Understand several metrics for accessing the quality of a classification algorithm such as accuracy, precision, sensitiv

After an instance of gnmi-gateway acquires a lock for a target and forms a connection, it begins to forward data into the local in-memory cache. To allow any instance of gnmi-gateway in the cluster to serve a subscription for any target in the cluster, gNMI messages are replicated from the instance with the lock to other instances in the cluster. This replication allows any clustered instance of gnmi-gateway to accept client requests for any known target.

Be familiar with linear regression and other advanced regression methods. Be competent in using packages such as scikit-learn and caret for linear regression model building. Have the following competencies:

Ethical Component: Here, you want to make sure your visualization tells the true story. You need to be aware of your actions when cleaning, summarizing, manipulating, and producing a data visualization and ensure you aren’t using your visualization to mislead or manipulate your audience.

That said, we also didn’t want to limit the ability for network operators to further extend the functionality. Whenever possible, we enabled additional exporter and target loading plugins to be added with loose coupling and without the need to develop a complete gNMI client.

gnmi-gateway uses a shared lock per-target for coordinating these connections. We chose to build locking on Apache Zookeeper, which is included in Netflix’s paved road and provides all of the features necessary for cluster consensus. Although Zookeeper is the included clustering implementation, gnmi-gateway provides a Golang interface that can be used to implement connection coordination with systems other than Zookeeper.

At Netflix, our network infrastructure is constantly changing. To allow network engineers to make changes on the network without needing to update the configuration of gnmi-gateway many times per day, we included a feature that loads our gNMI targets from our network management system (NMS) based on tags on network devices. Although our NMS (and therefore its API) is not open source, we included a Target Loader plugin for loading devices from NetBox as well as from watched files.

Additionally, we wanted to improve the accessibility of the gNMI protocol and OpenConfig data by enabling network operators everywhere to deploy the service with no additional software development (coding) required.

To support better failure tolerance, we included clustering in gnmi-gateway that allows multiple instances of the service to coordinate and deduplicate connections to targets. We have many consumers interested in this streaming telemetry, but we only need a single connection to a target to receive it. By using this clustering functionality and replication, we’re able to avoid unnecessary duplicate gNMI connections to targets.

One of the primary issues we found with existing software for gNMI was a lack of tolerance for failure. Most of the existing software was stateful and either required a mutable deployment or didn’t include any cluster awareness for failover or coordination.

With every instance in the cluster able to serve streams for each target, we’re able to load balance incoming clients connections among all of the cluster instances. The underlying transport for gNMI is, like most gRPC connections, HTTP/2 over TLS — so this allows us to use a simple Layer 4 load balancer between gnmi-gateway and our gNMI clients. Although we’ve chosen to use a Layer 4 load balancer, this could be substituted for a Layer 7 load balancer or an alternative load balancing solution, such as DNS load balancing.

--- connection: demo-gnmi-router: addresses: - demo-gnmi-router.example.com:9339 request: demo-request meta: {} request: demo-request: target: '*' paths: - /components - /interfaces/interface[name=*]/state/counters - /interfaces/interface[name=*]/ethernet/state/counters

As mentioned earlier, we wanted to use existing code whenever possible. Within the openconfig/gnmi repo there are three specific components built by the OpenConfig community that we directly utilized:

Mapping Component: Here, you need to decide what variable to use as your x-variable and what to use as your y-variable. This is important especially when your dataset is multi-dimensional with several features.

We chose to build gnmi-gateway in Golang given the first-class support for protobufs in Go and that much of the existing reference code for gNMI exists in Golang. Although we chose Golang, clients for the gNMI protocol can be generated for any language with Protobuf 3 tools. Network operators should feel encouraged to deploy gnmi-gateway to manage connections to gNMI targets and write consuming gNMI applications in the language that is most appropriate for their situation.

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lf-identified introverts and extroverts with both light background music and in silence. They found that both personality types performed equally as well in silence. The difference showed when playing music — the introverts’ performance was hindered.