FisherFindings

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In response to my post about Data Encryption, Rich Jordan noted in part:

We have a vanishing breed ’small’ VMS site that needs to start encrypting their backups. We’re looking at using the VMS Encrypt capabilities in the current version, and working up some appropriate method of key handling and preservation. They’re a single I64 server with locally attached mirrored disks and DAT tape drive; no arrays.

Unfortunately the low end customer has been left out of all the nice hardware encryption solutions like the XP arrays and SKM/LTO tape due to cost. While the backups can be handled with built-in capabilities, encryption of ‘data at rest’, should that become a requirement, will be a serious issue that may end up driving a move to another platform (this customer likes VMS and wants to continue using it).

I keep hoping the maintainer of the LD package will be able to add some usable encryption capabilities; that would provide a good solution for the small sites.

Rich makes a good point. I will make an effort to try to provide a post that shows options for low end OpenVMS customers (those who are not using SAN storage where this type of encryption can be built into the hardware).

Do you have any question about these posts? As you can see, I want this to be a useful tool for you, not just me plunking down a lot of words into this blog. So, ask away!

Remember that OpenVMS is a wonderful platform for bladed enclosures. Think of the ability to save power by sharing components within the blade center. Additionally, you can add CPUs / Blades on demand. Neither the physical nor carbon footprints needs to drastically increase. And using OpenVMS clustering technologies you can ascertain the survival of the platform through use of two blade centers. Many OpenVMS sites recognize this capability and embrace OpenVMS within blade centers.

You might also find this link of interest:

http://www.infoworld.com/d/green-it/epa-launches-energy-star-data-centers-440

I had some input (thanks Bob Blunt!) on what to include. As he points out, all too often presentations tend to gloss over the installation of these products. So, I will specially add a few screen casts that show how to install T4 & Friends in your environment.

Here’s the topics the screencasts on this type will cover:

  1. Installation
    1. installing T4
    2. installing TLViz
    3. installing CSVPNG
  2. Using T4
    1. to collect on-going statistics
    2. to collect performance data
  3. Using TLViz
    1. understanding statistics
    2. to explore data
    3. to identify problem areas
    4. using T4 files
    5. using Chart Settings
    6. using Modify Item List
    7. using Options
    8. adding calculated items
    9. graphing the results
  4. Using CSVPNG
    1. to quickly wade through data
    2. to produce consistent reports / graphs
    3. to produce exception reports / graphs
    4. to concatenate data
    5. to extract data
    6. adding calculated items
    7. control of graphs

So, in my spare time, I will try to capture this information and put together these screencasts to show how to use T4 & Friends. This is the basis of how to collect and analyze OpenVMS storage performance data.

This is a sample screencast. I am playing this using YouTube to host the videos:

Hope this works!

It would be wonderful if we could purchase whatever we want. I would love to have a Tier 1 storage array next to my computer. But somehow the cost of the purchase and running it just does not make sense for my own small environment.

Equation of Amdahl's Law

Equation: Amdahl's Law

So, how do we decide where to focus our efforts and funds? Here is where we can use Amdahl’s Law to try to obtain a feeling for where we should focus. In Amdahl’s Law, the speed increase (s) can be calculated if we know the fraction of the time (f) we use the faster mode and the amount of speed increase (k) while in the faster mode.

Let’s assume we have two different arrays. One array has 10K drives and the other has 15K drives. Certainly, if we upgrade the array with 10K drives we will get a 50% increase in performance, right? Right?

Well, not really. Here’s why. Let’s assume that our application processes data sequentially. For example, let’s assume we have two arrays and we shadow data between the two arrays. In that case, if we drop the member on the array with 10K drives and then use that member for backup operations, most of our backups will be fairly sequential in nature. We do a large block transfer (32K or 64K in size) and we tend to start at the beginning of the volume and head to the end of the volume.

Sure fragmentation will tend to create some randomness, but as long as the volume is not TOO fragmented, it tends to read from start and reads until the end of the volume. So the reads are very sequential in nature.

How does that change things? Well, in this situation, the storage actually spends very little time doing random I/O requests. It does not matter all that much how fast the heads spin. The heads are usually ready to read the next segment. And most arrays will prestage data into the array cache. Thus, having faster drives just does not matter. So, in this case of Amdahl’s Law shows a tiny speed increase (s) because we also spend very little time (f) in the increased mode. Though the speed increase while seeking is almost 50% greater (0.5), the fact is due to the little amount of time spent seeking, the actual improvement for performance is minuscule.

But what if the application does a huge amount of random I/O requests (such as for interactive lookups of data within a database). In that case, the improvement in performance, while not the full 50% (0.5), it is high enough to warrant the change. For example, those same two arrays, with an equal workload against them (using Host Based Volume Shadowing), will tend to see about a 20 to 25% increase against the array with 15K drives. OpenVMS automatically prefers the shadowset member with lower I/O requests and a lower latency. That random I/O request workload presented against the 15K based member will be better able to quickly respond and return data.

So, we can use Amdahl’s Law when we examine service centers throughout the computing and storage subsystem to try to identify the Best “Bang for the Buck”. By examining the potential improvement we can improve the return on investment (ROI).

And that’s the magic of Amdahl’s Law for storage performance analysis. The next blog entry will examine the impact of resource utilization on the responsiveness of work through the service centers.

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