# UniConfig Clustering

# Introduction

UniConfig can be easily deployed in the cluster thanks to its stateless architecture and transaction isolation:

  • stateless architecture - UniConfig nodes in the cluster don't keep any state that would have to be communicated directly to other Uniconfig nodes in a cluster. All network-topology configuration and state information are stored inside a PostgreSQL database that must be reachable from all UniConfig nodes in the same zone. All Uniconfig nodes share the same database, making the database single source of truth for Uniconfig cluster.
  • transaction isolation - Load-balancing is based on mapping UniConfig transactions to Uniconfig nodes in a cluster (transactions are sticky). One UniConfig transaction cannot span multiple UniConfig nodes in a cluster. Southbound sessions used for device management are ephemeral - they are created when UniConfig needs to access device on the network (like pushing cnfiguration updates) and they are closed as soon as a UniConfig transactions is committed or closed.

There are several advantages of clustered deployment of UniConfig nodes:

  • horizontal scalability - Increasing number of units that can process UniConfig transactions in parallel. Single UniConfig node tends to have limited processing and networking resources - by increasing number of nodes in the cluster, this constraint can be mitigated. The more Uniconfig nodes in a cluster, the more transactions can be executed in parallel. Number of connected UniConfig nodes in the cluster can also be adjusted at the runtime.
  • high-availability - Single UniConfig node doesn't represent single point of failure. If UniConfig node crashes, only UniConfig transactions that are processed by corresponding node, are cancelled. Application can retry failed transaction, and it will be processed by next node in the cluster.

There also are a couple limitations to be considered:

  • Parallel execution of transactions is subject to a locking mechanism, where 2 transactions cannot manipulate the same device at the same time.
  • Single transaction is always executed by a single Uniconfig node. This means that a scope of a single transaction is limited by the number devices and their configuration a single Uniconfig node can handle.

# Deployments

# Single-zone deployment

In the single-zone deployment, all managed network devices are reachable by all UniConfig nodes in the cluster - zone. Components of the single-zone deployment and connections between them are depicted by the next diagram.

Deployment with single zone
Deployment with single zone

Description of components:

  • UniConfig controllers - Network controllers that use common PostgreSQL system for persistence of data, communicate with network devices using NETCONF/GNMi/CLI management protocols and propagate notifications into Kafka topics (UniConfig nodes act only as Kafka producers). UniConfig nodes do not communicate with each other directly, their operation can only be coordinated using data stored in the database.
  • Database storage - PostgreSQL is used for persistence of network-topology configuration, mountpoints settings, and selected operational data. PostgreSQL database can also be deployed in the cluster (outside of scope).
  • Message and notification channels - Kafka cluster is used for propagation of notifications that are generated by UniConfig itself (e.g., audit and transaction notifications) or from network devices and only propagated by UniConfig controller.
  • Load-balancers - Load-balancer is used for distributing transactions (HTTP traffic) and SSH sessions from applications to UniConfig nodes. From the view of load-balancer, all UniConfig nodes in a cluster are equall. Currently, only round-robin load-balancing strategy is supported.
  • Managed network devices - Devices that are managed using NETCONF/GNMi/CLI protocols by UniConfig nodes or generate notifications to UniConfig nodes. Sessions between UniConfig nodes and devices are either on-demand/emphemeral (configuration of devices) or long-term (distribution of notifications over streams).
  • HTTP / SSH clients & Kafka consumers - Application layer such as workflow management systems or end-user systems. RESTCONF API is exposed using HTTP protocol, SSH server is exposing UniConfig shell and Kafka brokers allow Kafka consumers to listen to the events on subscribed topics.

# Multi-zone deployment

In this type of deployment there are multiple zones that manage separate sets of devices because:

  • network reachability issues - groups of devices are only reachable and thus manageable from some part of the network (zone) but not from others
  • logical separation - there are different scaling strategies or requirements for different zones
  • legal issues - some devices must be managed separately with split storage, for example, because of the regional restrictions

The following diagrams represents a sample deployment with 2 zones. The first zone contains 3 UniConfig nodes while the second zone contains only 2 UniConfig nodes. Multiple zones might share a single Kafka cluster but database instances need to be split (could be running in a single postgres server).

Deployment with multiple zones
Deployment with multiple zones

Description of multi-zone areas:

  • Applications - The application layer is responsible for managing mapping between network segments and Uniconfig zones. Typically this is achieved by deploying/using an additional inventory database that contains device <-> zone mappings - based on this information the application decides which zone to use.
  • Isolated zones - A zone contains one or more UniConfig nodes, load-balancers and managed network devices. The clusters in isolated zones share 0 information.
  • PostgreSQL databases - It is necessary to use dedicated database per zone.
  • Kafka cluster - Kafka cluster can be shared by multiple clusters in different zones or there could be single Kafka cluster per zone. Notifications from different zones can be safely pushed to the common topics since there can be no possible conflicts between Kafka publishers. However it is also possible to achieve isolation of published messages in a shared Kafka deployment by setting different topic names in different zones.

