While many storage clouds are built as multi-purpose clouds, e.g. to store backup files, images, documents etc., but a cloud builder may be tasked to build and optimize the cloud for a specific purpose. E.g. a storage cloud made for cloud backup may optimize for large object sizes and frequent writes, whereas a storage cloud built for storing images may be optimized for relatively smaller objects with frequent reads.
OpenStack Swift provides a versatile platform to build storage clouds for various needs. As we discussed in our last blog, a cloud builder can choose faster I/O devices for storing their Container database to enhance performance under some scenarios. However, a careful analysis is required to determine under what scenarios the investment in the faster I/O devices for the container DB makes sense. More broadly, we are interested in how to properly provision the Swift cloud for different workloads.
In the first part of this blog, we will focus on how to provision the I/O devices for the container DB. After that, our discussion will be generalized on how to provision the storage nodes under the workloads that contain either small or large objects. We understand that in the real world, the object sizes in a workload may be varied in a wide range. However, in order to study the broad question of provisioning the Swift cloud, it is instructive
to consider and separate two extreme workloads in which most objects in a workload are either pebble-sized or boulder-sized.
We will first present the experiments to show how to provision the I/O devices for the container DB with the workloads differing in object sizes.
As we did in
our last blog, we use Swift-bench as the workload generator to benchmark the Swift cloud in terms of # PUT operations per second. We configured Swift bench for our experiments as follows:
object_size: we use 10KB or 1MB as the average object size to simulate two different workloads: (1) the average size of the objects in the workload is relatively small. (2) The average size of the objects in the workload is relatively large. Some real-world examples of small objects could be the PDF, MS Word documents or the JPEG-format pictures. While the backup or archiving data is usually large in size. (Note that: the real workloads in productions may have even larger average object size. But comparing Swift’s behavior for 10KB sized objects vs. 1MB sized objects provides useful insights to predict behavior as size of objects gets larger. Also, an application like Amanda Enterprise will typically chunk the archives into smaller objects before transferring to the cloud.)
concurrency: we set this parameter to 500 in order to saturate the Swift cloud.
num_container: we use 10 containers in our experiments. This may e.g. imply that there are 10 users of this storage cloud.
num_put: when the object size is 10KB, we upload (PUT) 10 million of such objects to the Swift cloud. However, when the object size is 1MB, we upload (PUT) 100K of such objects. As discussed in , the performance of container DB degrades (e.g. to 5-10 updates per second for the container DB) when the number of objects in each container is in the order of magnitude of millions. Since we have 10 containers, our target is to have 1 million objects in each container. So, we set 10 million for the num_put parameter for 10 KB objects. In order to have an equivalent total upload size, we set the num_put parameter to 100K when we upload 1MB objects.
Two types of EC2 instances are used to implement a small-scale Swift cloud with 1 proxy node and 4 storage nodes.
Proxy node: EC2 Cluster Compute Quadruple Extra large Instance (23 GB of memory, 33.5 EC2 Compute Units)
Storage node: High-CPU Extra large Instance (7GB of memory, 20 EC2 Compute Units)
Recently, AWS released the new EBS volume based on the Provisioned IOPS, which lets the AWS user specify the IOPS (
from 100 to 1000) for each EBS volume that will be attached to an EC2 instance. For example, an EBS with 1000 IOPS indicates that it can achieve a maximum of 1000 IOPS (for 16KB I/O request size) regardless of the I/O access pattern. So, a cloud builder can experiment with an EBS volume with higher IOPS to simulate a faster I/O device.
As mentioned in our last blog, the current version of Swift-bench only allows using 1 account. But an unlimited number of containers can be stored in that account. So, our benchmark is executed based on the following sequence: log into 1 existing account, then create 10 containers, and then upload 10 million or 100K objects (depending on the object size). In our experiments, we measure the upload (PUT) operations per second.
Two implementations of Swift cloud are compared: (1) Swift with 1000-IOPS based container DB (We call this 1000-IOPS Swift) and (2) Swift with 500-IOPS based container DB (We call this 500-IOPS Swift).
The 1000-IOPS Swift is implemented with 1 proxy node and 4 storage nodes. Each storage node attaches 9 of 1000-IOPS EBS volumes for storing all objects, 1 of 200-IOPS EBS volume for storing the account DB and 1 of 1000-IOPS EBS for storing the container DB.
The 500-IOPS Swift is implemented with 1 proxy node and 4 storage nodes. Each storage node attaches 9 of 1000-IOPS EBS volumes for storing all objects, 1 of 200-IOPS EBS volume for storing the account DB and 1 of 500-IOPS EBS for storing the container DB.
The proxy node has 10Gbps Ethernet, while the storage node has 1Gbps Ethernet.
We use OpenStack Swift version 1.6.1 and the authentication method on proxy node is TempAuth. All proxy, container, account and object-related parameters are set to Defaults, except: in proxy-server.conf, #worker = 500; in account-server.conf, # workers = 32; in container-server.conf, #worker = 32 and db_preallocation = on; in object-server.conf, # workers = 32.
