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Performance of Storage I/O Control (SIOC) with SSD Datastores – vSphere 6.5

With Storage I/O Control (SIOC), vSphere 6.5 administrators can adjust the storage performance of VMs so that VMs with critical workloads will get the I/Os per second (IOPS) they need. Admins assign shares (the proportion of IOPS allocated to the VM), limits (the upper bound of VM IOPS), and reservations (the lower bound of VM IOPS) to the VMs whose IOPS need to be controlled. After shares, limits, and reservations have been set, SIOC is automatically triggered to meet the desired policies for the VMs.

A recently published paper shows the performance of SIOC meets expectations and successfully controls the number of IOPS for VM workloads.

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VMware Virtual SAN Stretched Cluster Best Practices White Paper

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VMware Virtual SAN 6.1 introduced the concept of a stretched cluster which allows the Virtual SAN customer to configure two geographically located sites, while synchronously replicating data between the two sites. A technical white paper about the Virtual SAN stretched cluster performance has now been published. This paper provides guidelines on how to get the best performance for applications deployed on a Virtual SAN stretched cluster environment.

The chart below, borrowed from the white paper, compares the performance of the Virtual SAN 6.1 stretched cluster deployment against the regular Virtual SAN cluster without any fault domains. A nine- node Virtual SAN stretched cluster is considered with two different configurations of inter-site latency: 1ms and 5ms. The DVD Store benchmark is executed on four virtual machines on each host of the nine-node Virtual SAN stretched cluster. The DVD Store performance metrics of cumulated orders per minute in the cluster, read/write IOPs, and average latency are compared with a similar workload on the regular Virtual SAN cluster. The orders per minute (OPM) is lower by 3% and 6% for the 1ms and 5ms inter-site latency stretched cluster compared to the regular Virtual SAN cluster.

Figure 1a. DVD Store orders per minute in the cluster and guest IOPS comparison

Guest read/write IOPS and latency were also monitored. The read/write mix ratio for the DVD Store workload is roughly at 1/3 read and 2/3 write. Write latency shows an obvious increase trend when the inter-site latency is higher, while the read latency is only marginally impacted. As a result, the average latency increases from 2.4ms to 2.7ms, and 5.1ms for 1ms and 5ms inter-site latency configuration.

Figure 1b. DVD Store latency comparison

These results demonstrate that the inter-site latency in a Virtual SAN stretched cluster deployment has a marginal performance impact on a commercial workload like DVD Store. More results are available in the white paper.

Virtualized Storage Performance: RAID Groups versus Storage pools

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RAID, a redundant array of independent disks, has traditionally been the foundation of enterprise storage. Grouping multiple disks into one logical unit can vastly increase the availability and performance of storage by protecting against disk failure, allowing greater I/O parallelism, and pooling capacity. Storage pools similarly increase the capacity and performance of storage, but are easier to configure and manage than RAID groups.

RAID groups have traditionally been regarded as offering better and more predictable performance than storage pools. Although both technologies were developed for magnetic hard disk drives (HDDs), solid-state drives (SSDs), which use flash memory, have become prevalent. Virtualized environments are also common and tend to create highly randomized I/O given the fact that multiple workloads are run simultaneously.

We set out to see how the performance of RAID group and storage pool provisioning methods compare in today’s virtualized environments.

First, let’s take a closer look at each storage provisioning type.

RAID Groups

A RAID group unifies a number of disks into one logical unit and distributes data across multiple drives. RAID groups can be configured with a particular protection level depending on the performance, capacity, and redundancy needs of the environment. LUNs are then allocated from the RAID group. RAID groups typically contain only identical drives, and the maximum number of disks in a RAID group varies by system model but is generally below fifty. Because drives typically have well defined performance characteristics, the overall RAID group performance can be calculated as the performance of all drives in the group minus the RAID overhead. To provide consistent performance, workloads with different I/O profiles (e.g., sequential vs. random I/O) or different performance needs should be physically isolated in different RAID groups so they do not share disks.

Storage Pools

Storage pools, or simply ‘pools’, are very similar to RAID groups in some ways. Implementation varies by vendor, but generally pools are made up of one or more private RAID groups, which are not visible to the user, or they are composed of user-configured RAID groups which are added manually to the pool. LUNs are then allocated from the pool. Storage pools can contain up to hundreds of drives, often all the drives in an array. As business needs grow, storage pools can be easily scaled up by adding drives or RAID groups and expanding LUN capacity. Storage pools can contain multiple types and sizes of drives and can spread workloads over more drives for a greater degree of parallelism.

Storage pools are usually required for array features like automated storage tiering, where faster SSDs can serve as a data cache among a larger group of HDDs, as well as other array-level data services like compression, deduplication, and thin provisioning. Because of their larger maximum size, storage pools, unlike RAID groups, can take advantage of vSphere 6 maximum LUN sizes of 64TB.

