This is an early release. Some features are not yet fully implemented.
Compose and orchestrate Kubernetes workloads using TOSCA.
Want to dive in?
Check out the included examples to understand what you can do with Turandot, and then head to the tutorial to get up and running.
Complex workloads: Turandot targets complex, large-scale, and multi-cluster workloads. Many examples are found in the field of Network Function Virtualization (NFV) (e.g. MANO). Included with Turandot is a comprehensive example of a multi-cluster telephony network service modeled entirely in TOSCA.
Diverse workloads: Workloads can comprise both standard and custom Kubernetes resources, as well as their operators. They can be deployed on a single cluster or on multi-cluster clouds. Virtual machines are supported via KubeVirt.
Service composition: Turandot implements TOSCA substitution mappings via policy-based service composition based on service templates selected from a repository.
Plugins: Helm charts and external orchestrators, such as Ansible, are supported via custom artifacts encapsulated as TOSCA types.
Design-time: TOSCA's extensibility via an object-oriented grammar is to Kubernetes's extensibility via custom resource definitions and operators. TOSCA's added value for Kubernetes is in providing a composable and validated graph of resource interrelations, effectively imbuing Kubernetes resources with architectural intent.
Run-time: Turandot manages resources together as single, coherent workloads, even across cluster boundaries, ensuring consistency and integration as well as allowing for cascading policies for allocation, composition, networking, security, etc.
Turandot is an in-cluster Kubernetes operator that:
- Handles custom resources called "services". (here is the CRD)
- Can work with an internal (built-in) or external repositories to retrieve CSAR-packaged service templates. A CSAR (Cloud Service Archive) is a zip file containing a TOSCA service template, TOSCA profiles, and other files ("artifacts") required for orchestration (see #5, below).
- Uses Puccini to compile the CSAR-packaged service templates into the Clout intermediary format.
- Renders the Clout to Kubernetes resources and schedules them as integrated workloads.
- Deploys and activates artifacts packaged in the CSAR file. This includes container images (as well as KubeVirt virtual machine images) and cloud-native configuration tools, such as scripts, playbooks, recipes, etc., as well as Kubernetes operators. These configuration tools have access to the entire workload topology, allowing them to essentially configure themselves.
- Are some of the resources remote? Turandot will delegate orchestration to Turandot operators in remote clusters (see multi-cluster workloads, below).
The Turandot operator can be controlled using the stateless turandot utility, e.g.:
turandot service deploy my-service --file=my-service-template.csar
Note that this utility is merely a convenience, not a requirement. You can use your existing Kubernetes tools to interact with the "service" custom resources.
Self-orchestration is coordinated by setting a "mode" for the entire workflow, following a pattern we call the Town-Crier Model. This mode is a proclamation: a modal, asynchronous, system-wide event, which answers the question "What should we be doing now?" Service template designers can attach actions to certain modes, modeled as TOSCA interfaces that use Kubernetes command streaming (or SSH for KubeVirt virtual machines). The guiding assumption is that components know their own status, needs, and obstacles better than a centralized orchestrator ever could, and so the best approach is to optimize for coordination rather than dictation. The end result is that the total state of the system is emergent rather than imposed.
Turandot acts as the proclamation controller, or "town crier", ensuring that the interfaces for the current mode are continuously polled for all running components and collating their success/failure statuses, even across multi-cluster boundaries. The Clout functions as the "town hall": it is where components can continuously store and share configuration data with the entire topology.
What if your workload crosses the boundaries of a single Kubernetes cluster?
Each cluster will have its own Turandot operator that manages resources only for that cluster, however the Clout will always contain a view of all resources, ensuring workload integration. Each operator can delegate work to specific other operators, according to composition policy. This network of operators essentially turns your multi-cluster environment into a single cloud.
Note that allowing operators to network with each other across cluster boundaries is beyond the scope of Turandot, however you can definitely use Turandot to orchestrate this control plane itself. Often this will be a SDN solution, such as shared virtual LANs across SD-WAN connections, using a combination of Kubernetes CNI providers, Multus, Cilium, Network Service Mesh, custom proxies, etc. Indeed, one size does not fit all, which is why Turandot insists on not having an opinion.
The Turandot operator can work in either "namespaced mode", in which it can only manage resources in the namespace in which it is installed, or "cluster mode", in which it can manage all namespaces.
Cluster mode requires elevated permissions, and as such may not be applicable in multi-tenancy scenarios. A more secure configuration is to have Turandot installed only in supported namespaces within a cluster and to allow secure delegation between them, in effect treating it like the multi-cluster scenario (see above).
Day -1: Modeling. TOSCA is used to create "profiles" of reusable, composable types, which together provide a validated and validating model for the target domain. TOSCA profiles vastly simplify the work of the service template designer. For example, our telephony network service example uses profiles for Kubernetes, KubeVirt, network services (including data planes), and telephony.
Day 0: Design. Solution architects compose service templates from the models provided by the TOSCA profiles, either by writing the TOSCA manually, or by using a wonderful graphical TOSCA IDE (that is yet to be created). The templates are tested in lab and staging environments using CI/CD-style automation.
