Communications of the ACM

examples of how developers are using serverless to come up with new solutions and applications.

Conclusion

Serverless computing is an evolution in cloud application development, exemplified by the Function-as-a-Service model where users write small functions, which are then managed by the cloud platform. This model has proven useful in a number of application scenarios ranging from event handlers with bursty invocation patterns, to compute-intensive big data analytics. Serverless computing lowers the bar for developers by delegating to the platform provider much of the operational complexity of monitoring and scaling large-scale applications. However, the developer now needs to work around limitations on the stateless nature of their functions, and understand how to map their application’s SLAs to those of the serverless platform and other dependent services. While many challenges remain, there have been rapid advances in the tools and programming models offered by industry, academia, and open source projects.

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Paul Castro ( castrop@us.ibm.com) is a research staff member at IBM T. J. Watson Research Center in Yorktown Heights, NY, USA.

Vatche Ishakian ( vishakian@bentley.edu) is an assistant professor at Bentley University, Waltham, MA, USA.

Vinod Muthusamy ( vmuthus@us.ibm.com) is a research scientist at IBM Research AI in Austin, TX, USA.

Aleksander Slominski ( https://aslom.net) is research staff member in the Serverless Group in Cloud Platform, Cognitive Systems and Services Department at IBM T.J.

Watson Research Center in Yorktown Heights, NY, USA.

Stateful serverless: Current serverless platforms are mostly stateless, and it is an open question if there will be inherently stateful serverless applications in the future with different degrees of QoS without sacrificing the scalability and fault-tolerance properties.

Service-level agreements (SLA): Serverless computing is poised to make developing services easier, but providing QoS guarantees remains difficult. 17, 27 While the serverless platform needs to offer some guarantees of scalability, performance, and availability, this is of little use if the application relies on an ecosystem of services, such as identity providers, messaging queues, and data persistence, which are outside the control of the serverless platform. To provide certain QoS guarantees, the serverless platform must communicate the required QoS requirements to the dependent components. Furthermore, enforcement may be needed across functions and APIs, through the careful measurement of such services, either through a third-party evaluation system, or self-reporting, to identify the bottlenecks.

Serverless at the edge: There is a natural connection between serverless functions and edge computing as events are typically generated at the edge with the increased adoption of Io T and other mobile devices. iRobot’s use of AWS Lambda and step functions for image recognition was described by Barga as an example of an inherently distributed serverless application. 8 Recently, Amazon extended its serverless capabilities to an edge based cloud environment by releasing AWS Greengrass. Consequently, the code running at the edge, and in the cloud may not just be embedded but virtualized to allow movement between devices and cloud. That may lead to specific requirements that redefine cost. For example, energy usage may be more important than speed.

New serverless applications: The serverless programming model is inherently different, but that should be a motivation to think about building—or rebuilding—new and innovative solutions that tap into what it can provide. Pywren, 18 ExCamera, 13 HPC, numerical analysis, and AI chatbots are but some