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Response time monitoring

Many SLAs have user response times as one of the targets to be measured, but equally many organizations have great difficulty in supporting this requirement. User response times of IT and network services can be monitored and measured by the following:

  • Incorporating specific code withinclientandserverapplications software. This can be used to provide complete ‘end-to-end’ service response times or intermediate timing points to break down the overall response into its constituent components. The figures obtained from these tools give the actual response times as perceived by the users of a service.
  • Using ‘robotic scripted systems’ with terminal emulation software. These systems consist of client systems with terminal emulation software (e.g. browser or Telnet systems) and specialized scripted software for generating and measuring transactions and responses. These systems generally provide sample ‘end-to-end’ service response times and are useful for providing representative response times, particularly for multi-phase transactions or complex interactions. These only give sample response times, not the actual response times as perceived by the real users of the service.
  • Using distributed agent monitoring software. Useful information on service response times can be obtained by distributing agent systems with monitoring software at different points of a network (e.g. within different countries on the internet). These systems can then be used to generate transactions from a number of locations and give periodic measurements of an internet site as perceived by international users of an internet website. However, again the times received are only indications of the response times and are not the real user response times.
  • Using specificpassive monitoringsystems. Tracking a representative sample number of client systems. This method relies on the connection of specific network monitoring systems, often referred to as ‘sniffers’ being inserted at appropriate points within the network. These can then monitor, record and time all traffic passing a particular point within the network. Once recorded, this traffic can then be analysed to give detailed information on the service response times. Once again, however, these can only be used to give an approximation to the actual user response times, although these are often very close to the real-world situation, but this depends on the position of the monitor itself within the IT infrastructure.

In some cases, a combination of a number of systems may be used. The monitoring of response times is a complex process even if it is an in-house service running on a private network. If this is an external internet service, the process is much more complex because of the sheer number of different organizations and technologies involved.

A private company with a major website implemented a website monitoring service from an external supplier that would provide automatic alarms on the availability and responsiveness of their website. The availability and speed of the monitoring points were lower than those of the website being monitored. Therefore the figures produced by the service were of the availability and responsiveness of the monitoring service itself, rather than those of the monitored website.

When implementing external monitoring services, ensure that the service levels and performance commitments of the monitoring service are in excess of those of the service(s) being monitored.


The data collected from the monitoring should be analysed to identify trends from which the normal utilization and service levels, or baselines, can be established. By regular monitoring and comparison with this baseline, exception conditions in the utilization of individual components or service thresholds can be defined, and breaches or near misses in the SLAs can be reported and actioned. Also the data can be used to predict future resource usage, or to monitor actual business growth against predicted growth.

Analysis of the data may identify issues such as:

  • ‘Bottlenecks’ or ‘hot spots’ within the infrastructure
  • Inappropriate distribution of workload across available resources
  • Inappropriate database indexing
  • Inefficiencies in the application design
  • Unexpected increase in workloads or transaction rates
  • Inefficient scheduling or memory usage.

The use of each component and service needs to be considered over the short, medium and long term, and the minimum, maximum and average utilization for these periods recorded. Typically, the short-term pattern covers the utilization over a 24-hour period, while the medium term may cover a one- to four-week period, and the long term a year-long period. Over time, the trend in the use of the resource by the various IT services will become apparent. The usefulness of this information is further enhanced by recording any observed contributing factors to peaks or valleys in utilization – for example, if a change of business process or staffing coincides with any deviations from the normal utilization.

It is important to understand the utilization in each of these periods, so that changes in the use of any service can be related to predicted changes in the level of utilization of individual components. The ability to identify the specific hardware or software components on which a particular IT service depends is improved greatly by an accurate, up-to-date and comprehensive CMS.

When the utilization of a particular resource is considered, it is important to understand both the total level of utilization and the utilization by individual services of the resource.

If a processor that is 75% loaded during the peak hour is being used by two different services, A and B, it is important to know how much of the total 75% is being used by each service. Assuming the system overhead on the processor is 5%, the remaining 70% load could be split evenly between the two services. If a change in either Service A or Service B is estimated to double its loading on the processor, then the processor would be overloaded.

However, if service A uses 60% and Service B uses 10% of the processor, then the processor would be overloaded if service A doubled its loading on the processor. But if service B doubled its loading on the processor, then the processor would not necessarily be overloaded.


The analysis of the monitored data may identify areas of the configuration that could be tuned to better utilize the service, system and component resources or improve the performance of the particular service.

Tuning techniques that are of assistance include:

  • Balancing workloads and traffic – transactions may arrive at the host or server at a particular gateway, depending on where the transaction was initiated; balancing the ratio of initiation points to gateways can provide tuning benefits
  • Balancing disk traffic – storing data on disk efficiently and strategically, e.g. striping data across many spindles may reduce data contention
  • Definition of an accepted locking strategy that specifies when locks are necessary and the appropriate level, e.g. database, page, file, record and row – delaying the lock until an update is necessary may provide benefits
  • Efficient use of memory – may include looking to utilize more or less memory, depending on the circumstances.

Before implementing any of the recommendations arising from the tuning techniques, it may be appropriate to consider testing the validity of the recommendation. For example, ‘Can Demand Management be used to avoid the need to carry out any tuning?’ or ‘Can the proposed change be modelled to show its effectiveness before it is implemented?’


The objective of this activity is to introduce to the live operation services any changes that have been identified by the monitoring, analysis and tuning activities. The implementation of any changes arising from these activities must be undertaken through a strict, formal Change Management process. The impact of system tuning changes can have major implications on the customers of the service. The impact and risk associated with these types of changes are likely to be greater than that of other different type of changes.

It is important that further monitoring takes place, so that the effects of the change can be assessed. It may be necessary to make further changes or to regress some of the original changes.

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