We used two benchmarks to compare the performance of RAID groups and storage pools: VMmark, which is a virtualization platform benchmark, and I/O Analyzer with Iometer, which is a storage microbenchmark. VMmark is a multi-host virtualization benchmark that uses diverse application workloads as well as common platform level workloads to model the demands of the datacenter. VMs running a complete set of the application workloads are grouped into units of load called tiles. For more details, see the VMmark 2.5 overview. Iometer places high levels of load on the disk, but does not stress any other system resources. Together, these benchmarks give us both a ‘real-world’ and a more focused perspective on storage performance.

VMmark Testing

Array Configuration

Testing was conducted on an EMC VNX5800 block storage SAN with Fibre Channel. This was one of the many storage solutions which offered both RAID group and storage pool technologies. Disks were 200GB single-level cell (SLC) SSDs. Storage configuration followed array best practices, including balancing LUNs across Storage Processors and ensuring that RAID groups and LUNs did not span the array bus. One way to optimize SSD performance is to leave up to 50% of the SSD capacity unutilized, also known as overprovisioning. To follow this best practice, 50% of the RAID group or storage pool was not allocated to any LUN. Since overprovisioning SSDs can be an expensive proposition, we also tested the same configuration with 100% of the storage pool or RAID group allocated.

RAID Group Configuration

Four RAID 5 groups were used, each composed of 15 SSDs. RAID 5 was selected for its suitability for general purpose workloads. RAID 5 provides tolerance against a single disk failure. For best performance and capacity, RAID 5 groups should be sized to multiples of five or nine drives, so this group maintains a multiple of the preferred five-drive count. One LUN was created in each of the four RAID groups. The LUN was sized to either 50% of the RAID group (Best Practices) or 100% (Fully Allocated). For testing, the capacity of each LUN was fully utilized by VMmark virtual machines and randomized data.

Storage Pool Configuration

A single RAID 5 Storage Pool containing all 60 SSDs was used. Four thick LUNs were allocated from the pool, meaning that all of the storage space was reserved on the volume. LUNs were equivalent in size and consumed a total of either 50% (Best Practices) or 100% (Fully Allocated) of the pool capacity.

Storage Layout

Most of the VMmark storage load was created by two types of virtual machines: database (DVD Store) and mail server (Microsoft Exchange). These virtual machines were isolated on two different LUNs. The remaining virtual machines were spread across the remaining two LUNs. That is, in the RAID group case, storage-heavy workloads were physically isolated in different RAID groups, but in the storage pool case, all workloads shared the same pool.

Systems Under Test:	Two Dell PowerEdge R720 servers
Configuration Per Server:
Virtualization Platform:	VMware vSphere 6.0. VMs used virtual hardware version 11 and current VMware Tools.
CPUs:	Two 12-core Intel^® Xeon^® E5-2697 v2 @ 2.7 GHz, Turbo Boost Enabled, up to 3.5 GHz, Hyper-Threading enabled.
Memory:	256GB ECC DDR3 @ 1866MHz
Host Bus Adapter:	QLogic ISP2532 DualPort 8Gb Fibre Channel to PCI Express
Network Controller:	One Intel 82599EB dual-port 10 Gigabit PCIe Adapter, one Intel I350 Dual-Port Gigabit PCIe Adapter

Each configuration was tested at three different load points: 1 tile (the lowest load level), 7 tiles (an approximate mid-point), and 13 tiles, which was the maximum number of tiles that still met Quality of Service (QoS) requirements. All datapoints represent the mean of two tests of each configuration.

VMmark Results

RAID Group vs. Storage Pool Performance comparison using VMmark benchmark

Across all load levels tested, the VMmark performance score, which is a function of application throughput, was similar regardless of storage provisioning type. Neither the storage type used nor the capacity allocated affected throughput.

VMmark 2.5 performance scores are based on application and infrastructure workload throughput, while application latency reflects Quality of Service. For the Mail Server, Olio, and DVD Store 2 workloads, latency is defined as the application’s response time. We wanted to see how storage configuration affected application latency as opposed to the VMmark score. All latencies are normalized to the lowest 1-tile results.

Storage configuration did not affect VMmark application latencies.

Lastly, we measured read and write I/O latencies: esxtop Average Guest MilliSec/Write and Average Guest MilliSec/Read. This is the round trip I/O latency as seen by the Guest operating system.

No differences emerged in I/O latencies.

I/O Analyzer with Iometer Testing

In the second set of experiments, we wanted to see if we would find similar results while testing storage using a synthetic microbenchmark. I/O Analyzer is a tool which uses Iometer to drive load on a Linux-based virtual machine then collates the performance results. The benefit of using a microbenchmark like Iometer is that it places heavy load on just the storage subsystem, ensuring that no other subsystem is the bottleneck.

