7. Performance Co-Pilot Deployment Strategies

Performance Co-Pilot (PCP) is a coordinated suite of tools and utilities allowing you to monitor performance and make automated judgements and initiate actions based on those judgements. PCP is designed to be fully configurable for custom implementation and deployed to meet specific needs in a variety of operational environments.

Because each enterprise and site is different and PCP represents a new way of managing performance information, some discussion of deployment strategies is useful.

The most common use of performance monitoring utilities is a scenario where the PCP tools are executed on a workstation (the PCP monitoring system), while the interesting performance data is collected on remote systems (PCP collector systems) by a number of processes, specifically the Performance Metrics Collection Daemon (PMCD) and the associated Performance Metrics Domain Agents (PMDAs). These processes can execute on both the monitoring system and one or more collector systems, or only on collector systems. However, collector systems are the real objects of performance investigations.

The material in this chapter covers the following areas:

Section 7.1, “Basic Deployment”, presents the spectrum of deployment architectures at the highest level.

Section 7.2, “PCP Collector Deployment”, describes alternative deployments for PMCD and the PMDAs.

Section 7.3, “PCP Archive Logger Deployment”, covers alternative deployments for the pmlogger tool.

Section 7.4, “PCP Inference Engine Deployment”, presents the options that are available for deploying the pmie tool.

The options shown in this chapter are merely suggestions. They are not comprehensive, and are intended to demonstrate some possible ways of deploying the PCP tools for specific network topologies and purposes. You are encouraged to use them as the basis for planning your own deployment, consistent with your needs.

7.1. Basic Deployment

In the simplest PCP deployment, one system is configured as both a collector and a monitor, as shown in Figure 7.1. PCP Deployment for a Single System. Because some of the PCP monitor tools make extensive use of visualization, this suggests the monitor system should be configured with a graphical display.


Figure 7.1. PCP Deployment for a Single System

However, most PCP deployments involve at least two systems. For example, the setup shown in Figure 7.2. Basic PCP Deployment for Two Systems would be representative of many common scenarios.


Figure 7.2. Basic PCP Deployment for Two Systems

But the most common site configuration would include a mixture of systems configured as PCP collectors, as PCP monitors, and as both PCP monitors and collectors, as shown in Figure 7.3. General PCP Deployment for Multiple Systems.

With one or more PCP collector systems and one or more PCP monitor systems, there are a number of decisions that need to be made regarding the deployment of PCP services across multiple hosts. For example, in Figure 7.3. General PCP Deployment for Multiple Systems there are several ways in which both the inference engine (pmie) and the PCP archive logger (pmlogger) could be deployed. These options are discussed in the following sections of this chapter.


Figure 7.3. General PCP Deployment for Multiple Systems

7.2. PCP Collector Deployment

Each PCP collector system must have an active pmcd and, typically, a number of PMDAs installed.

7.2.1. Principal Server Deployment

The first hosts selected as PCP collector systems are likely to provide some class of service deemed to be critical to the information processing activities of the enterprise. These hosts include:

  • Database servers

  • Web servers for an Internet or Intranet presence

  • NFS or other central storage server

  • A video server

  • A supercomputer

  • An infrastructure service provider, for example, print, DNS, LDAP, gateway, firewall, router, or mail services

  • Any system running a mission-critical application

Your objective may be to improve quality of service on a system functioning as a server for many clients. You wish to identify and repair critical performance bottlenecks and deficiencies in order to maintain maximum performance for clients of the server.

For some of these services, the PCP base product or the PCP add-on packages provide the necessary collector components. Others would require customized PMDA development, as described in the companion Performance Co-Pilot Programmer’s Guide.

7.2.2. Quality of Service Measurement

Applications and services with a client-server architecture need to monitor performance at both the server side and the client side.

The arrangement in Figure 7.4. PCP Deployment to Measure Client-Server Quality of Service illustrates one way of measuring quality of service for client-server applications.


Figure 7.4. PCP Deployment to Measure Client-Server Quality of Service

The configuration of the PCP collector components on the Application Server System is standard. The new facility is the deployment of some PCP collector components on the Application Client System; this uses a customized PMDA and a generalization of the ICMP “ping” tool as follows:

  • The Client App is specially developed to periodically make typical requests of the App Server, and to measure the response time for these requests (this is an application-specific “ping”).

  • The PMDA on the Application Client System captures the response time measurements from the Client App and exports these into the PCP framework.

At the PCP monitor system, the performance of the system running the App Server and the end-user quality of service measurements from the system where the Client App is running can be monitored concurrently.

PCP contains a number of examples of this architecture, including the shping PMDA for IP-based services (including HTTP), and the dbping PMDA for database servers.

The source code for each of these PMDAs is readily available; users and administrators are encouraged to adapt these agents to the needs of the local application environment.

It is possible to exploit this arrangement even further, with these methods:

  • Creating new instances of the Client App and PMDA to measure service quality for your own mission-critical services.

  • Deploying the Client App and associated PCP collector components in a number of strategic hosts allows the quality of service over the enterprise’s network to be monitored. For example, service can be monitored on the Application Server System, on the same LAN segment as the Application Server System, on the other side of a firewall system, or out in the WAN.

7.3. PCP Archive Logger Deployment

PCP archives are created by the pmlogger utility, as discussed in Chapter 6, Archive Logging. They provide a critical capability to perform retrospective performance analysis, for example, to detect performance regressions, for problem analysis, or to support capacity planning. The following sections discuss the options and trade-offs for pmlogger deployment.

7.3.1. Deployment Options

The issue is relatively simple and reduces to “On which host(s) should pmlogger be running?” The options are these:

  • Run pmlogger on each PCP collector system to capture local performance data.

