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Power Management and Performance in VMware vSphere 5.1 and 5.5

Power consumption is an important part of the datacenter cost strategy. Physical servers frequently offer a power management scheme that puts processors into low power states when not fully utilized, and VMware vSphere also offers power management techniques. A recent technical white paper describes the testing and results of two performance studies: The first shows how power management in VMware vSphere 5.5 in balanced mode (the default) performs 18% better than the physical host’s balanced mode power management setting. The second study compares vSphere 5.1 performance and power savings in two server models that have different generations of processors. Results show the newer servers have 120% greater performance and 24% improved energy efficiency over the previous generation.

For more information, please read the paper: Power Management and Performance in VMware vSphere 5.1 and 5.5.

Line-Rate Performance with 80GbE and vSphere 5.5

With the increasing number of physical cores in a system, the networking bandwidth requirement per server has also increased. We often find many networking-intensive applications are now being placed on a single server, which results in a single vSphere server requiring more than one 10 Gigabit Ethernet (GbE) adapter. Additional network interface cards (NICs) are also deployed to separate management traffic and the actual virtual machine traffic. It is important for these servers to service the connected NICs well and to drive line rate on all the physical adapters simultaneously.

vSphere 5.5 supports eight 10GbE NICs on a single host, and we demonstrate that a host running with vSphere 5.5 can not only drive line rate on all the physical NICs connected to the system, but can do it with a modest increase in overall CPU cost as we add more NICs.

We configured a single host with four dual-port Intel 10GbE adapters for the experiment and connected them back-to-back with an IXIA Application Network Processor Server with eight 10GbE ports to generate traffic. We then measured the send/receive throughput and the corresponding CPU usage of the vSphere host as we increased the number of NICs under test on the system.

Environment Configuration

  • System Under Test: Dell PowerEdge R820
  • CPUs: 4 x  Intel Xeon Processors E5-4650 @ 2.70GHz
  • Memory: 128GB
  • NICs:8 x Intel 82599EB 10GbE, SFP+ Network Connection
  • Client: Ixia Xcellon-Ultra XT80-V2, 2U Application Network Processor Server

Challenges in Getting 80Gbps Throughput

To drive near 80 gigabits of data per second from a single vSphere host, we used a server that has not only the required CPU and memory resources, but also the PCI bandwidth that can perform the necessary I/O operations. We used a Dell PowerEdge Server with an Intel E5-4650 processor because it belongs to the first generation of Intel processors that supports PCI Gen 3.0. PCI Gen 3.0 doubles the PCI bandwidth capabilities compared to PCI Gen 2.0. Each dual-port Intel 10GbE adapter needs at least a PCI Gen 2.0 x8 to reach line rate. Also, the processor has Intel Data Direct I/O Technology where the packets are placed directly in the processor cache rather than going to the memory. This reduces the memory bandwidth consumption and also helps reduce latency.

Experiment Overview

Each 10GbE port of the vSphere 5.5 server was configured with a separate vSwitch, and each vSwitch had two Red Hat 6.0 Linux virtual machines running an instance of Apache web server. The web server virtual machines were configured with 1 vCPU and 2GB of memory with VMXNET3 as the virtual NIC adapter.  The 10GbE ports were then connected to the Ixia Application Server port. Since the server had two x16 slots and five x8 slots, we used the x8 slots for the four 10GbE NICs so that each physical NIC had identical resources. For each physical connection, we then configured 200 web/HTTP connections, 100 for each web server, on an Ixia server that requested or posted the file. We used a high number of connections so that we had enough networking traffic to keep the physical NIC at 100% utilization.

Figure 1. System design of NICs, switches, and VMs

The Ixia Xcellon application server used an HTTP GET request to generate a send workload for the vSphere host. Each connection requested a 1MB file from the HTTP web server.

Figure 2 shows that we could consistently get the available[1] line rate for each physical NIC as we added more NICs to the test. Each physical NIC was transmitting 120K packets per second and the average TSO packet size was close to 10K. The NIC was also receiving 400K packets per second for acknowledgements on the receive side. The total number of packets processed per second was close to 500K for each physical connection.