Day 1: Operations handoff. The service templates are ready to be instantiated in production. A ticket from an operations support system (OSS) initiates the transfer to a managed multi-cluster cloud. Turandot is deployed to the target clusters (or automatically delegated from central clusters) and takes it from there.
Day 2+: Cloud-native operations. Once they are up and running the services should orchestrate themselves by adapting to changing internal and external conditions, as well as triggered and manual actions from operations. Changes include scaling, healing, migration, as well as more elaborate transformations. The Turandot operator will continue to monitor these changes and update the Clout. Components can refer to the Clout as "single source of truth" to see the complete topology in order to make self-orchestration decisions, as well as checking against policies to which they must or can adhere. Machine learning and AI can be applied to the Clout in order to make the best possible runtime orchestration decisions.
No, or not exactly. In Kubernetes, LCM is hardcoded behind the scheduling paradigm. Of course work is done by built-in and custom controllers to provision containers, wire up the networking, run init containers and sidecars, attach storage blocks, etc., but from an orchestration perspective LCM is largely reduced to a simple binary: either the resource is scheduled or it isn't.
Individual resources can be updated, and this can have cascading effects on other resources, but these effects are event-driven, not necessary sequential, and are certainly not "workflows" or atomic transactions that can be rolled back. Changes are expected to be dynamic, asynchronous, and "eventual". In other words: the total state of the workload is emergent rather than imposed.
This is so different from "legacy" LCM that it's probably best not to use that term in this scenario. Kubernetes introduces a new, cloud-native orchestration paradigm.
Is Turandot a replacement for full-blown NFV orchestrators like ONAP?
Absolutely not. Turandot's scope is purposely limited and focused only on managing Kubernetes workloads. The point is not to replace full-blown orchestrators but rather to make their job much easier by allowing them delegate the actual work of orchestrating Kubernetes workloads to Kubernetes itself, thus completing the cloud-native paradigm. All the orchestrator would need to do is tell Turandot to deploy a workload packaged as a CSAR file, and to provide it with inputs and to process its outputs. The orchestrator would not have to concern itself with the complex internal composition of these workloads.
Workflow engines are unreliable in any cloud environment but are an especially bad fit for Kubernetes. In Kubernetes a single container is, by design, controlled by multiple levels of operators, e.g. changes to the Pod, ReplicaSet, and Deployment resources can cause a container to restart and lose state at any moment. An event or message may very well be invalid as soon as it is triggered or sent.
And so Turandot introduces and embraces the Town-Crier Model. The "town" of components will be continuously attempting to achieve the current mode (a modal event). Turandot is merely a coordinator, not an orchestrator per se. In other words, we encourage self-orchestration.
That said, Turandot does not stop you from using a workflow engine if you reall need or want one. You can delegate to it via the mode interfaces or have it running as an entirely separate system.
For a workflow solution that is well integrated with Kubernetes consider Argo Workflows, which extends the scheduling functionality of Kubernetes jobs to allow for declarative dependency graphs.
(Note: We are working on an TOSCA profile for Argo, which will include a workflow example.)
Surely, for production systems a robust repository is necessary. Turandot can work with various repository backends, as well as any container image repository adhering to the OCI or Docker standards, e.g. Quay and Harbor. Indeed, the internal repository is implemented via the reference Docker repository. (Note that Turandot can store and retrieve CSAR files from such repositories even though they are not container images.)
The built-in repository does not have to be used in production, but it can be useful as a local cache in cases in which the main repositories are slow to access or if access is unreliable, e.g. on cloud edge datacenters.
Turandot comes with a Helm profile that allows you to package one or more Helm charts inside the CSAR or install them from an external chart repository. See the example. This feature allows you to combine the advantages of TOSCA and Turandot with existing Helm packaging efforts. Up to Helm version 3, Helm had an in-cluster controller named Tiller. At version 3 it was removed, leaving Helm entirely devoted to text templating. Turandot can be understood in this context as a super-charged replacement for Tiller.
All that said, it is worth considering abandoning Helm entirely and converting your charts into pure TOSCA CSARs. A Helm chart is essentially a collection of text templates for low-level Kubernetes YAML resource manifests stored in a bespoke repository format. Text templating is a rather miserable mechanism for generating YAML, and it's hard to use it to model reusable types. By contrast, TOSCA is a strictly-typed object-oriented language that supports inheritance and topological composition, making it vastly superior for modeling complex cloud workloads. TOSCA and CSAR are industry-supported standards.
"Turandot" is the last opera by composer Giacomo Puccini, likely inspired by Count Carlo Gozzi's commedia dell'arte play of the same name. Its final aria, Nessun Dorma, is one of the most well-known of all arias. Puccini is also famous for his Tosca opera. See, everything is connected.
Turandot, the name of the protagonist of the opera, comes from Persian Turandokht, meaning "daughter of Turan", Turan being an older name for much of what we now call Central Asia. Turan in turn is named for its legendary ruler, Tūr (meaning "brave"), a prince of the ancient Shahnameh epic.
There is some disagreement over whether the final "t" should be pronounced or not, as it likely wasn't pronounced by Puccini himself. All you should know is that if you pronounce it incorrectly this software will not work well for you.