Configuration

Testing used a VNX5800 array and RAID 5 level as in the prior configuration, but all storage configurations spanned 9 SSDs, also a preferred drive count. In contrast to the prior test, the storage pool or RAID group spanned an identical number of disks, so that the number of disks per LUN was the same in both configurations. Testing used nine disks per LUN to achieve greater load on each disk.

The LUN was sized to either 50% or 100% of the storage group. The LUN capacity was fully occupied with the I/O Analyzer worker VM and randomized data. The I/O Analyzer Controller VM, which initiates the benchmark, was located on a separate array and host.

Testing used one I/O Analyzer worker VM. One Iometer worker thread drove storage load. The size of the VM’s virtual disk determines the size of the active dataset, so a 100GB thick-provisioned virtual disk on VMFS-5 was chosen to maximize I/O to the disk and minimize caching. We tested at a medium load level using a plausible datacenter I/O profile, understanding, however, that any static I/O profile will be a broad generalization of real-life workloads.

Iometer Configuration

1 vCPU, 2GB memory
70% read, 30% write
100% random I/O to model the “I/O blender effect” in a virtualized environment
4KB block size
I/O aligned to sector boundaries
64 outstanding I/O
60 minute warm up period, 60 minute measurement period

Systems Under Test:	One Dell PowerEdge R720 server
Configuration Per Server:
Virtualization Platform:	VMware vSphere 6.0. Worker VM used the I/O Analyzer default virtual hardware version 7.
CPUs:	Two 12-core Intel^® Xeon^® E5-2697 v2 @ 2.7 GHz, Turbo Boost Enabled, up to 3.5 GHz, Hyper-Threading enabled.
Memory:	256GB ECC DDR3 @ 1866MHz
Host Bus Adapter:	QLogic ISP2532 DualPort 8Gb Fibre Channel to PCI Express

Iometer results

Iometer Throughput Results Storage Comparison RAID Group vs Storage Pool

In Iometer testing, the storage pool showed slightly improved performance compared to the RAID group, and the amount of capacity allocated also did not affect performance.

In both our multi-workload and synthetic microbenchmark scenarios, we did not observe any performance penalty of choosing storage pools over RAID groups on an all-SSD array, even when disparate workloads shared the same storage pool. We also did not find any performance benefit at the application or I/O level from leaving unallocated capacity, or overprovisioning, SSD RAID groups or storage pools. Given the ease of management and feature-based benefits of storage pools, including automated storage tiering, compression, deduplication, and thin provisioning, storage pools are an excellent choice in today’s datacenters.

Virtual SAN 6.0 Performance: Scalability and Best Practices

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A technical white paper about Virtual SAN performance has been published. This paper provides guidelines on how to get the best performance for applications deployed on a Virtual SAN cluster.

We used Iometer to generate several workloads that simulate various I/O encountered in Virtual SAN production environments. These are shown in the following table.

Type of I/O workload	Size (1KiB = 1024 bytes)	Mixed Ratio	Shows / Simulates
All Read	4KiB	–	Maximum random read IOPS that a storage solution can deliver
Mixed Read/Write	4KiB	70% / 30%	Typical commercial applications deployed in a VSAN cluster
Sequential Read	256KiB	–	Video streaming from storage
Sequential Write	256KiB	–	Copying bulk data to storage
Sequential Mixed R/W	256KiB	70% / 30%	Simultaneous read/write copy from/to storage

In addition to these workloads, we studied Virtual SAN caching tier designs and the effect of Virtual SAN configuration parameters on the Virtual SAN test bed.

Virtual SAN 6.0 can be configured in two ways: Hybrid and All-Flash. Hybrid uses a combination of hard disks (HDDs) to provide storage and a flash tier (SSDs) to provide caching. The All-Flash solution uses all SSDs for storage and caching.

Tests show that the Hybrid Virtual SAN cluster performs extremely well when the working set is fully cached for random access workloads, and also for all sequential access workloads. The All-Flash Virtual SAN cluster, which performs well for random access workloads with large working sets, may be deployed in cases where the working set is too large to fit in a cache. All workloads scale linearly in both types of Virtual SAN clusters—as more hosts and more disk groups per host are added, Virtual SAN sees a corresponding increase in its ability to handle larger workloads. Virtual SAN offers an excellent way to scale up the cluster as performance requirements increase.

You can download Virtual SAN 6.0 Performance: Scalability and Best Practices from the VMware Performance & VMmark Community.

Docker Containers Performance in VMware vSphere

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by Qasim Ali, Banit Agrawal, and Davide Bergamasco

“Containers without compromise.” This was one of the key messages at VMworld 2014 USA in San Francisco. It was presented in the opening keynote, and then the advantages of running Docker containers inside of virtual machines were discussed in detail in several breakout sessions. These include security/isolation guarantees and also the existing rich set of management functionalities. But some may say, “These benefits don’t come for free: what about the performance overhead of running containers in a VM?”