Figure 2. vSphere 5.5 drives throughput at available line rates. TSO on the NIC resulted in lower packets per second for send.

Similar to the send case, we configured the application server to post a 1MB file using an HTTP POST request for generating receive traffic for the vSphere host. We used the same number of connections and observed similar behavior for the throughput. Since the NIC does not have support for hardware LRO, we were getting 800K packets per second for each NIC. With eight 10GbE NICs, the packet rate reached close to 6.4 million packets per second. VMware does Software LRO for Linux and as a result we see large packets in the guest. The guest packet rate is around 240K packets per second. There was also significant traffic for TCP acknowledgements and for each physical NIC. The host was transmitting close to 120K acknowledgement packets for each physical NIC, bringing the total packets processed close to 7.5 million packets per second for eight 10Gb ports.

Figure 3. Average vSphere 5.5 host CPU utilization for send and receive

We also measured the average CPU reported for each of the tests. Figure 3 shows that the vSphere host’s CPU usage increased linearly as we added more physical NICs to the test for both send and receive. This indicates that performance improves at an expected and acceptable rate.

Test results show that vSphere 5.5 is an excellent platform on which to deploy networking-intensive workloads. vSphere 5.5 makes use of all the physical bandwidth capacity available and does this without incurring additional CPU cost.

 


[1]A 10GbE NIC can achieve only 9.4 Gbps of throughput with standard MTU. For a 1500 byte packet, we have 40 bytes for the TCP /IP header and 38 bytes for the Ethernet frame format.

Deploying Extremely Latency-Sensitive Applications in VMware vSphere 5.5

VMware vSphere ensures that virtualization overhead is minimized so that it is not noticeable for a wide range of applications including most business critical applications such as database systems, Web applications, and messaging systems. vSphere also supports well applications with millisecond-level latency constraints, including VoIP services. However, performance demands of latency-sensitive applications with very low latency requirements such as distributed in-memory data management, stock trading, and high-performance computing have long been thought to be incompatible with virtualization.

vSphere 5.5 includes a new feature for setting latency sensitivity in order to support virtual machines with strict latency requirements. This per-VM feature allows virtual machines to exclusively own physical cores, thus avoiding overhead related to CPU scheduling and contention. A recent performance study shows that using this feature combined with pass-through mechanisms such as SR-IOV and DirectPath I/O helps to achieve near-native performance in terms of both response time and jitter.

The paper explains major sources of latency increase due to virtualization in vSphere and presents details of how the latency-sensitivity feature improves performance along with evaluation results of the feature. It also presents some best practices that were concluded from the performance evaluation.

For more information, please read the full paper: Deploying Extremely Latency-Sensitive Applications in VMware vSphere 5.5.

 

Exploring Generational Differences in Performance and Energy Efficiency Using VMware VMmark 2.5

Each new generation of servers brings advances in hardware components. For IT professionals purchasing or managing new generations of hardware, it’s vital to understand how these incremental hardware improvements translate into real-world gains in the datacenter. Using the VMware VMmark 2.5 virtualization benchmark, we compared performance and energy efficiency of two different generations of servers in four-node clusters.

VMmark 2.5 is a multi-host virtualization benchmark that uses varied application workloads as well as common datacenter operations to model the demands of the datacenter. VMs running diverse application workloads are grouped into units of load called tiles. For more details, see the VMmark 2.5 overview.

Testing Methodology
All tests were conducted on two four-node clusters running VMware vSphere 5.1. We compared performance and energy efficiency between a cluster of previous generation Dell R310 servers, and a cluster of current generation Dell R620 servers. For simplicity, we refer to these as the ‘old cluster’ and ‘new cluster,’ respectively. Among other hardware differences, the old cluster servers contained four-core Intel Nehalem processors while the new cluster servers contained eight-core Intel Sandy Bridge EP processors. Memory in the newer servers was appropriately scaled up to accommodate their increased processing power and represents common current server configurations. Software and storage configurations were identical between clusters.

