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To integrate Virtual Local Area Networks and Juniper Networks with your monitoring system, please reach out to MetricFire. Book a demo with the MetricFire team to discuss integrating Virtual Local Area Networks and Juniper Networks and how that can support your monitoring system.
A virtual local area network (VLAN) logically segments a LAN into different broadcast domains. A broadcast domain is divided and secluded in a computer network at the data link layer (OSI layer 2). This allows for network administrators to group hosts together. This is possible even if the hosts are not directly connected to the same network switch and are even in different locations.
If devices must be kept separated but need to share the cabling of a physical network, VLANs are used. This prevents these devices directly interacting and colliding with one another. An example of VLANs being used is by Internet hosting services. To separate customers' private zones from one other these internet hosting services use VLANs. This allows for each customer's servers to be in a single network segment, not being reliant on where the individual servers are located within the data center.
Juniper Networks is an American based multinational company. They provide networking products such as Routers, Switches, Networking management software, networking security products, and much more. Established during the mid 90’s they are among the market leaders in the networking space today.
In the past, Juniper focused more on Routers and switches, but as of 2014, have begun focusing and developing more software-defined networking products and security. Juniper currently has the 3rd largest market share for routers and switches used by internet services providers.
To integrate Virtual Local Area Networks and Juniper Networks with your monitoring system, sign up for a free trial with MetricFire. Talk with the MetricFire team about how to integrate Virtual Local Area Networks and Juniper Networks and get Virtual Local Area Networks and Juniper Networks interacting with your MetricFire dashboards directly.
MetricFire is a full-scale platform that provides infrastructure, system, and application monitoring using a suite of open-source monitoring tools. We enable you to use Hosted Graphite and aesthetic custom dashboards to visualize your metrics so you can understand what is happening.
MetricFire offers users a complete ecosystem of end-to-end infrastructure monitoring, comprised of popular open-source monitoring software services: Graphite and popular dashboards. Plugins for many other open-source projects are preconfigured, such as StatsD, collectd, and Kubernetes. You get all these within a hosted environment as a single product. Not only does MetricFire fit well into the infrastructure monitoring use-case, such as network monitoring and server monitoring, but we also do application monitoring and business intelligence.
Through this hosted environment, MetricFire boosts the unique features of open-source projects to give you more functionality than the original products. Below are some of the MetricFire features at a glance:
The key thing to remember is that Hosted Graphite by MetricFire is more than just Graphite. Our Hosted Graphite product actually adds data dimensionality and better data storage.
The benefits of MetricFire are:
“We now have over ten times the amount of metrics we started with, and on different accounts. One of the great things about MetricFire is that scaling to support this increase has been hassle-free, requiring no additional work on our side.”
“Building and managing an on-premise installation at this scale would require a lot of engineer time, especially in the first year...we use this engineering time to work on initiatives closer to our core business”
“There’s complete transparency with everything MetricFire do which means we can accurately predict what we’ll be spending and comfortably keep within our budget.”
"Every time I have a question, I get an answer from support after just a couple of hours. Their technical knowledge is excellent.”
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In this article, we’ll discuss what can go wrong with our machine-learning model after... Continue Reading