A recent report compared the performance of a Docker container to a KVM VM and showed very poor performance in some micro-benchmarks and real-world use cases: up to 60% degradation. These results were somewhat surprising to those of us accustomed to near-native performance of virtual machines, so we set out to do similar experiments with VMware vSphere. Below, we present our findings of running Docker containers in a vSphere VM and in a native configuration. Briefly,

We find that for most of these micro-benchmarks and Redis tests, vSphere delivered near-native performance with generally less than 5% overhead.
Running an application in a Docker container in a vSphere VM has very similar overhead of running containers on a native OS (directly on a physical server).

Next, we present the configuration and benchmark details as well as the performance results.

Deployment Scenarios

We compare four different scenarios as illustrated below:

Native: Linux OS running directly on hardware (Ubuntu, CentOS)
vSphere VM: Upcoming release of vSphere with the same guest OS as native
Native-Docker: Docker version 1.2 running on a native OS
VM-Docker: Docker version 1.2 running in guest VM on a vSphere host

In each configuration all the power management features are disabled in the BIOS and Ubuntu OS.

Figure 1. Different test scenarios

Benchmarks/Workloads

For this study, we used the micro-benchmarks listed below and also simulated a real-world use case.

Micro-benchmarks:

LINPACK: This benchmark solves a dense system of linear equations. For large problem sizes it has a large working set and does mostly floating point operations.
STREAM: This benchmark measures memory bandwidth across various configurations.
FIO: This benchmark is used for I/O benchmarking for block devices and file systems.
Netperf: This benchmark is used to measure network performance.

Real-world workload:

Redis: In this experiment, many clients perform continuous requests to the Redis server (key-value datastore).

For all of the tests, we run multiple iterations and report the average of multiple runs.

Performance Results

LINPACK

LINPACK solves a dense system of linear equations (Ax=b), measures the amount of time it takes to factor and solve the system of N equations, converts that time into a performance rate, and tests the results for accuracy. We used an optimized version of the LINPACK benchmark binary based on the Intel Math Kernel Library (MKL).

Hardware: 4 socket Intel Xeon E5-4650 2.7GHz with 512GB RAM, 32 total cores, Hyper-Threading disabled
Software: Ubuntu 14.04.1 with Docker 1.2
VM configuration: 32 vCPU VM with 45K and 65K problem sizes

Figure 2. LINPACK performance for different test scenarios

We disabled HT for this run as recommended by the benchmark guidelines to get the best peak performance. For the 45K problem size, the benchmark consumed about 16GB memory. All memory was backed by transparent large pages. For VM results, large pages were used both in the guest (transparent large pages) and at the hypervisor level (default for vSphere hypervisor). There was 1-2% run-to-run variation for the 45K problem size. For 65K size, 33.8GB memory was consumed and there was less than 1% variation.

As shown in Figure 2, there is almost negligible virtualization overhead in the 45K problem size. For a bigger problem size, there is some inherent hardware virtualization overhead due to nested page table walk. This results in the 5% drop in performance observed in the VM case. There is no additional overhead of running the application in a Docker container in a VM compared to running the application directly in the VM.

STREAM

We used a NUMA-aware STREAM benchmark, which is the classical STREAM benchmark extended to take advantage of NUMA systems. This benchmark measures the memory bandwidth across four different operations: Copy, Scale, Add, and Triad.

Hardware: 4 socket Intel Xeon E5-4650 2.7GHz with 512GB RAM, 32 total cores, HT enabled
Software: Ubuntu 14.04.1 with Docker 1.2
VM configuration: 64 vCPU VM (Hyper-Threading ON)

Figure 3. STREAM performance for different test scenarios

We used an array size of 2 billion, which used about 45GB of memory. We ran the benchmark with 64 threads both in the native and virtual cases. As shown in Figure 3, the VM added about 2-3% overhead across all four operations. The small 1-2% overhead of using a Docker container on a native platform is probably in the noise margin.

FIO

We used Flexible I/O (FIO) tool version 2.1.3 to compare the storage performance for the native and virtual configurations, with Docker containers running in both. We created a 10GB file in a 400GB local SSD drive and used direct I/O for all our tests so that there were no effects of buffer caching inside the OS. We used a 4k I/O size and tested three different I/O profiles: random 100% read, random 100% write, and a mixed case with random 70% read and 30% write. For the 100% random read and write tests, we selected 8 threads and an I/O depth of 16, whereas for the mixed test, we select an I/O depth of 32 and 8 threads. We use the taskset to set the CPU affinity on FIO threads in all configurations. All the details of the experimental setup are given below:

Hardware: 2 socket Intel Xeon E5-2660 2.2GHz with 392GB RAM, 16 total cores, Hyper-Threading enabled
Guest: 32-vCPU 14.04.1 Ubuntu 64-bit server with 256GB RAM, with a separate ext4 disk in the guest (on VMFS5 in vSphere run)
Benchmark: FIO, Direct I/O, 10GB file
I/O Profile: 4k I/O, Random Read/Write: depth 16, jobs 8, Mixed: depth 32, jobs 8

Figure 4. FIO benchmark performance for different test scenarios

The figure above shows the normalized maximum IOPS achieved for different configurations and different I/O profiles. For random read in a VM, we see that there is about 2% reduction in maximum achievable IOPS when compared to the native case. However, for the random write and mixed tests, we observed almost the same performance (within the noise margin) compared to the native configuration.