Configuration
Old Cluster
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): 32GB DDR3 ECC @ 800 MHz

New Cluster
Systems Under Test: Four Dell PowerEdge R620 servers
CPUs (per server): One Eight-Core Intel® Xeon® E5-2665 @ 2.4 GHz, Hyper-Threading enabled
Memory (per server): 96GB DDR3 ECC @ 1067 MHz

Storage Array: EMC VNX5700
        62 Enterprise Flash Drives (SSDs), RAID 0, grouped as 3 x 8 SSD LUNs, 7 x 5 SSD LUNs, and 1 x 3 SSD LUN
Hypervisor: VMware vSphere 5.1.0
Virtualization Management: VMware vCenter Server 5.1.0
VMmark version: 2.5

Results
To determine the maximum VMmark load the old cluster could support, we increased the number of VMmark tiles until the cluster reached saturation, which is defined as the largest number of tiles that still meet Quality of Service (QoS) requirements. We then tested the new cluster at the same number of tiles. All data points are the mean of four tests in each configuration and VMmark scores are normalized to the old cluster’s performance.

The new cluster had a 32% higher VMmark score in combination with a 41% lower CPU utilization. The new cluster also showed a 24% increase in energy efficiency over the old cluster, which we’ll discuss further below. At four tiles, the old cluster was bottlenecked on CPU, resulting in decreased workload throughput, while the new cluster was not. With CPU resources to spare, the new cluster met the requested load at lower latencies, which increased its total throughput and score. Mean I/O latencies remained low for both clusters at 1.2ms reads and 1.1ms writes for the old cluster and 1.0ms reads and 0.9ms writes for the new cluster.

We next determined the maximum VMmark load the new cluster could support. While the old cluster was saturated at four tiles, the new cluster accommodated more than twice the load at nine tiles and produced a score 120% higher than the old cluster. Mean I/O latencies remained low at 1.0ms.

Click to enlarge

The performance advantages of the R620 over the R310 were largely due to the generational improvements of the R620’s eight-core E5-2665 processor versus the R310’s four-core x3460 processor, which includes improved bus speeds and larger L3 cache, and the R620’s increased memory.

These performance results suggest that it would be possible to replace four Dell R310 servers with two Dell R620 servers and expect better than equivalent performance. We put this to the test by removing two nodes from the new cluster and found that the two remaining nodes did support four tiles at 93% utilization, with an 11% higher VMmark score and 74% greater energy efficiency than the four-host old cluster.

Beyond their raw performance capability, we also compared the two server generations on their energy efficiency. The Performance per Kilowatt metric, which is new to VMmark 2.5, models energy efficiency as VMmark score per kilowatt of power consumed. Below, we’ve plotted energy efficiency against the normalized VMmark score. Both clusters were run with their servers’ power management set to “maximum performance.”

Energy Efficiency as a Function of VMmark 2.5 Score

Two trends emerge from this figure. First, at four tiles, the four-host new cluster accomplishes more work at higher energy efficiency than the old cluster. Across the board, the new cluster is more energy efficient than the old cluster. Second, within the four-host new cluster, greater energy efficiency is correlated with increase in VMmark score. As the CPUs become busier, performance increases at a faster rate than the required power. This can be understood by noting that an idle server will still consume power, but with no performance to show for it. A highly utilized server is typically the most energy efficient per request completed, which is confirmed by the two-host new cluster that achieved high efficiency at 93% utilization. Higher energy efficiency creates cost savings in energy consumption and in cooling costs.

Our investigation shows that, while running vSphere 5.1, two newer Dell R620 servers are capable of supporting a greater load than four older Dell R310 servers. Because the Dell R620 performance is more than double that of the Dell R310, a four-node Dell R620 cluster reached a 120% higher maximum score than the Dell R310 cluster. In addition to its performance advantages, at each load level the Dell R620 cluster performed with greater energy efficiency, showing that the Dell R620 has superior performance but also has greater energy efficiency than the Dell R310.

