinst and Webtask by Auth0.

This is a timeline of Amazon Web Services, which offers a suite of cloud computing services that make up an on-demand computing platform.

Function as a service (FaaS) is a category of cloud computing services that provides a platform allowing customers to develop, run, and manage application functionalities without the complexity of building and maintaining the infrastructure typically associated with developing and launching an app. Building an application following this model is one way of achieving a "serverless" architecture, and is typically used when building microservices applications.

Apache MXNet is an open-source deep learning software framework that trains and deploys deep neural networks. It aims to be scalable, allows fast model training, and supports a flexible programming model and multiple programming languages. The MXNet library is portable and can scale to multiple GPUs and machines. It was co-developed by Carlos Guestrin at the University of Washington, along with GraphLab.

<span class="mw-page-title-main">Netlify</span> American cloud computing company

Netlify is a remote-first cloud computing company that offers a development platform that includes build, deploy, and serverless backend services for web applications and dynamic websites. The platform is built on open web standards, making it possible to integrate build tools, web frameworks, APIs, and various web technologies into a unified developer workflow.

<span class="mw-page-title-main">IBM Cloud</span> Cloud computing services provided by IBM

IBM Cloud is a set of cloud computing services for business offered by the information technology company IBM.

AWS Glue is an event-driven, serverless computing platform provided by Amazon as a part of Amazon Web Services. It is a computing service that runs code in response to events and automatically manages the computing resources required by that code. It was introduced in August 2017.

AWS App Runner is a fully managed container application service offered by Amazon Web Services (AWS). Launched in May 2021, it is designed to simplify the process of building, deploying, and scaling containerized applications for developers. The service enables users to focus on writing code and developing features, without needing to manage the underlying infrastructure. It provides automatic scaling, load balancing, and security features, making it a suitable choice for deploying web applications and APIs. The service also simplifies MLOps.