Netperf

Netperf is used to measure throughput and latency of networking operations. All the details of the experimental setup are given below:

Hardware (Server): 4 socket Intel Xeon E5-4650 2.7GHz with 512GB RAM, 32 total cores, Hyper-Threading disabled
Hardware (Client): 2 socket Intel Xeon X5570 2.93GHz with 64GB RAM, 8 cores total, Hyper-Threading disabled
Networking hardware: Broadcom Corporation NetXtreme II BCM57810
Software on server and Client: Ubuntu 14.04.1 with Docker 1.2
VM configuration: 2 vCPU VM with 4GB RAM

The server machine for Native is configured to have only 2 CPUs online for fair comparison with a 2-vCPU VM. The client machine is also configured to have 2 CPUs online to reduce variability. We tested four configurations: directly on the physical hardware (Native), in a Docker container (Native-Docker), in a virtual machine (VM), and in a Docker container inside a VM (VM-Docker). For the two Docker deployment scenarios, we also studied the effect of using host networking as opposed to the Docker bridge mode (default operating mode), resulting in two additional configurations (Native-Docker-HostNet and VM-Docker-HostNet) making total six configurations.

We used TCP_STREAM and TCP_RR tests to measure the throughput and round-trip network latency between the server machine and the client machine using a direct 10Gbps Ethernet link between two NICs. We used standard network tuning like TCP window scaling and setting socket buffer sizes for the throughput tests.

Figure 5. Netperf Recieve performance for different test scenarios

Figure 6. Netperf transmit performance for different test scenarios

Figures 5 and 6 show the unidirectional throughput over a single TCP connection with standard 1500 byte MTU for both transmit and receive TCP_STREAM cases (We used multiple Streams in VM-Docker* transmit case to reduce the variability in runs due to Docker bridge overhead and get predictable results). Throughput numbers for all configurations are identical and equal to the maximum possible 9.40Gbps on a 10GbE NIC.

Figure 7. Netperf TCP_RR performance for different test scenarios (Lower is better)

For the latency tests, we used the latency sensitivity feature introduced in vSphere5.5 and applied the best practices for tuning latency in a VM as mentioned in this white paper. As shown in Figure 7, latency in a VM with VMXNET3 device is only 15 microseconds more than in the native case because of the hypervisor networking stack. If users wish to reduce the latency even further for extremely latency- sensitive workloads, pass-through mode or SR-IOV can be configured to allow the guest VM to bypass the hypervisor network stack. This configuration can achieve similar round-trip latency to native, as shown in Figure 8. The Native-Docker and VM-Docker configuration adds about 9-10 microseconds of overhead due to the Docker bridge NAT function. A Docker container (running natively or in a VM) when configured to use host networking achieves similar latencies compared to the latencies observed when not running the workload in a container (native or a VM).

Figure 8. Netperf TCP_RR performance for different test scenarios (VMs in pass-through mode)

Redis

We also wanted to take a look at how Docker in a virtualized environment performs with real world applications. We chose Redis because: (1) it is a very popular application in the Docker space (based on the number of pulls of the Redis image from the official Docker registry); and (2) it is very demanding on several subsystems at once (CPU, memory, network), which makes it very effective as a whole system benchmark.

Our test-bed comprised two hosts connected by a 10GbE network. One of the hosts ran the Redis server in different configurations as mentioned in the netperf section. The other host ran the standard Redis benchmark program, redis-benchmark, in a VM.

The details about the hardware and software used in the experiments are the following:

Hardware: HP ProLiant DL380e Gen8 2 socket Intel Xeon E5-2470 2.3GHz with 96GB RAM, 16 total cores, Hyper-Threading enabled
Guest OS: CentOS 7
VM: 16 vCPU, 93GB RAM
Application: Redis 2.8.13
Benchmark: redis-benchmark, 1000 clients, pipeline: 1 request, operations: SET 1 Byte
Software configuration: Redis thread pinned to CPU 0 and network interrupts pinned to CPU 1

Since Redis is a single-threaded application, we decided to pin it to one of the CPUs and pin the network interrupts to an adjacent CPU in order to maximize cache locality and avoid cross-NUMA node memory access. The workload we used consists of 1000 clients with a pipeline of 1 outstanding request setting a 1 byte value with a randomly generated key in a space of 100 billion keys. This workload is highly stressful to the system resources because: (1) every operation results in a memory allocation; (2) the payload size is as small as it gets, resulting in very large number of small network packets; (3) as a consequence of (2), the frequency of operations is extremely high, resulting in complete saturation of the CPU running Redis and a high load on the CPU handling the network interrupts.

