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Over the next few columns, we will continue our discussion on data storage systems and look at how they are evolving to cater to the world of data-centric computing.

Last month, we had started our discussion of storage systems by looking at various concepts like SAN, NAS, etc. In this column, we will look at the concept of scale-up vs scale-out storage, and discuss their relative advantages and disadvantages.

Scale-up vs scale-out storage
Most of us are familiar with the concepts of scale-up and scale-out computing as they apply to server computing. A scale-up server is a single system with high CPU power and huge memory, and hence is able to handle increasing workloads. As the load on the system increases, it can be scaled up by adding more CPUs, more memory and storage to the single system, which is typically a shared memory system. The work is usually done by multiple threads and processes running on the same system, and they typically communicate through shared memory. This is a shared-everything system. On the other hand, scale-out computing is when multiple nodes are part of the system, and they all act together to handle the workload, which is typically partitioned across the nodes. There is usually no shared memory across the nodes they communicate through explicit messages over the network. Each node has both processing and storage elements. This is normally a shared-nothing approach, where the nodes interact in a loosely coupled manner. The scale-out system can be scaled up to handle additional loads by adding more nodes to it; the work can be distributed across newly added nodes transparently, without the need to bring down the system.

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Now, scale-out and scale-up storage systems have the same conceptual difference. In a scale-up storage system, you add more and more storage capacity to the single storage node to meet increasing storage requirements. This can be achieved by adding many individual disk drives to a storage controller (or a pair of storage controllers so as to ensure failover and high availability). Since the storage capacity (i.e., number of individual disk drives) that a storage controller can support is limited, if storage requirements exceed that capacity, the only option is to move to the next bigger controller, with a higher capacity. With scale-out storage, this problem is avoided, since each node has its own storage controller, and as you add more nodes to meet increasing storage demands, it allows the controller architecture to grow as well. Scale-out storage is typically marketed under the slogan, ‘Pay as you grow’, since it allows you to add nodes as and when your storage requirements increase, instead of having to over-provision from the beginning itself.

The next question that arises is about performance and cost. How do scale-up and scale-out storage systems compare in terms of performance? Before that, we need to understand what the common measures of performance for storage systems are. For computing systems, you can specify its performance in terms of its clock frequency (1/clock frequency gives the clock cycle period) and Cycles Per Instruction (CPI). It is possible to approximate the execution time for a program as (clock period * CPI * Instruction Count). For a computation system, the number of operations/instructions completed per second is a performance measure. This is typically specified as how many MIPS (million instructions per second) or how many FLOPS (floating point operations per second) the system can execute. For storage systems, the typical performance measures used are:

  • Throughput
  • IOPS (number of Input/Output Operations Per Second), and
  • Average latency of an operation.

The most common measure of a storage system be it a simple SATA drive connected directly to your PC, or a complex storage array controller connected through SCSI is how much maximum throughput it can deliver. This is measured as data transfer per second (megabytes per second or MBps). IOPS is the number of IO operations that can happen per second on the storage device, and is defined as the total number of read/write operations that can be performed in one second on the storage device in question. An IO operation request will take a certain time to complete. This can be specified in terms of average latency of an IO operation. Each IO operation is an IO request for a certain size of IO. In simple terms, an IO request can be a read operation of X bytes or a write operation of Y bytes. Hence, each IO request is characterised by its type and size. Now, we can approximate the throughput/data transferred into and out of storage systems as:

Throughput = IOPS * average latency of IO operation * average request size

The IO request latency is limited by a number of factors, including the physical factors associated with the storage system. Let us consider a trivial example where the storage system is the traditional hard disk. Here the IO latency is limited by the rotational delay of the disk, the seek time of the disk and data transfer latency. The rotational delay is governed by the disk’s rotational speed, which is expressed in Revolutions Per Minute (RPM) and indicates the amount of time it takes to get the right sector under the disk head. The seek time is governed by the time it takes for the disk head to position itself on the right track on the disk. Once the disk head is in the right position, it can start reading/writing the data. The delay incurred in reading/writing the data is the data transfer latency. All these three components contribute to the IO request latency.

Depending on the purpose of the storage system, any or all of these performance measures would be significant. For instance, if you are using the storage system for backup/archival purposes, you may be more concerned with overall IO throughput rather than the individual IO request latency. On the other hand, if the purpose is to serve data to a latency-sensitive/client-facing application such as a database, IO request latency would become a significant measure of performance for the storage system under consideration. Hence, different performance measures are applicable for different workloads being deployed on the storage system.

Coming back to comparing scale-out storage and scale-up storage, each has its own performance benefits. Scale-up storage can offer good IO request latencies, while scale-out can offer good IO throughputs. Since scale-out storage is a distributed system, it can suffer from greater communication costs incurred between the nodes. For instance, consider a distributed file system supported on scale-out storage. Both the data and the meta-data associated with each file can be distributed over different nodes on the storage system. For instance, consider a simple example where you are trying to access a file /dir1/file1. It is possible that dir1 is located on node1 and file1 is located on node2. Hence, the file access operation spans multiple nodes in the scale-out storage system, which leads to greater communication costs and a longer request latency.

Here is a question for our readers to think about. Given that the file access operation can span multiple nodes as in the example mentioned earlier, let us assume that we are trying to delete the file located at /dir1/file1 in this example. This operation spans two nodes. How can we ensure that the operation is atomic, even if one of the nodes fails while the operation is in progress?

My must-read book for this month
This month’s must-read book suggestion comes from one of our readers, Ravi Krishnan. He recommends the book ‘Unix Filesystems: Evolution, Design, and Implementation’ by Steve Pate. This book discusses how various file systems have evolved over time, and gives detailed information on file systems internals. Thanks Ravi, for your suggestion.

If you have a favourite programming book/article that you think is a must-read for every programmer, please do send me a note with the book’s name, and a short write-up on why you think it is useful, so I can mention it in the column. This would help many readers who want to improve their software skills.

If you have any favourite programming questions/software topics that you would like to discuss on this forum, please send them to me, along with your solutions and feedback, at sandyasm_AT_yahoo_DOT_com. Till we meet again next month, happy programming and here’s wishing you the very best!

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Sandya Mannarswamy
Sandya Mannarswamy
The author is a specialist in compiler optimisation, and works at Hewlett-Packard India. She has a number of publications and patents to her credit, and her areas of interest include virtualisation technologies and software development tools. You can reach her directly at


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