Impact of Enhanced vMotion Compatibility on Application Performance

Enhanced vMotion Compatibility (EVC) is a technique that allows vMotion to proceed even when ESXi hosts with CPUs of different technologies exist in the vMotion destination cluster. EVC assigns a baseline to all ESXi hosts in the destination cluster so that all of them will be compatible for vMotion. An example is assigning a Nehalem baseline to a cluster mixed with ESXi hosts with Westmere, Nehalem processors. In this case, the features available in Westmere would be hidden, because it is a newer processor than Nehalem. But all ESXi hosts would “broadcast” that they have Nehalem features.

Tests showed how utilizing EVC with different applications affected their performance. Several workloads were chosen to represent typical applications running in enterprise datacenters. The applications represented included database, Java, encryption, and multimedia. To see the results and learn some best practices for performance with EVC, read Impact of Enhanced vMotion Compatibility on Application Performance.

1millionIOPS On 1VM

Last year at VMworld 2011 we presented one million I/O operations per second (IOPS) on a single vSphere 5 host (link).  The intent was to demonstrate vSphere 5′s performance by using mutilple VMs to drive an aggregate load of one million IOPS through a single server.   There has recently been some interest in driving similar I/O load through a single VM.  We used a pair of Violin Memory 6616 flash memory arrays, which we connected to a two-socket HP DL380 server, for some quick experiments prior to VMworld.  vSphere 5.1 was able to demonstrate high performance and I/O efficiency by exceeding one million IOPS, doing so with only a modest eight-way VM.  A brief description of our configuration and results is given below.

Configuration:
Hypervisor: vSphere 5.1
Server: HP DL380 Gen8
CPU: 2 x Intel Xeon E5-2690, HyperThreading disabled
Memory: 256GB
HBAs: 5 x QLE2562
Storage: 2 x Violin Memory 6616 Flash Memory Arrays
VM: Windows Server 2008 R2, 8 vCPUs and 48GB.
Iometer Config: 4K IO size w/ 16 workers

Results:
Using the above configuration we achieved 1055896 total sustained IOPS.  Check out the following short video clip from one of our latest runs.

Look out for a more thorough write-up after VMworld.

 

Exploring FAST Cache Performance Using VMmark 2.1.1

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.

VMmark 2.1.1 Scaling with FAST Cache

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.

Total Cache Hits at 4 TilesRead and Write Cache Hits at 4 tiles
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.

I/O Latency at 4 Tiles

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.

Comparing ESXi 4.1 and ESXi 5.0 Scaling Performance

In previous articles on VROOM! we used VMmark 2 to investigate the effects of altering a single hardware component, such as a storage array or server model, in a vSphere cluster. In contrast to these earlier studies, we now examine the effects of upgrading the hosts’ software from ESXi 4.1 to ESXi 5.0 on the performance of a VMmark 2 cluster.

vSphere 5 includes many new features and virtual machine enhancements, the details of which can be found here. To the IT professional weighing the costs and benefits of upgrading their existing infrastructure to vSphere 5, an often important question is whether ESXi 5.0 can outperform ESXi 4.1 in the same environment. VMmark 2 is an ideal tool for answering this question with measurable results. We used VMmark 2.1.1 to see how ESXi 5.0 stacked up to ESXi 4.1 on an identically configured cluster.

VMmark 2 is a multi-host virtualization benchmark that models application performance as well as the effects of common infrastructure operations such as vMotion, Storage vMotion, and virtual machine deployments. Each VMmark tile contains a set of VMs running diverse application workloads as a unit of load. VMmark 2 scores are computed as a weighted average of application workload throughput and infrastructure operation throughput. For more details, see the overview, release notes for VMmark 2.1, and for 2.1.1.

Testing Methodology

All VMmark 2 tests were conducted on a cluster of four identically configured entry-level Dell Power Edge R310 servers. To determine the impact of the vSphere 5 environment on performance, a series of tests was conducted with these hosts running ESXi 4.1, then with ESXi 5.0. In addition, for the vSphere 5 environment, the virtual machine hardware and VMware Tools were upgraded on all workload VMs, and LUNs were reformatted as VMFS5. All other components in the environment remained unchanged during testing.