# Load-balancer operation

The responsibility of a load-balancer is to allocate UniConfig transaction on one of the UniConfig nodes in the cluster. It is done by forwarding requests without UniConfig transaction header to one of the UniConfig nodes (using round-robin strategy) and afterwards appending a backed identifier to the create-transaction RPC response in form of an additional Cookie header ('sticky session' concept). Afterwards, it is the responsibility of an application to assure that all requests that belong to the same transaction contain the same backend identifier.

The next sequence diagram captures a process of creating and using 2 UniConfig transactions with focus on load-balancer operation.

Load-balancing UniConfig transactions
Load-balancing UniConfig transactions

  • The first create-transaction RPC is forwarded to the first UniConfig node (applying round-robin strategy), because it does not contain uniconfig_server_id key in the Cookie header. The response contains both UniConfig transaction ID (UNICONFIGTXID) and uniconfig_server_id that represents 'sticky cookie'. Cookie header uniconfig_server_id is appended to the response by load-balancer.
  • The next request that belongs to the created transaction, contains same UNICONFIGTXID and uniconfig_server_id. Load balancer uses the uniconfig_server_id to forward this request to the correct UniConfig node.
  • The last application request represents again create-transaction RPC. This time, request is forwarded to the next registered UniConfig node in the cluster according to the round-robin strategy.

# Configuration

# UniConfig configuration

All UniConfig nodes in the cluster should be configured with the same parameters. There are several important sections of config/lighty-uniconfig-config.json file related to clustered environment.

# Database connection settings

This section contains information how to connect to PostgreSQL database and connection pool settings. It is placed under 'dbPersistence.connection' JSON object.

Example with essential settings:

database connection settings
// Grouped settings related to database connection.
"connection": {
    // name of the database
    "dbName": "uniconfig",
    // name of the user that has the remote access to database specified by 'dbName'
    "username": "uniremote",
    // user password (it is used only for the password-base authentication)
    "password": "unipass",
    // initial size of the connection pool (pre-initialized connections)
    "initialDbPoolSize": 5,
    // maximum size of the connection pool, before creation of next connections are blocked
    "maxDbPoolSize": 20,
    // maximum number of idle connections before next idle connections are cleaned
    "maxIdleConnections": 5,
    Timeout value used for socket read operations. If reading from the server takes longer than this value,
    the connection is closed. This can be used as both a brute force global query timeout and a method of
    detecting network problems. The timeout is specified in seconds and a value of 0 means that it is disabled.
    "socketReadTimeout": 20,
    // maximum wait time for obtaining of a new connection before fetch request is dropped [milliseconds]
    "maxWaitTime": 30000,
    List of network locations at which target database resides. The first entry is always tried in the first
    attempt during creation of database connection. If there are multiple entries specified, then other
    locations are used as fallback method in the order in which they are specified.
    "databaseLocations": [
            // database hostname / IP address
            "host": "uniconfig-db",
            // TCP port on which target database listens to incoming connections
            "port": 26257

# UniConfig node identification and heartbeat

By default, UniConfig node name is generated randomly. This behaviour can be changed by setting 'dbPersistence.uniconfigInstance.instanceName'. Instance name is leveraged, for example, in the clustering of stream subscriptions.

UniConfig nodes reports themselves in the cluster by updating heartbeat timestamp in database. Currently, this feature is not used by any other component in the UniConfig cluster. Reporting interval can be adjusted by 'dbPersistence.heartBeat.heartbeatInterval' field.


node identification and heartbeat settings
// UniConfig instance naming settings.
"uniconfigInstance": {
    // Identifier of the local UniConfig instance (name must be unique in the cluster). If it is set to 'null'
    // then this identifier is tried to be loaded from 'data/instance_name'. If this file doesn't exist, then
    // name of the UniConfig instance is randomly generated and this file is created with new name of instance.
    "instanceName": null
// Heart beat service settings.
"heartBeat": {
    // interval between updating of local UniConfig instance heartbeat timestamp [milliseconds]
    "heartbeatInterval": 1000

# Kafka and notification settings

This section contains settings related to connections to Kafka brokers, Kafka publisher timeouts, authentication, subscription allocation, and rebalancing settings.

