Chapter 3. NMS Architectures
Now
that you understand the basic concepts behind how management stations
(NMSs) and agents communicate, it's time to introduce the
concept of a network-management architecture. Before rushing out to
deploy SNMP management, you owe it to yourself to put some effort
into developing a coherent plan. If you simply drop NMS software on a
few of your favorite desktop machines, you're likely to end up
with something that doesn't work very well. By NMS
architecture, we mean a plan that helps you use NMSs effectively to
manage your network. A key component of network management is
selecting the proper hardware (i.e., an appropriate platform on which
to run your NMS) and making sure that your management stations are
located in such a way that they can observe the devices on your
network effectively.
3.1. Hardware Considerations
Managing a reasonably large
network requires an NMS with substantial computing power. In
today's complex networked environments, networks can range in
size from a few nodes to thousands of nodes. The process of polling
and receiving traps from hundreds or thousands of managed entities
can be taxing on the best of hardware. Your NMS vendor will be able
to help you determine what kind of hardware is appropriate for
managing your network. Most vendors have formulas for determining how
much RAM you will need to achieve the level of performance you want,
given the requirements of your network. It usually boils down to the
number of devices you want to poll, the amount of information you
will request from each device, and the interval at which you want to
poll them. The software you want to run is also a consideration. NMS
products such as OpenView are large, heavyweight applications; if you
want to run your own scripts with Perl, you can get away with a much
smaller management platform.
Is it possible to say something more helpful than "ask your
vendor"? Yes. First, although we've become accustomed to
thinking of NMS software as requiring a midrange workstation or
high-end PC, desktop hardware has advanced so much in the past year
or two that running this software is within the range of any modern
PC. Specifically, surveying the recommendations of a number of
vendors, we have found that they suggest a PC with at least a 300 MHz
CPU, 128 MB of memory, and 500 MB of disk space. Requirements for Sun
SPARC and HP workstations are similar.
Let's look at each of these requirements:
- 300 MHz CPU
-
This is well within the range of any modern desktop system, but you
probably can't bring your older equipment out of retirement to
use as a management station.
- 128 MB of memory
-
You'll probably have to add memory to any off-the-shelf PC; Sun
and HP workstations come with more generous memory configurations.
Frankly, vendors tend to underestimate memory requirements anyway, so
it won't hurt to upgrade to 256 MB. Fortunately, RAM is cheap
these days. (Memory prices fluctuate from day to day, but we recently
found 256 MB DIMMs for under $100.)
- 500 MB of disk space
-
This recommendation is probably based on the amount of space
you'll need to store the software, and not on the space
you'll need for log files, long-term trend data, etc. But
again, disk space is cheap these days, and skimping is
counterproductive.
Let's think a bit more
about how long-term data collection affects your disk requirements.
First, you should recognize that some products have only minimal
data-collection facilities, while others exist purely for the purpose
of collecting data (for example, MRTG). Whether or not you can do
data collection effectively depends to some extent on the NMS product
you've selected. Therefore, before deciding on a software
product, you should think about your data-collection requirements. Do
you want to do long-term trend analysis? If so, that will affect both
the software you choose and the hardware on which you run it.
For a starting point, let's
say that you have 1,000 nodes, you want to collect data every minute,
and you're collecting 1 KB of data per node. That's 1 MB
per minute, 1.4 GB per day -- you'd fill a 40 GB disk in
about a month. That's bordering on extravagant. But let's
look at the assumptions:
-
Collecting data
every minute is certainly excessive; every 10 minutes should do. Now
your 40 GB disk will store almost a year's worth of data.
-
1,000 nodes
isn't that big a network. But do you really want to store trend
data for all your users' PCs? Much of this book is devoted to
showing you how to control the amount of data you collect. Instead of
1,000 nodes, let's first count interfaces. And let's
forget about desktop systems -- we really care only about trend
data for our network backbone: key servers, routers, switches, etc.
Even on a midsize network, we're probably talking about 100 or
200 interfaces.
-
The amount of
data you collect per interface depends on many factors, not the least
of which is the format of the data. An interface's status may
be up or down -- that's a single bit. If it's being
stored in a binary data structure, it may be represented by a single
bit. But if you're using syslog to store
your log data and writing Perl scripts to do trend analysis, your
syslog records are going to be 80 bytes or so
even if you are storing only one bit of information. Data-storage
mechanisms range from syslog to fancy database
schemes -- you obviously need to understand what you're
using, and how it will affect your storage requirements. Furthermore,
you need to understand how much information you really want to keep
per interface. If you want to track only the number of octets going
in and out of each interface and you're storing this data
efficiently, your 40 GB disk could easily last the better part of a
century.
Seriously, it's hard to talk about
what your storage requirements will be when they vary over two or
three orders of magnitude. But the lesson is that no vendor can tell
you what your storage requirements will be. A gigabyte should be
plenty for log data on a moderately large network, if you're
storing data only for a reasonable subset of that network, not
polling too often, and not saving too much data. But that's a
lot of variables, and you're the only one in control of them.
Keep in mind, though, that the more data you collect, the more time
and CPU power will be required to grind through all that data and
produce meaningful results. It doesn't matter whether
you're using expensive trend-analysis software or some
homegrown scripts -- processing lots of data is expensive. At
least in terms of long-term data collection, it's probably
better to err by keeping too little data around than by keeping too
much.
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