We ran five experiments for each of the above-mentioned configurations, and we measured the average throughput (operations per second) achieved during each run. The results of these experiments are summarized in the following chart.

Figure 9. Redis performance for different test scenarios

The results are reported as a ratio with respect to native of the mean throughput over the 5 runs (error bars show the range of variability over those runs).

Redis running in a VM has slightly lower performance than on a native OS because of the network virtualization overhead introduced by the hypervisor. When Redis is run in a Docker container on native, the throughput is significantly lower than native because of the overhead introduced by the Docker bridge NAT function. In the VM-Docker case, the performance drop compared to the Native-Docker case is almost exactly the same small amount as in the VM-Native comparison, again because of the network virtualization overhead. However, when Docker runs using host networking instead of its own internal bridge, near-native performance is observed for both the Docker on native hardware and Docker in VM cases, reaching 98% and 96% of the maximum throughput respectively.

Based on the above results, we can conclude that virtualization introduces only a 2% to 4% performance penalty. This makes it possible to run applications like Redis in a Docker container inside a VM and retain all the virtualization advantages (security and performance isolation, management infrastructure, and more) while paying only a small price in terms of performance.

Summary

In this blog, we showed that in addition to the well-known security, isolation, and manageability advantages of virtualization, running an application in a Docker container in a vSphere VM adds very little performance overhead compared to running the application in a Docker container on a native OS. Furthermore, we found that a container in a VM delivers near native performance for Redis and most of the micro-benchmark tests we ran.

In this post, we focused on the performance of running a single instance of an application in a container, VM, or native OS. We are currently exploring scale-out applications and the performance implications of deploying them on various combinations of containers, VMs, and native operating systems. The results will be covered in the next installment of this series. Stay tuned!

Exploring FAST Cache Performance Using VMmark 2.1.1

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A system’s performance is often limited by the access time of its hard disk drive (HDD). Solid-state drives (SSDs), also known as Enterprise Flash Drives (EFDs), tout a superior performance profile to HDDs. In our previous comparison of EFD and HDD technologies using VMmark 2.1, we showed that EFD reads were on average four times faster than HDD reads, while EFD and HDD write speeds were comparable. However, EFDs are more costly per gigabyte.

Many vendors have attempted to address this issue using tiered storage technologies. Here, we tested the performance benefits of EMC’s FAST Cache storage array feature, which merges the strengths of both technologies. FAST Cache is an EFD-based read/write storage cache that supplements the array’s DRAM cache by giving frequently accessed data priority on the high performing EFDs. We used VMmark 2, a multi-host virtualization benchmark, to quantify the performance benefits of FAST Cache. For more details, see the overview, release notes for VMmark 2.1, and release notes for 2.1.1. VMmark 2 is an ideal tool to test FAST Cache performance for virtualized datacenters in that its varied workloads and bursty I/O patterns model the demands of the datacenter. We found that FAST Cache produced remarkable improvements in datacenter capacity and storage access latencies. With the addition of FAST Cache, the system could support twice as much load while still meeting QoS requirements.

FAST Cache
FAST Cache is a feature of EMC’s storage systems that tracks frequently accessed data on disk, promotes the data into an array-wide EFD cache to take advantage of Flash I/O access speeds, then writes it back to disk when the data is superseded in importance. FAST Cache optimizes the use of EFD storage. In most workloads only a small percentage of data will be frequently accessed. This is referred to as the ‘working set.’ An EFD-based cache allows the data in the working set to take advantage of the performance characteristics of EFDs while the rest of the data stays on lower-cost HDDs. Relevant data is rapidly promoted into the cache in increments of 64 KB pages, and a least-recently-used algorithm is used to decide which data to write back to disk.

The benefit achieved with FAST Cache depends on the workload’s I/O profile. As with most caches, FAST Cache will show the most benefit for I/O with a high locality of reference, such as database indices and reference tables. FAST Cache will be least beneficial to workloads with sequential I/O patterns like log files or large I/O size access because these may not access the same 64 KB block multiple times and the FAST Cache would never become populated.