Example with essential settings:

notification settings
// Grouped settings that are related to notifications.
"notifications": {
    // Flag that determines whether notifications are collected
    "enabled": true,
    "kafka": {
        // Username used for authentication into Kafka brokers (SASL). If it is not set, then authentication
        // is disabled (PLAINTEXT scheme).
        // "username": "kafka",
        // Password used for authentication into Kafka brokers.
        //"password": "kafka",
        "kafkaServers": [
                // Address / hostname of the interface on which Kafka broker is listening to incoming connections.
                "brokerHost": "kafka-broker",
                // TCP port on which Kafka broker is listening to incoming connections.
                "brokerListeningPort": 9092
        // Kafka producer settings
        "kafkaProducer": {
            // Specifies the number of messages that the Kafka handler processes as a batch
            "batchSize": 16384
        // Configuration of how long the send() method and the creation of connection for
        // reading of metadata methods will block. (in ms)
        "blockingTimeout": 60000,
        // Configuration of how long will the producer wait for the acknowledgement of a request. (in ms)
        // If the acknowledgement is not received before the timeout elapses, the producer will resend the
        // request or fail the request if retries are exhausted
        "requestTimeout": 30000,
        // Configuration of the upper bound on the time to report success or failure after a
        // call to send() returns.(in ms)
        // This limits the total time that a record will be delayed prior to sending, the time to
        // await acknowledgement from the broker (if expected), and the time allowed for retriable send failures.
        "deliveryTimeout": 120000
    // How often should uniconfig check for unassigned netconf notifications subscriptions (in seconds)
    "netconfSubscriptionsMonitoringInterval": 5,
    // How many unassigned netconf subscriptions can be processed within one subscription monitoring interval
    "maxNetconfSubscriptionsPerInterval": 10,
    // hard limit for how many subscriptions can a single UniConfig node handle
    "maxNetconfSubscriptionsHardLimit": 5000,
    // grace period for a UniConfig node going down. Other nodes will not restart the subscriptions until the grace
    // period passes from when a dead Uniconfig node has been seen last
    "rebalanceOnUCNodeGoingDownGracePeriod": 120,
    // the lower margin to calculate optimal range start
    "optimalNetconfSubscriptionsApproachingMargin": 0.05,
    // the higher margin to calculate optimal range end
    "optimalNetconfSubscriptionsReachedMargin" : 0.10

# Load-balancer configuration

The following YAML code represents sample Traefik configuration that can be used in the clustered UniConfig deployment (deployment with 1 Traefik node). There is one registered entry-point with identifier 'uniconfig' and port '8181'.

traefik configuration
  insecure: true

    address: ":8181"

  docker: {}

Next, it is needed to configure UniConfig docker containers with traefik labels - UniConfig nodes are automatically detected by Traefik container as 'uniconfig' service providers. There is also URI prefix '/rests', name of the 'sticky cookie' 'uniconfig_server_id' and server port number '8181' (UniConfig web server is listening to incoming HTTP requests on this port).

configuration of UniConfig docker container
    container_name: "uc_1"
      - traefik.enable=true
      - traefik.http.routers.uniconfig.entrypoints=uniconfig
      - traefik.http.routers.uniconfig.rule=PathPrefix(`/rests`)
      - traefik.http.services.uniconfig.loadBalancer.sticky.cookie.name=uniconfig_server_id
      - traefik.http.services.uniconfig.loadbalancer.server.port=8181

Values of all traefik labels should be same on all nodes in the cluster - scaling of UniConfig service in the cluster (for example, using Docker Swarm tools) is simple since container settings do not change.

# Clustering of NETCONF subscriptions and notifications

When device is installed with stream property set, subscriptions for all provided streams are created in database. These subscriptions are always created with UniConfig instance id set to null, so they can be acquired by any UniConfig from cluster. Each UniConfig instance in cluster uses its own monitoring system to acquire free subscriptions. Monitoring system uses specialized transaction to lock subscriptions which prevents more UniConfig instances to lock same subscriptions. While locking subscription, UniConfig instance writes its id to subscription table to currently locked subscription and which means that this subscription is already acquired by this UniConfig instance. Other instances of UniConfig will not find this subscription as free anymore.

# Optimal subscription count and rebalancing

With multiple UniConfig instances working in a cluster, each instance calculates an optimal range of subscriptions to manage.

# Example: optimal range for 5000 subscriptions with 3 nodes in the cluster and margins set to 0.05 and 0.10 = (1750, 1834)

Based on optimal range and a number of currently opened subscriptions, each UniConfig node (while performing a monitoring system iteration) decides whether it should:

  • Acquire additional subscriptions before optimal range is reached
  • Stay put and not acquire additional subscriptions in case optimal range is reached
  • Release some of its subscriptions to trigger rebalancing until optimal range is reached

When an instance goes down, all of its subscriptions will be immediately released and the optimal range for the other living nodes will changemanaged network devices and thus the subscriptions will be reopened by the rest of the cluster.

Following example illustrates a timeline of a 3 node cluster and how many subscriptions each node handles: