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Sizing Remote Access: A Straight Shot In The Dark

Finding the Right Size Sizing remote access is an ongoing process. Initially, you can estimate the number of users, usage patterns, the amount of data that will be transferred and a host of other variables. Finding the balance between adequate QoS (quality of service), measured by port availability and cost of ownership is difficult. Although remote-access vendors and users want nearly perfect availability, you want efficient use of remote access to reduce cost while providing decent QoS. Balancing those ends is the goal of finding the right size.

First, look at your user population and determine if you need to meet differing levels of service. The IS department in the research hospital wants high availability for key network administrators to access the network remotely. Certain administrators also need above-average availability as well. The bulk of the remaining user population has similar needs. On-call network administrators may need user-to-port ratios as low as 2:1 or 3:1. Hospital administrators may need 4:1 or 5:1, while the rest of the population can be settled at 8:1 or 10:1.

Segmenting users cuts into available ports. And to effectively provide differing QoS, you must restrict user access to specific ports--taking them out of the available pool. Consequently, segmenting requires more ports than if the ratio were flat (see "Port Requirements" at right). You could reduce the overall port count to 24 or 23, a single T1 or T1/PRI respectively. When all users are at 8:1, the effect is negligible; however, when users are segmented, the ratio for the general population rises to 11:1. Although that may be acceptable, it means more users will be contending for fewer resources, and as the user population increases, you'll have to move fast to keep up with the growth.

Sizing for Growth Sizing an existing remote-access solution is in some ways easier and increases the accuracy of the resulting predictions, provided you have solid historical data about usage patterns and calling patterns. Logs from remote-access servers provide information about individual and group usage patterns. This is a good indication of when users are accessing services and how long they need access. Records from PBXes or the telephone company can indicate the number of calls attempted--both successful calls and blocked calls (a blocked call is met with a busy signal).

Remote-access logs provide a picture of who uses remote access and when they need service. Actual usage may be very different than expected usage, and monitoring provides the information necessary to identify trends over time and adjust accordingly. For example, if a large percentage of users in a group are online for less than 30 minutes a session, you can adjust the user-to-port ratio a few points without significantly effec ting QoS. Adjusting the ratio to meet user needs lets you squeeze the most out of your available remote-access ports.

On the other hand, tracking the number of calls attempted (offered traffic per hour), you will have solid data on when users need remote access. If you look at only the remote-access logs and notice that you have two hours daily at 95 percent capacity (this takes into account modem recycle time and training time, for example), you might assume that you need to add ports. However, if the offered traffic indicates a low number of blocked calls, then your setup is probably fine as is.

Proper planning requires you to size for the points of highest utilization, or the average of the highest utilization, not for the average usage utilization. If you size for low or medium utilization, users will have a more difficult time gaining access during peak periods. Of course, it's always a balance between cost and service.

Dividing the number of blocked calls by the total number of calls attempted results in a measure of QoS. If 13 out of 250 calls attempted are blocked, then 5 percent of all calls get hit with a busy signal. Raise the number of blocked calls to 50, and 20 percent of all calls are blocked. For the general user population, higher rates of busy signals might be acceptable, but for mission-critical applications and users, you'll want to maximize availability.

General Guidelines The following guidelines will help ensure greater success in installing and managing a successful remote access solution.

· Look for remote-access solutions that are easily scalable.

· Keep thorough logs and review them for trends and sizing.

· Set reasonable access policies and be prepared to adjust them as needed.

· Examine access logs looking for usage patterns for the general population and groups of users.

Mike Fratto can be reached at mfratto@nwc.com.We would like to thank the Renex Corporation, Shiva Corp. and Phil Williams of Children's Hospital Care Center for their assistance with this article.

Sizing With The Erlang-B Calculation
Voice communication sizing relies on queueing theory and solid network traffic analysis. This calculation, which was developed over decades, can be used to determine (with a high degree of reliability) the appropriate number of ports, or trunks, to install for an average population. The Erlang-B calculation is used to determine the number of ports (trunks) needed to achieve a desired grade of service during the busiest hour of the month. The grade of service is the number of busy signals out of some number of users dialing in. For example, 1 percent reliability means 1 out of 100 calls will be met with a busy signal when a user dials in.

The Erlang-B calculation assumes that traffic distribution patterns are statistically normal, indicating that traffic dispersion curves are random or smooth--the bulk of the calls fall within one standard deviation of the average and that the VMR (variance-to-mean ratio) is close to one (VMR=the mean/standard deviation2). In other words, the majority of the calls generally arriving at a similar rate and call duration also is similar in length. The traffic is random and predictable. This has been borne out through traffic analysis of the voice network. By standardizing one of the variables, call duration of call rate, you can determine the approximate number of ports (or trunks) necessary to carry the load. Because the number of callers varies from location to location, it makes sense to try to standardize a more uniform variable, such as call duration. The voice network, for example, is largely designed for three minute calls.

Data traffic, on the other hand, is far more uneven. The VMR is larger than one, meaning calls and call duration don't follow a statistica lly normal distribution or bell curve. The number of users trying to access services can vary widely depending on the time of day or the day of the week and call duration can last from 20 minutes to several hours depending on the population. This results in an uneven distribution that cannot be predicted with any statistical accuracy using the Erlang-B calculation.


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