According to the web search results, labels are a key feature for Mist, as they can be used to make decisions and policies easier to understand. Labels are similar to tags or groups that represent a collection of users or resources.Labels can be used to classify your users and resources while building the WxLAN policies, which define a list of rules, restrictions, and other settings that can be applied to devices in order to change how they are treated by the network1.Labels can also be used to create a group of access points and apply a WLAN configuration using the apply to Access Points configuration while creating a WLAN1.

According to the web search results, Mist detects rogue APs by using the AP's third dedicated radio, which is used for scanning the wireless spectrum and detecting unauthorized APs on both 2.4 GHz and 5 GHz radio bands1.A rogue AP is an unauthorized access point that is installed on a network without the knowledge or consent of the network administrator1.A rogue AP can pose a serious security threat to a network, as it can allow attackers to bypass the network's security policies and gain access to sensitive data or launch other attacks1.A rogue AP can also interfere with the performance of the legitimate APs and cause network congestion or degradation1.

Mist APs have three radios: one for 2.4 GHz, one for 5 GHz, and one for scanning1.The scanning radio is used for detecting rogue, neighbor, and honeypot APs on both 2.4 GHz and 5 GHz radio bands1.This means that regardless of which radios you have enabled on your AP (2.4 GHz or 5 GHz), it will still be able to detect unauthorized APs at any frequency using the scanning radio1.The scanning radio can also provide other benefits, such as monitoring the network performance, optimizing the channel selection, and enhancing the location services1.

Therefore, based on the web search results, Mist detects rogue APs by using the AP's third dedicated radio, which is used for scanning the wireless spectrum and detecting unauthorized APs on both 2.4 GHz and 5 GHz radio bands.

In a Mist deployment, the Radio Resource Management (RRM) feature is an example ofreinforcement learning.Reinforcement learning is a type of machine learning that works on the principle that an agent takes an action which is either penalized or rewarded based on the result in order to reinforce the optimal behavior1.

RRM is a feature that optimizes the wireless network performance and user experience by dynamically adjusting the radio frequency (RF) settings, such as channel, bandwidth, and power, of the access points (APs) based on the changing environmental conditions, such as interference, traffic, or user density2.RRM uses reinforcement learning to learn from the feedback and data collected by the APs and the Mist cloud and to make decisions that improve the network quality of service (QoS) and service level expectations (SLEs)3.

A microservice can be added without system downtime. This is because microservices are independent and loosely coupled components that communicate over well-defined APIs. Adding a new microservice does not affect the functioning of other existing microservices or the overall application.The new microservice can be deployed and integrated seamlessly with the rest of the system1.

A microservice can be updated without system downtime. This is because microservices allow each service to be developed, tested, and deployed independently of other services. Updating a microservice does not require changing or restarting the entire application.The updated microservice can be rolled out gradually or reverted easily if needed2.

According to the [Service Level Expectations] page, Service Level Expectations (SLEs) are a set of metrics that measure the quality and performance of the wireless network from the user's perspective. SLEs can help network administrators monitor, troubleshoot, and optimize their network by providing real-time and historical data and analytics. SLEs can also help network administrators identify and resolve issues before they impact the user experience.

There are seven SLEs that are supported by Mist: Successful Connects, Time to Connect, Capacity, Coverage, Throughput, Roaming, and WAN. Each SLE has a target value and a threshold value that can be customized according to the network requirements. The SLE score is calculated as the percentage of clients that meet or exceed the target value for each SLE.

Two SLEs that report DHCP problems are Successful Connects and Time to Connect. DHCP (Dynamic Host Configuration Protocol) is a protocol that assigns IP addresses and other network configuration parameters to devices on a network. DHCP problems can occur when a device fails to obtain an IP address from the DHCP server or when the DHCP server is slow or unreachable.

Successful Connects is an SLE that measures the percentage of clients that successfully connect to the wireless network within a specified time frame. Successful Connects can report DHCP problems by showing the number of clients that fail to connect due to DHCP errors, such as DHCP timeout, DHCP NAK, or DHCP decline. Successful Connects can also show the root cause analysis and recommended actions for resolving DHCP problems.

Time to Connect is an SLE that measures the average time it takes for a client to connect to the wireless network from the moment it sends a probe request to the moment it receives an IP address from the DHCP server. Time to Connect can report DHCP problems by showing the breakdown of the connection time into different stages, such as probe, auth, assoc, EAPOL, and DHCP. Time to Connect can also show the outliers and trends for each stage and identify if there are any delays or failures in the DHCP process.