  • Run pmlogger on some of the PCP monitor systems to capture performance data from remote PCP collector systems.

As an extension of the previous option, designate one system to act as the PCP archive site to run all pmlogger instances. This arrangement is shown in Figure 7.5. Designated PCP Archive Site.


Figure 7.5. Designated PCP Archive Site

7.3.2. Resource Demands for the Deployment Options

The pmlogger process is very lightweight in terms of computational demand; most of the (very small) CPU cost is associated with extracting performance metrics at the PCP collector system (PMCD and the PMDAs), which are independent of the host on which pmlogger is running.

A local pmlogger consumes disk bandwidth and disk space on the PCP collector system. A remote pmlogger consumes disk space on the site where it is running and network bandwidth between that host and the PCP collector host.

The archives typically grow at a rate of anywhere between a few kilobytes (KB) to tens of megabytes (MB) per day, depending on how many performance metrics are logged and the choice of sampling frequencies. There are some advantages in minimizing the number of hosts over which the disk resources for PCP archives must be allocated; however, the aggregate requirement is independent of where the pmlogger processes are running.

7.3.3. Operational Management

There is an initial administrative cost associated with configuring each pmlogger instance, and an ongoing administrative investment to monitor these configurations, perform regular housekeeping (such as rotation, compression, and culling of PCP archive files), and execute periodic tasks to process the archives (such as nightly performance regression checking with pmie).

Many of these tasks are handled by the supplied pmlogger administrative tools and scripts, as described in Section 6.2.3, “Archive File Management”. However, the necessity and importance of these tasks favor a centralized pmlogger deployment, as shown in Figure 7.5. Designated PCP Archive Site.

7.3.4. ⁠Exporting PCP Archives

Collecting PCP archives is of little value unless the archives are processed as part of the ongoing performance monitoring and management functions. This processing typically involves the use of the tools on a PCP monitor system, and hence the archives may need to be read on a host different from the one they were created on.

NFS mounting is obviously an option, but the PCP tools support random access and both forward and backward temporal motion within an archive. If an archive is to be subjected to intensive and interactive processing, it may be more efficient to copy the files of the archive to the PCP monitor system first.


Each PCP archive consists of at least three separate files (see Section 6.2.3, “Archive File Management” for details). You must have concurrent access to all of these files before a PCP tool is able to process an archive correctly.

7.4. PCP Inference Engine Deployment

The pmie utility supports automated reasoning about system performance, as discussed in Chapter 5, Performance Metrics Inference Engine, and plays a key role in monitoring system performance for both real-time and retrospective analysis, with the performance data being retrieved respectively from a PCP collector system and a PCP archive.

The following sections discuss the options and trade-offs for pmie deployment.

7.4.1. Deployment Options

The issue is relatively simple and reduces to “On which host(s) should pmie be running?” You must consider both real-time and retrospective uses, and the options are as follows:

  • For real-time analysis, run pmie on each PCP collector system to monitor local system performance.

  • For real-time analysis, run pmie on some of the PCP monitor systems to monitor the performance of remote PCP collector systems.

  • For retrospective analysis, run pmie on the systems where the PCP archives reside. The problem then reduces to pmlogger deployment as discussed in Section 7.3, “PCP Archive Logger Deployment”.

  • As an example of the “distributed management with centralized control” philosophy, designate some system to act as the PCP Management Site to run all pmlogger and pmie instances. This arrangement is shown in Figure 7.6. PCP Management Site Deployment.

One pmie instance is capable of monitoring multiple PCP collector systems; for example, to evaluate some universal rules that apply to all hosts. At the same time a single PCP collector system may be monitored by multiple pmie instances; for example, for site-specific and universal rule evaluation, or to support both tactical performance management (operations) and strategic performance management (capacity planning). Both situations are depicted in Figure 7.6. PCP Management Site Deployment.


Figure 7.6. PCP Management Site Deployment

7.4.2. Resource Demands for the Deployment Options

Depending on the complexity of the rule sets, the number of hosts being monitored, and the evaluation frequency, pmie may consume CPU cycles significantly above the resources required to simply fetch the values of the performance metrics. If this becomes significant, then real-time deployment of pmie away from the PCP collector systems should be considered in order to avoid the “you’re part of the problem, not the solution” scenario in terms of CPU utilization on a heavily loaded server.

7.4.3. Operational Management

An initial administrative cost is associated with configuring each pmie instance, particularly in the development of the rule sets that accurately capture and classify “good” versus “bad” performance in your environment. These rule sets almost always involve some site-specific knowledge, particularly in respect to the “normal” levels of activity and resource consumption. The pmieconf tool (see Section 5.7, “Creating pmie Rules with pmieconf”) may be used to help develop localized rules based upon parameterized templates covering many common performance scenarios. In complex environments, customizing these rules may occur over an extended period and require considerable performance analysis insight.

One of the functions of pmie provides for continual detection of adverse performance and the automatic generation of alarms (visible, audible, e-mail, pager, and so on). Uncontrolled deployment of this alarm initiating capability throughout the enterprise may cause havoc.

These considerations favor a centralized pmie deployment at a small number of PCP monitor sites, or in a PCP Management Site as shown in Figure 7.6. PCP Management Site Deployment.

However, it is most likely that knowledgeable users with specific needs may find a local deployment of pmie most useful to track some particular class of service difficulty or resource utilization. In these cases, the alarm propagation is unlikely to be required or is confined to the system on which pmie is running.

Configuration and management of a number of pmie instances is made much easier with the scripts and control files described in Section 5.8, “Management of pmie Processes”.