examples of how developers are using
serverless to come up with new solutions and applications.
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 ( email@example.com) is a research staff
member at IBM T. J. Watson Research Center in Yorktown
Heights, NY, USA.
Vatche Ishakian ( firstname.lastname@example.org) is an assistant
professor at Bentley University, Waltham, MA, USA.
Vinod Muthusamy ( email@example.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.
© 2019 ACM 0001-0782/19/12 $15.00
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
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
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
Watch the authors discuss
this work in the exclusive