Configuration
Systems Under Test: Four Dell PowerEdge R310 Servers
CPUs (per server): One Quad-Core Intel® Xeon® X3460 @ 2.8 GHz, Hyper-Threading enabled
Memory (per server): 32 GB DDR3 ECC @ 800 MHz
Storage Array: EMC VNX5500
FAST Cache configurations:
366 GB FAST Cache, 8 EFDs, RAID 1
92 GB FAST Cache, 2 EFDs, RAID 1
FAST Cache disabled
LUN configurations:
20 HDDs, 10K RPM, grouped into 3 LUNs of 8, 8, and 4 HDDs each
11 HDDs, 10K RPM, grouped into 3 LUNs of 4, 4, and 3 HDDs each
Hypervisor: ESXi 5.0.0
Virtualization Management: VMware vCenter Server 5.0
VMmark version: 2.1.1

Methodology
We used VMmark 2 to investigate several different factors relating to FAST Cache. We wanted to measure the performance benefit afforded by adding FAST Cache into a VMmark 2 environment and we wanted to observe how the performance benefit of FAST Cache would scale as we changed the size of the cache. We tested with FAST Cache disabled and with two different FAST Cache sizes which were made from two EFDs and eight EFDs in RAID 1, creating a cache of 92 GB and 366 GB usable space, respectively. FAST Cache was configured according to best practices to ensure FAST Cache performance was not limited by array bus bandwidth. After the FAST Cache was created, it was warmed up by repeating VMmark 2 runs until scores showed less than 3% variability between runs.

We also wanted to examine whether FAST Cache could reduce the hardware requirements of our tests. As processors and other system hardware components have increased in capacity and speed, there has been greater and greater pressure for corresponding increasing performance from storage. RAID groups of HDDs have been one answer to these increasing performance demands, as RAID arrays provide performance and reliability benefits over individual disks. In typical RAID configurations, performance increases nearly linearly as disks are added to the RAID group. However, adding disks in order to increase storage access speed can result in underutilization of HDD space, which becomes far greater than required. FAST Cache should allow us to reduce the number of HDDs we require for RAID performance benefits, also reducing the cluster’s total power, cooling and space requirements, which results in lower cost. FAST Cache services the bulk of the workloads’ I/O operations at high speeds, so it is acceptable for us to service the remainder of operations at lower speeds and use only as many HDDs as needed for storage capacity rather than performance.

To test whether an environment with FAST Cache and a reduced number of disks could perform as well as an environment without FAST Cache, but with a larger number of disks, we tested performance with two different disk configurations. Workloads were tested on a set of 20 HDDs and then on a set of 11 HDDs, in both cases grouped into three LUNs. Each LUN was in a distinct RAID 0 group. Due to the performance characteristics of RAID 0, we expected the 20 HDD configuration to have better performance to than the 11 HDD configuration. The placement of workloads onto LUNs was meant to model a naïve environment with nonoptimal storage setup. Two LUNs held workload tile data, and the third smaller LUN served as the destination for VM Deploy and Storage vMotion workloads. The first LUN held VMs from the first and third tiles, and the second LUN held VMs from the second and fourth tiles. Running VMmark 2 with more than one tile per LUN was atypical of our best practices for the benchmark. It created a severe bottleneck for the disk, which was meant to simulate the types of storage performance issues we sometimes see in customer environments.

All VMmark 2 tests were conducted on a cluster of four identically configured entry-level Dell Power Edge R310 servers running ESXi 5.0. All components in the environment besides FAST Cache and number of HDDs remained unchanged during testing.

Results
To characterize cluster performance at multiple load levels, we increased the number of tiles until the cluster reached saturation, defined as when the run failed to meet Quality of Service (QoS) requirements. Scaling out the number of tiles until saturation allows us to determine the maximum VMmark 2 load the cluster could support and to compare performance at each level of load for each cache and storage configuration. All data points are the mean of three tests in each configuration. Scaling data was generated by normalizing every score to the lowest passing score, which was 1 tile with FAST Cache disabled on 20 HDDs.

VMmark 2.1.1 Scaling With and Without FAST Cache

With FAST Cache disabled, the 20 HDD LUNs reached saturation at 2 tiles, and the 11 HDD LUNs were unable to support even 1 tile of load. Because all VMs for each tile were placed on the same LUN, a 1 tile run used one LUN, consisting of only four out of 11 HDDs or eight out of 20 HDDs. 4 HDDs were insufficient to provide the required QoS for even 1 tile. When FAST Cache was enabled, the 11 HDD and 20 HDD configurations supported 4 tiles. This is a remarkable improvement; with the addition of FAST Cache, the system could support twice as much load while still meeting QoS requirements. Even at lower load levels, the equivalent system with FAST Cache was allowing greater throughput and showed resulting increases in the VMmark score of 26% at 1 tile and 31% at 2 tiles. With FAST Cache enabled, the configuration with 11 HDDs performed equivalently to one with 20 HDDs until the system approached saturation.

With FAST Cache enabled, the system supported twice as much load on almost half as many disks. The results show that an environment with a 92 GB FAST Cache was able to greatly outperform a HDD-only environment that contains 82% more disks. At 4 tiles with FAST Cache enabled, the cluster’s CPU utilization was approaching saturation, reaching an average of 84%, but was not yet bottlenecked on storage.

In our tests, performance did not scale up very much as we increased FAST Cache size from 92 GB to 366 GB and the number of HDDs from 11 to 20.