References

  1. 1 2 3 Miller, Ron (24 Nov 2015). "AWS Lambda Makes Serverless Applications A Reality". TechCrunch . Retrieved 10 July 2016.
  2. 1 2 MSV, Janakiram (16 July 2015). "PaaS Vendors, Watch Out! Amazon Is All Set To Disrupt the Market". Forbes . Retrieved 10 July 2016.
  3. ↑ Williams, Christopher. "Fotango to smother Zimki on Christmas Eve". The Register . Retrieved 2017-06-11.
  4. ↑ "Python Runtime Environment | App Engine standard environment for Python | Google Cloud Platform". Google Cloud Platform. Retrieved 2017-06-11.
  5. ↑ "PiCloud Launches Serverless Computing Platform To The Public". TechCrunch. 20 July 2010. Retrieved 2018-12-17.
  6. ↑ Evans, Jon (11 April 2015). "Whatever Happened to PaaS?". TechCrunch. Retrieved 17 December 2020.
  7. ↑ Kincaid, Jason (25 February 2009). "Google App Engine Offers Pricing Plan Beyond Quotas; Grab A Free I/O Ticket To Celebrate". TechCrunch. Retrieved 17 December 2020.
  8. ↑ Miller, Ron (13 Nov 2014). "Amazon Launches Lambda, An Event-Driven Compute Service". TechCrunch . Retrieved 10 July 2016.
  9. ↑ Novet, Jordan (9 February 2016). "Google has quietly launched its answer to AWS Lambda". VentureBeat . Retrieved 10 July 2016.
  10. ↑ "How to choose a cloud serverless platform". www.arnnet.com.au. Retrieved 2022-03-23.
  11. ↑ "One-click Database Administration & Automation | Nutanix Era".
  12. ↑ "Amazon Aurora Serverless - On-demand, Auto-scaling Relational Database - AWS". Amazon Web Services, Inc. Retrieved 2019-08-08.
  13. ↑ "Oracle brings the Autonomous Database to JSON". ZDNet. Retrieved 2022-03-23.
  14. ↑ Lardinois, Frederic (21 October 2014). "Google Acquires Firebase To Help Developers Build Better Real-Time Apps | TechCrunch" . Retrieved 2017-06-11.
  15. ↑ Darrow, Barb (2013-06-20). "Firebase gets $5.6M to launch its paid product and fire up its base". gigaom.com. Retrieved 2017-06-11.
  16. 1 2 3 4 5 6 7 8 Jamieson, Frazer (4 September 2017). "Losing the server? Everybody is talking about serverless architecture".
  17. ↑ Miller, Ron (31 March 2016). "Microsoft answers AWS Lambda's event-triggered serverless apps with Azure Functions". TechCrunch . Retrieved 10 July 2016.
  18. ↑ van Eyk, Erwin; Iosup, Alexandru; Abad, Cristina L.; Grohmann, Johannes; Eismann, Simon (2018). "A SPEC RG Cloud Group's Vision on the Performance Challenges of FaaS Cloud Architectures" (PDF). Companion of the 2018 ACM/SPEC International Conference on Performance Engineering. pp. 21–24. doi:10.1145/3185768.3186308. hdl: 1871.1/8aa529e9-f8f9-4305-8073-91dd1a9451fb . ISBN   9781450356299. S2CID   4718290.
  19. ↑ Hellerstein, Joseph; Faleiro, Jose; Gonzalez, Joseph; Schleier-Smith, Johann; Screekanti, Vikram; Tumanov, Alexey; Wu, Chenggang (2019). "Serverless Computing: One Step Forward, Two Steps Back". arXiv: 1812.03651 .{{cite journal}}: Cite journal requires |journal= (help)
  20. ↑ Leitner, Philipp; Wittern, Erik; Spillner, Josef; Hummer, Waldemar (2019). "A mixed-method empirical study of Function-as-a-Service software development in industrial practice". Journal of Systems and Software. 149: 340–359. doi:10.1016/j.jss.2018.12.013. hdl: 11475/14313 . ISSN   0164-1212. S2CID   67775784.
  21. ↑ "Cloud Workload Protection (CWP) | CWPP".
  22. ↑ Solow, Hillel (2019-02-05). "Serverless Computing Security Risks & Challenges". protego.io. Retrieved 2019-03-20.
  23. ↑ "The Standards Framework for the Application Ecosystem | International Data Center Authority (IDCA)".
  24. ↑ "CNCF, Oracle Boost Serverless Standardization Efforts". SDxCentral. Retrieved 2018-11-24.
  25. ↑ Aske, Austin; Zhao, Xinghui (2018-08-13). "Supporting Multi-Provider Serverless Computing on the Edge". Proceedings of the 47th International Conference on Parallel Processing Companion. ICPP Workshops '18. New York, NY, USA: Association for Computing Machinery. pp. 1–6. doi:10.1145/3229710.3229742. ISBN   978-1-4503-6523-9. S2CID   195348799.
  26. ↑ Baarzi, Ataollah Fatahi; Kesidis, George; Joe-Wong, Carlee; Shahrad, Mohammad (2021-11-01). "On Merits and Viability of Multi-Cloud Serverless". Proceedings of the ACM Symposium on Cloud Computing. SoCC '21. New York, NY, USA: Association for Computing Machinery. pp. 600–608. doi:10.1145/3472883.3487002. ISBN   978-1-4503-8638-8. S2CID   239890130.
  27. ↑ Zhao, Haidong; Benomar, Zakaria; Pfandzelter, Tobias; Georgantas, Nikolaos (2022-12-06). "Supporting Multi-Cloud in Serverless Computing". 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC). pp. 285–290. arXiv: 2209.09367 . doi:10.1109/UCC56403.2022.00051. ISBN   978-1-6654-6087-3. S2CID   252383217.
  28. ↑ "What Is Serverless Computing?". ITPro Today. 2021-12-13. Retrieved 2022-03-23.

Further reading

  1. ↑ Jonas, Eric (February 2019). "Cloud Programming Simplified: A Berkeley View on Serverless Computing". pp. 1–33. arXiv: 1902.03383 [cs.OS].
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