The Swift-bench, proxy and authentication services run on the proxy node and we ensure that the proxy server is never the bottleneck of the Swift cloud. The account, container, object and rsync services run on the storage nodes.
The number of replicas in the Swift cloud is set to two and the main memory of each node is fully utilized for caching the files and data.
Figure 1 show the operation rate (operations per second on the Y-axis) of the PUT operation for the two Swift implementations over the benchmark window, when the object size is 10KB. Overall, as seen from Figure 1, we notice that when the object size is set to 10KB, the 1000-IOPS Swift achieves higher operation rate than the 500-IOPS, and 68% extra operation rate is observed when 10 million objects have been uploaded.
Figure 1: Comparing two Swift implementations when the object size is 10KB
To compare with Figure 1, we also plot the operation rate of the PUT operation when the object size 1MB, as shown in Figure 2.
Figure 2: Comparing two Swift implementations when the object size is 1MB
In contrast with Figure 1, the two Swift implementations show the same performance when the object size is 1MB. Moreover, in Figure 1, when the objects (10KB size) are being uploaded, the performance of the two Swift implementations kept decreasing from first object upload onwards.. However, when the object size is 1MB (see Figure 2), the performance of the two Swift implementations increases initially and then becomes stable after the initial stage.
We conclude the results in Figure 1 and Figure 2 as follow:
(1) For the upload workload that mostly contains small objects (e.g. 10KB in our test), it is a good practice to use faster I/O devices for the container DB, because each small object can be quickly uploaded to I/O devices, the container DB should have a faster I/O device to keep up with the fast speed of uploading small objects.
(2) For the upload workload that mostly contains larger objects, using faster I/O devices for the container DB does not make much sense. This is because the storage node spends more time on storing the large objects to the I/O devices and consequently, the update frequency of the container DB is relatively slow. So, there is no need to supply the container DB with faster I/O devices.
Besides the discussion on how to provision the I/O device for the container DB, we also want to discuss how to provision other types of resources in the storage node for these two workloads. To this end, we also monitored the CPU usage, network bandwidth and the I/O devices (that are used for storing the objects) of the storage node during the runs of our benchmarks and summarize our observations below.
CPU: Comparing to case of uploading large objects, we note that the CPU usage is higher when the small objects are being uploaded. The reason is the object service is much busier to handle the newly uploaded small objects every second. (2) the container service has to deal with more updates generated from the container DB. Thus, more CPU resource in the storage node will be consumed when uploading the small objects.
Network bandwidth: Uploading large objects will consume more network bandwidth. This is can be verified by Figure 1 and Figure 2: in Figure 1, when the 10 millions of objects are uploaded, the operation rate of 1000-IOPS Swift is 361 and the object size is 10KB, so the total network bandwidth is about 3.5 MB/s. However, while uploading the large objects (see Figure 2), when 100K of objects are uploaded, the operation rate of 1000-IOPS Swift is 120 and the object size is 1MB, so the total network bandwidth is around 120 MB/s.
I/O devices for storing the objects: The I/O pattern of those I/O devices is more random when the small objects are being uploaded. This can be verified by Figure 3, where we plot the distribution of the logical block distance (LBN) distance between two successive I/Os. As seen from Figure 3, when uploading the objects of 1MB size, only 9% of successive I/Os are separated more than 2.5 million LBN away. However, for the case of uploading the objects of 10KB size, about 38% of successive I/Os are more than 2.5 million LBN away. So, this comparison shows that the I/O pattern generated by uploading 1MB objects is much less random. For reference we also plot the pattern for a large sequential write on the same storage node. We observe that for the case of uploading 1MB objects, 70% of successive I/Os are more than 80 and less than 160 LBN away, which is also the range where most of the successive I/Os for Sequential Write fall into.
Figure 3: The distribution of logical block number (LBN) between two successive I/Os for the 1MB object size and 1KB object size. (“M” denotes Million in x-axis)
To summarize, the important take-away points from the above discussion are:
For the upload workload that mostly contains small objects (pebbles), it will be rewarded for higher operation rate by provisioning the storage node with faster CPU, faster I/O devices for the container DB and only moderate network bandwidth. We should avoid using I/O devices with very low random IOPS for storing the objects, because the I/O pattern from those I/O devices is not sequential, and this will become the bottleneck of the storage node. So, use of SSDs can be considered for this workload.
For the upload workload that mostly contains the large objects, it is adequate to provision the storage node with the commodity CPU and moderate I/O speed for the container DB. In order to have better throughput (MB/s) of the Swift cloud, it is recommended to choose a large bandwidth network, and the I/O devices with high sequential throughput (MB/s) for storing the objects. IOPs are not critical for this workload, so standard SATA drives may be sufficient.
Of course, these choices have to be aligned with higher level choices e.g. number of storage and proxy nodes. Overall, cloud builders can benefit from the optimization practices we mentioned in our Swift Advisor blog.
If you are thinking of putting together a storage cloud, we would love to discuss your challenges and share our observations. Please drop us a note at email@example.com