We can see that all configurations scaled very similarly from 1 to 3 tiles with only minor differences appearing, primarily between the 92 GB FAST Cache and 366 GB FAST Cache. Only at the highest load level did performance begin to diverge. Predictably, the largest cache configurations show the best performance at 4 tiles, followed by the smaller cache configurations. To determine whether this performance falloff was directly attributable to the cache size and number of HDDs, we needed to know whether FAST Cache was performing to capacity.

Below are the FAST Cache and DRAM cache hit percentages for read and write operations at the 4 tile load. On average, our VMmark testing had I/O operations of 24% reads and 76% writes.

Click to Enlarge

With the 366 GB FAST Cache, nearly all reads and writes were hitting either the DRAM or FAST Cache. In these cases, the number of backing disks did not affect the score because disks were rarely being accessed. At this cache size, all frequently accessed data fit into the FAST Cache. However, with the 92 GB FAST Cache, the cache hit percentage decreased to 96.5% and 92.1% for the 11 HDD and 20 HDD configurations, respectively. This indicated that the entire working set could no longer fit into the 92 GB FAST Cache. The 11 HDD configuration began to show decreased performance relative to 20 HDDs, because although only 3.5% of total I/O operations were going to disk, the increase in disk latency was large enough to reduce throughput and affect VMmark score. Despite this, a FAST Cache of 92 GB was still sufficient to provide us with VMmark performance that met QoS requirements. The higher read hit percentages in the 11 HDD configuration reflected this reduced throughput. Lower throughput resulted in a smaller working set and an accordingly higher read hit percentage.

Overall, FAST Cache did an excellent job of identifying the working set. Although only 8% of the 1.09 TB dataset could fit in the 92 GB cache at any one time, at least 92% of I/O requests were hitting the cache.

Scaling FAST Cache gave us a sense of the working set size of the VMmark benchmark. As performance with the 92 GB FAST Cache demonstrated a knee at 3 tiles, this suggests the working set size at 3 tiles is less than 92 GB and the working set size at 4 tiles is slightly greater than 92 GB. Knowing the approximate working set size per tile would allow us to select the minimum FAST Cache size required if we wanted our entire working set to fit into the FAST Cache, even if we scaled the benchmark to an arbitrary number of tiles in a different cluster.

The results below show that I/O operations per second and I/O latency were affected by our environment characteristics.

The variability in read latency is clearly affected by both FAST Cache size and number of backing HDDs. Latency is highest with only 11 HDDs and the smaller FAST Cache, and decreases as we add HDDs. Latency decreases even more with the larger FAST Cache size as nearly all reads hit the cache. Write latency, however, is relatively constant across configurations, which is as expected because in each configuration nearly all writes are being served by either the DRAM cache or FAST Cache.

Summary
It’s clear that we can replace a large number of HDDs with a much smaller number of EFDs and get similar or improved performance results. An array with 11 HDDs and FAST Cache outperformed an array with 20 HDDs without FAST Cache. FAST Cache handles the workloads’ performance requirements so that we need only to supply the HDDs necessary for their storage space, rather than performance capabilities. This allows us to reduce the number of HDDs and their associated power, space, cooling, and cost.

Tiered storage solutions like FAST Cache make excellent use of EFDs, even to the extent that 92% or more of our I/O operations are benefitting from Flash-level latencies while the EFD storage itself holds only 8% of our total data. The increased VMmark scores demonstrate the ability of FAST Cache to pinpoint the most active data remarkably well, and, even in a bursty environment, show incredible improvements in I/O latency and in the load that a cluster can support. Our testing showed FAST Cache provides Flash-level storage access speeds to the data that needs it most, reduces storage bottlenecking and increases supported load, making FAST Cache a highly valuable addition to the datacenter.

Single vSphere Host, a Million I/O Operations per Second

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One of the essential requirements for a platform supporting enterprise datacenters is the capability to support the extreme I/O demands of applications running in those datacenters. A previous study has shown that vSphere can easily handle demands for high I/O operations per second. Experiments discussed in a recently published paper strengthen this assertion further by demonstrating that a vSphere 5 virtual platform can easily satisfy an extremely high level of I/O demand that originates from the hosted applications.

Results obtained from performance testing done at EMC lab show that:

A single vSphere 5 host is capable of supporting a million+ I/O operations per second.
300,000 I/O operations per second can be achieved from a single virtual machine.
I/O throughput (bandwidth consumption) scales almost linearly as the request size of an I/O operation increases.
I/O operations on vSphere 5 systems with Paravirtual SCSI (PVSCSI) controllers use less CPU cycles than those with LSI Logic SAS virtual SCSI controllers.

For more details, refer to the paper Achieving a Million I/O Operations per Second from a Single VMware vSphere 5 Host.

from VMware's performance team

RAID Groups

Storage Pools

VMmark Testing

Array Configuration

RAID Group Configuration

Storage Pool Configuration

Storage Layout

VMmark Results

I/O Analyzer with Iometer Testing

Configuration

Iometer Configuration

Iometer results