Almost all size organizations now view flash as a means to accelerate application performance in their infrastructure … and for good reason. Organizations that deploy flash typically see increases in performance by factor of up to 10x. But while many all-flash storage arrays can deliver these increases in performance, savvy organizations must prepare to do more than simply increase workload performance. They need to identify solutions that help them better troubleshoot their emerging flash infrastructure as well as future proof their investment in flash by better modeling anticipated application workloads on all-flash arrays being evaluated before they are acquired.
One of the big advantages of all-flash arrays is that they make it much easier for organizations to improve the performance of almost any application regardless of its type. However the ease in which these all-flash arrays accelerate performance also may prompt organizations to lower their guard and fail to consider all of the potential pitfalls that accompany the deployment of such an array. One can just as easily over-provision an all-flash array as a disk-based array. Given the price per GB differences between the two, the cost penalty for over-provisioning all-flash arrays can be very significant.
Common pitfalls that DCIG hears about include:
- The all-flash array works fine at first but performance unexpectedly drops. This leaves everyone wondering, “What is the root cause of the problem?” The all-flash array? The storage network? The server? The application? Or some other component?
- An organization starts by putting a few or even one high-performance application on the all-flash array. It works so well that all of sudden everyone in the organization wants to put their applications on the array so performance on the all-flash array begins to suffer.
Performance analytics software can help in both of these cases as the recently released Load DynamiX 5.0 Storage Performance Analytics solution helps to illustrate. In the first scenario mentioned above, Load DynamiX provides a workload analyzer that examines performance in existing networked storage environments (FC/iSCSI now, CIFS/NFS coming in 1H2016.) This analyzer pulls performance data from the production storage arrays as well as from the Ethernet or FC switches so organizations can visualize existing storage workloads.
The Load DynamiX software then more importantly equips organizations to analyze these workloads as it automates this task using a combination of real-time and historical views of the data. By comparing IOPs, throughput, latency, read/write and random/sequential workload mixes among many others, it can begin to paint a picture of what is actually going on in the environment and identify the root cause of the performance bottleneck. This type of automation and insight becomes especially important when performance bottlenecks occur intermittently and at seemingly random and unpredictable intervals.
Yet maybe what makes the Load DynamiX solution particularly impressive is that after it captures these various pools of performance data, organizations can use it to optionally recreate the same behavior in their labs. In this way, they can experiment and trial possible solutions to the problem in a lab environment without tampering with the production environment and potentially making the situation worse. This gives IT organizations the opportunity to identify a viable solution and verify it works in their lab so they have a higher degree of confidence it will work in their production environment before they start the process of actually implementing the proposed fix.
This ability to capture and model workloads also becomes a very handy feature to have at one’s disposal when trialing new all-flash arrays as one organization recently discovered. It used Load DynamiX to first capture current performance data on its existing environment and then ran it against six (6) all-flash arrays under consideration.
As it turns out, all six (6) of them achieved the desired sub-2ms response times that they were hoping and expecting to get (as opposed to the 10ms response times that they were seeing using their existing disk-based array) when each of these all-flash arrays was tested using the company’s existing Oracle-based application workloads as Chart 1 illustrates.
However the organization then did something very clever. It fully expected that over time the workloads on the all-flash array would increase for the reasons cited above – perhaps by as much as 10x in the years to come. To model those anticipated increases, it again used Load DynamiX to simulate a 10x increase in application workload performance. When measured against this 10x increase in workload, substantial performance differences emerged between the various all-flash arrays as Chart 2 illustrates.
Under this 10x increase in workload, all of the all-flash arrays still outperformed the disk-based array. However only one of these arrays was able to deliver the sustained sub-2ms response times that this organization wanted its all-flash array solution to deliver over time. While a variety of factors came into play that account for these lower performance numbers, , it is noteworthy that all of these all-flash arrays except one had compression and deduplication turned on. As such, as applications workloads increased, it is conceivable and logical to conclude that these data reduction technologies begin to extract a heavier performance toll.
All-flash arrays have been a boon for organizations as they eliminate many of the complex, mind-numbing tasks that highly skilled individuals previously had to perform to coax the maximum amount of performance out of disk-based arrays. However that does not mean performance issues no longer exist once flash is deployed. Using performance analytics software like the Load Dynamix 5.0 Storage Performance Analytics solution, organizations can now better trouble-shoot both their legacy and new all-flash environment as well as make better, more informed choices about all-flash arrays so they can better scale them to match their anticipated increases in workload demands.