Providing the storage network with sufficient bandwidth is a necessary, but not sufficient, step to ensuring good performance of a storage networking application. If excessive network latency is causing the application to spend a large amount of time waiting for responses from a distant data center, then the bandwidth may not be fully utilized, and performance will suffer.
As mentioned previously, network latency is comprised of propagation delay, node delay and congestion delay. The end systems themselves also add processing delay, which is covered later in this section.
Good network design can minimize node delay and congestion delay, but a law of physics governs propagation delay: the speed of light. Still, there are ways to seemingly violate this physical law and decrease the overall amount of time that applications spend waiting to receive responses from remote sites.
The first is credit spoofing. As described previously, credit spoofing switches can convince Fibre Channel switches or end systems to forward chunks of data much larger than the amount that could be stored in their own buffers. The multiprotocol switches continue to issue credits until their own buffers are full, while the originating Fibre Channel devices suppose that the credits arrived from the remote site.
Since the far-end switches also have large buffers, they can handle large amounts of data as it arrives across the network from the sending switch. As a result, fewer control messages need to be sent for each transaction and less time is spent waiting for the flow control credits to travel end to end across the high-latency network. That, in turn, increases the portion of time spent sending actual data and boosts the performance of the application.
Another way to get around the performance limitations caused by network latency is to use parallel sessions or flows. Virtually all storage networks operate with multiple sessions, so the aggregate performance can be improved simply by sending more data while the applications are waiting for the responses from earlier write operations. Of course, this has no effect on the performance of each individual session, but it can improve the overall performance substantially.
Some guidelines for estimating propagation delay and node delay are shown in Figure 10-7.
|Determine the actual route distance between the endpoints of the network link. The service provider usually can furnish that information. Assume a propagation delay of one millisecond per hundred miles (round trip).|
|Determine the number of switches and routers in the data path. The service provider also can furnish that information or at least estimate it. Assume a relatively conservative estimate of two milliseconds per switch or router. Be sure to include the number of nodes in both directions for the round trip delay.|
Figure 10-7: Estimating the propagation and node delays.
Network congestion delay is more difficult to estimate. In a well-designed network -- one that has ample bandwidth for each application – it simply may be possible to ignore the effects of congestion, as it would be a relatively small portion of the overall network latency. Even in congested networks, the latency can be minimized for high-priority applications, such as storage networking, through the use of the traffic prioritization or bandwidth reservation facilities now available in some network equipment.
Congestion queuing models are very complex, but a method for calculating a very rough approximation of the network congestion delay is shown in Figure 10-8.
|Determine the current average utilization of the network link without the storage application (0-100%).|
|Subtract that value from 100% to derive the proportion of bandwidth that is available for the new storage networking application.|
|Divide the total node delay by the proportion of bandwidth available for the new storage networking application. For example, if all of the bandwidth is available for storage networking, the divisor will be 1.0, and there will be no additional congestion queuing. On the other hand, if only half of the bandwidth is available, the divisor will be 0.5, which would predict that the node delay would be doubled by the congestion queuing.|
Figure 10-8: Estimating the congestion queuing delay.
Figure 10-9 shows a numerical example of estimates for the total network latency.
|The distance between the endpoints of the network link, which connects the primary and secondary data centers, is 200 miles. Therefore, the round -trip propagation delay is: 200 miles x 2 = 400 miles 400 miles x 1 ms/100 miles = 4 ms|
|There are four routers in the path taken by the data, so the estimated round trip node delay is: 4 nodes x 2 x (2 ms/node) = 16 ms|
|The current average network link utilization without the storage application is 15%, so the amount of bandwidth available for the new storage networking application is: 100% – 15% = 85%|
|With congestion processing delay, the round trip node delay is increased to: 16 ms / 0.85 = 19 ms (in this case, the congestion delay adds 3 ms)|
|The total network latency (propagation delay + node delay + congestion delay) is: 4 ms + 16 ms + 3 ms = 23 ms|
Figure 10-9: Numerical example of total network latency estimate.
In simplest terms, the maximum rate at which write operations can be completed is the inverse of the network latency. In other words, if the data can be sent to the secondary array, written, and acknowledged in 23 ms, as in the numerical example, then in one second, about 43 operations can be completed for a single active session, as shown below:
1s / 0.023s _ 43.5
The rate of remote write operations can be increased linearly, up to a point, by increasing the number of active sessions used. So, if four sessions were used in the example, the performance would increase to 174 write operations per second:
43.5 _ 4 _ 174
As mentioned, there is a point at which other factors may limit the performance of the remote write operations. For example, transactions with large block sizes may be limited by the amount of network bandwidth allocated. Or, in applications that have plenty of bandwidth, the performance of the serverless backup software used to mirror the disk arrays may have an upper limit of, say, 40 to 50 MBps.
The above tip was excerpted from IP Storage Networking: Straight to the Core. Get additional book excerpts and information below.
Long-distance storage networking applications
IP storage networking expands remote data replication
IP storage data replication technology
Sizing link bandwidth for long-distance applications
Network latency effects on application performance
TCP effects on application performance
About the book: Whether you're a technical or business professional, IP Storage Networking: Straight to the Core will help you develop storage action plans that leverage innovation to maximize value on every dime you invest.
About the author: Gary Orenstein has been active in the IP storage networking industry since its inception with a career spanning multiple network storage companies and industry efforts. He was an initial governing board member of the Storage Networking Industry Association (SNIA) IP Storage Forum where he helped develop, promote, and deliver educational information furthering market growth. Gary is currently vice president of marketing at Compellent Technologies, a network storage company.