Agent and Agentless Monitoring
When selecting an enterprise-level monitoring solution, one of the first decisions the IT department needs to make is whether to opt for an agent based, or agentless monitoring solution. The most important part of this decision making process is to understand what metrics you need to measure. Once you understand this, the process of finding the correct solution is quite simple. In a nutshell, agent-based solutions will give you access to much deeper and more granular metrics to aid in the fight against downtime while agentless solutions allow you to skip the step of deploying agents at the risk of less data and a less secure environment.
Agent-Based Monitoring Metrics
Agent-based monitoring consists of a software component, typically a small application, that resides on the client server and collects data. The data is then returned to the monitoring station based on a policy within the local agent, or as requested by the monitoring station. In the case of up.time, the agent responds with information based on requests originating from its monitoring station.
Agents whose policy is not managed by the monitoring station, but by the agent itself, impose additional load on the client servers that can reduce the overall performance of the services they are supporting. Many framework solutions (like IBM Tivoli, HP Openveiw, BMC, Patrol, and CA Unicenter) employ this model, and the result is a heavy workload on the servers and poor performance. Ironically, this effect is counter to the goal, as monitoring of performance with a heavy framework may actually degrade the performance of the servers.
The ideal solution is a lightweight agent (or 'invisible agent') that collects deep metrics, but doesn't introduce any recognizable load on the server. Look for a solution that gives you the depth you need without the negative impact on your servers. (up.time's pioneering development of lightweight/invisible agents are leading the industry).
In a Typical agent-based solution, the agents communicate with the monitoring station at predefined intervals, relaying the data back to a central repository for storage. Alerts are then generated if the metrics contained within the returned datasets exceed user defined thresholds. One of the biggest benefits of using agents is the more granular data that is returned by agent-based solutions (one exception can be in the case of WMI and Windows platforms). This allows the monitoring station to collect detailed metrics on the servers, their hardware, and the individual processes running on them. This deeper level of system and service metrics translates into faster time Mean-Time-To-Repair (MTTR) for service problems, more accurate capacity planning, and insight into systems behavior for performance tuning. The end result is less downtime, easier monitoring, and happier management.
Capabilities
By implementing an agent-based solution, advanced capabilities can be encapsulated within the agent functionality. The ability to directly interact with the client platform and its services allows the monitoring station to remotely execute service recovery and maintenance tasks based upon the data collected through the agent metrics. For example, a service monitor may be watching the log directory on an active Web server. When the directory exceeds a set capacity threshold, the agent can automatically compress and archive the log files, and begin a new set of logs, keeping the volume from filling and potentially crashing the Web server.
Agent-based solutions allow for greater flexibility with the creation of customizable service monitors. The end user of the monitoring solution can create scripts and/or binaries that check the status of services or collect non-standard metrics from applications and hardware. These custom monitors can be used to extend the functionality of the product to support applications or services that are not covered by the monitoring stations core functionality.
Agentless Monitoring
Agentless monitoring is deployed in one of two ways: using a remote API exposed by the platform or service being monitored, or directly analyzing network packets flowing between service components. Network packet analysis is typically implemented in addition to either an agent based or agentless monitoring solution. Network analysis will not provide detailed metrics on the servers supporting the application services communicating over the network, but will provide data on service performance and availability. End user experience monitoring is typically deployed using network traffic analysis.
