The latest update to the open source Cyclops Framework, part of our ongoing work to advance metering and monetization across cloud platforms, brings yet more new features and improvements:
Small fixes to the versioning/rollback features
New estimation and forecasting engine
The new forecasting engine is now built into Cyclops’ UDR service and can generate individual or global usage forecasts and cost/revenue estimates based on the existing usage data and be used to evaluate new pricing models.
A full-featured CLI client for the forecasting engine was also created to make using the new functionality more intuitive.
Looking into a possible post-cloud world, we see mentions of different computing paradigms, many of them based on decentralised structures to overcome scalability and user control limitations. Among them is blockchain-as-a-service (BCaaS or BaaS), mimicking the platform-as-a-service (PaaS) user experience for both application providers and consumers. In PaaS, providers first sign up and subscribe to the platform, then design and build their applications and deploy them to the platform where it is executing either permanently or upon incoming network requests or other event triggers. Additionally, developers may advertise their apps at technology-specific hubs such as AWS SAR or Helm Hub. Consumers then adhere to the application terms, which might require a sign-up at the provider site, before being able to invoke and make use of the application.
While hybrid, multi- and cross-cloud applications are on the rise, even for scenarios in which purely public cloud deployments are planned, having an equivalent private cloud stack available is useful in many ways. With the relative portability of popular open source cloud stacks, this is rather trivial to accomplish. For many large cloud providers, there are commercial solutions like Microsoft’s Azure Stack, IBM’s Cloud Private, Oracle’s Cloud Native Framework, Google’s Anthos (née CSP), Alibaba’s Apsara Stack and Amazon’s AWS Outposts (as well as Greengrass for Lambda and other specialised offers). Yet sometimes, these are not an option for technical or business reasons. In this blog post, alternative options are discussed.
The Serverless Application Repository by Amazon Web Services (AWS SAR) is, in simplified terms, a marketplace for Lambda functions. You can speed up application development by building on the functions (or function compositions) provided by it, and you can share your own functions with other cloud application developers. AWS SAR was launched over a year ago. In the Service Prototyping Lab at Zurich University of Applied Sciences, we are investigating better ways of building applications for cloud and post-cloud environments. Consequently, we did a full year observation of AWS SAR to find out what’s in it and what’s going on. Read on for some interesting excerpts and findings and for accessing the study document.
In previous blog posts – here and here – we showed how to set up OpenWhisk and deploy a sample application on the platform. We also provided a comparison between the two open-source serverless platforms OpenWhisk and Knative in this blog post. In progressing this work, we shifted focus slightly to that other critical component of realistic serverless platforms, the services that they integrate with – so-called Backend-as-a-service – which are (arguably) more important. For this reason, in this blog post we look at how to integrate widely used databases with Knative and potentially OpenWhisk in future.
Our initial thoughts were to leverage database trigger mechanisms and write components which would listen to these events and publish them to a Kafka bus. Indeed, we started to write code that targeted PostgreSQL to do just that, but then we came across the Debezium project which essentially solves the same problem, albeit not in the same context, but with a much more mature codebase and support for multiple database systems. It didn’t make sense to reinvent the wheel so the objective then turned into how to best integrate Debezium with Knative.
The first four “wild” years of serverless computing, starting with simple Function-as-a-Service (FaaS) launches in 2014, are over, and we are in the fifth year now. All major cloud companies offer FaaS, corresponding Backend-as-a-Service (BaaS), and related “serverless” services such as frameworks for cloud function-based data processing at the edge or in constrained environments. Researchers from universities, research institutes and research divisions in companies have covered this development, and proposed improved systems and frameworks, since 2016 – trailing two years behind industry initially, but with promising designs and prototypes which may give the necessary impetus for a next-generation serverless computing paradigm. We have surveyed 130+ research papers and announce the Serverless Research Output website which makes the results accessible.
Migrating an application from one cloud to another is a challenging activity and one must be mindful of both potential incompatibility and data loss when migrating. It is also, however, often necessary, so a proper way to automate the process and ensure a working deployment on the other end is certain to be a handy tool to an administrator. Since we have been working with multi and cross cloud environments and application portability (see paper and blog), we present a tool to automate this process for Openshift.
As far as use cases for migration go, the easiest example to visualize is moving an application from the development environment to production. Minishift, the single node local development version of Openshift is a great way to develop and test a new application, isolated from the risks and expenses of exposing it to the outside world. But at some point, this application will need to be recreated on a production Openshift instance and while doing this ‘traditionally’ is easy for small applications, it can become cumbersome for larger cases, especially if parts of it were configured using the graphical dashboard.
With interest in serverless computing increasing rapidly, the question of which technology solutions will win is receiving much interest. Although there is significant industrial activity relating to serverless – driven primarily by the AWS Lambda ecosystem – there is a clear need for solutions which are not premised on lock-in to a single provider and which can work across clouds. OpenWhisk and Knative are two technologies which focus on this space – here we consider the relative positioning of these technologies based on our experience working with them.
In two previous blog posts – here and here – we discussed our experience with deploying OpenWhisk on Kubernetes on OpenStack. As applied researchers at the Service Prototyping Lab, we are investigating potential use cases for such setups and for FaaS-based applications in general. In this blog post, we will therefore describe how we built a sample MQTT-based application that shows OpenWhisk in action for sensor data processing for future Internet of Things and smart dust scenarios.
The basic idea of the application is that it consumes data from an MQTT feed, stores it in a database and provides a means to access the database via a web UI. The architecture of the application is shown in the figure below. The application is based on this blog post.
In a previous blog post, we described our experience with deploying OpenWhisk on Kubernetes on OpenStack. During subsequent testing, we observed some issues with the OpenWhisk deployment wherein some OpenWhisk components – specifically, the controller and the invoker – would fail to restart after rebooting the machines running the Kubernetes nodes for maintenance tasks. To fix this, we had to redeploy OpenWhisk after each failure which resulted in significant data loss and was clearly an unacceptable operational solution.