Configuration
Systems Under Test: Four Dell PowerEdge R310 Servers
CPUs: One Quad-Core Intel® Xeon® X3460 @ 2.8 GHz, hyper-threading enabled per server
Memory: 32GB DDR3 ECC @ 800 MHz per server
Storage Array: EMC VNX5500
Hypervisors under test:
VMware ESXi 4.1
VMware ESXi 5.0
Virtualization Management: VMware vCenter Server 5.0
VMmark version: 2.1.1

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 the ESXi 4.1 and ESXi 5.0 configurations at each level of load.

The graph below shows the results of the VMmark 2 testing as described above with identically configured clusters running ESXi 4.1 and ESXi 5.0. All data points are the mean of three tests in each configuration.

  Scaling

 

The ESXi 4.1 cluster reached saturation at 3 tiles, but ESXi 5.0 was able to support 4 tiles while still meeting workload Quality of Service requirements. The ESXi 5.0 cluster also outperformed ESXi 4.1 by 3% and 4% on the two and three-tile runs, respectively. Differences in CPU utilization were negligible. The results show that, in an equivalent environment, vSphere 5 handled greater load than ESXi 4.1 before reaching saturation, and showed increased performance at lower levels of load as well. At saturation, vSphere 5 showed a 22% increase in overall VMmark 2 scores over ESXi 4.1. In this cluster, vSphere 5 supported 33% more VMs and twice the number of infrastructure operations while meeting Quality of Service requirements.

VMmark 2 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. The completion time for vMotion, Storage vMotion, and VM Deploy is used as the latency measurement for the infrastructure operations. Latency can be very informative about the functioning of the environment and how the cluster as a whole performs under increasing loads. Examining latency at a 3-tile load, as seen in the figure below, reveals significant differences between the hypervisor versions. Latencies were normalized to the ESXi 4.1 results.

Latency

We saw decreases in latency for all VMmark 2 workloads with vSphere 5. The latency decreases were most striking in Olio, Storage vMotion, and DVD Store 2, with decreases of 20%, 19%, and 15%, respectively. These improvements to vMotion and Storage vMotion are consistent with publicized improvements in vMotion and Storage vMotion latency for vSphere 5 (details here).

A VMmark 2 run passes when all of its application QoS metrics, or latencies, remain below a specified threshold. These decreases in latency with ESXi 5.0 are directly related to why ESXi 5.0 was able to support an additional tile relative to ESXi 4.1.

Our comparison has shown that upgrading an ESXi 4.1 cluster to vSphere 5 had two high-level effects on performance. The vSphere 5 cluster supported 33% more VMs at saturation than the ESXi 4.1 cluster, and it also exhibited improved latency and throughput at lower levels of load, showing that ESXi 5.0 does outperform ESXi 4.1.

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

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.

Performance Implications of Storage I/O Control-Enabled NFS Datastores

Storage I/O Control (SIOC) allows administrators to control the amount of access virtual machines have to the I/O queues on a shared datastore. With this feature, administrators can ensure that a virtual machine running a business-critical application has a higher priority to access the I/O queue than that of other virtual machines sharing the same datastore. In vSphere 4.1, SIOC was supported on VMFS-based datastores that used SAN with iSCSI and Fibre Channel. In vSphere 5, SIOC support has been extended to NFS-based datastores.

Recent tests conducted at VMware Performance Engineering lab studied the following aspects of SIOC:

  • The performance impact of SIOC: A fine-grained access management of the I/O queues resulted in a 10% improvement in the response time of the workload used for the tests.
  • SIOC’s ability to isolate the performance of applications with a smaller request size: Some applications like Web and media servers use I/O patterns with a large request size (for example, 32K). But some other applications like OLTP databases request smaller I/Os ≤8K. Test findings show that SIOC helped an OLTP database workload to achieve higher performance when sharing the underlying datastore with a workload that used large-sized I/O requests.
  • The intelligent prioritization of I/O resources: SIOC monitors virtual machines’ usage of the I/O queue at the host and dynamically redistributes any unutilized queue slots to those virtual machines that need them. Tests show that this process happens consistently and reliably.

For the full paper, see Performance Implications of Storage I/O Control–Enabled NFS Datastores in VMware vSphere 5