Posts Tagged ‘price-performance’

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First 12-core VMmark for Istanbul Appears

June 10, 2009

VMware has posted the VMmark score for the first Istanbul-based system and it’s from HP: the ProLiant DL385 G6. While it’s not at the top of the VMmark chart at 15.54@11 tiles (technically it is at the top of the 12-core benchmark list), it still shows a compelling price-performance picture.

Comparing Istanbul’s VMmark Scores

For comparison’s sake, we’ve chosen the HP DL385 G5 and HP DL380 G6 as they were configured for their VMmark tests. In the case of the ProLiant DL380 G6, we could only configure the X5560 and not the X5570 as tested so the price is actually LOWER on the DL380 G6 than the “as tested” configuration. Likewise, we chose the PC-6400 (DDR2/667, 8x8GB) memory for the DL 385 G5 versus the more expensive PC-5300 (533) memory as configured in 2008.

As configured for pricing, each system comes with processor, memory, 2-SATA drives and VMware Infrastructure Standard for 2-processors. Note that in testing, additional NIC’s, HBA, and storage are configured and such additions are not included herein. We have omitted these additional equipment features as they would be common to a deployment set and have no real influence on relative pricing.

Systems as Configured for Pricing Comparison

System Processor Speed Cores Threads Memory Speed Street
HP ProLiant DL385 G5 Opteron 2384 2.7 8 8 64 667 $10,877.00
HP ProLiant DL385 G6 Opteron 2435 2.6 12 12 64 667 $11,378.00
HP ProLiant DL380 G6 Xeon X5560* 2.93 8 16 96 1066 $30,741.00

Here’s some good news: 50% more cores for only 5% more (sound like an economic stimulus?) The comparison Nehalem-EP is nearly 3x the Istanbul system in price.

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The Cost of Benchmarks

May 8, 2009

We’ve been challenged to backup our comparison of Nehalem-EP systems to Opteron Shanghai in price performance based on prevailing VMmark scores available on VMware’s site. In earlier posts, our analysis predicted “comparable” price-performance results between Shanghai and Nehalem-EP systems based on the economics of today’s memory and processors availability:

So what we’ve done here is taken the on-line configurations of some of the benchmark competitors. To make things very simple, we’ve just configured memory and CPU as tested – no HBA or 10GE cards to skew the results. The only exception – as pointed out by our challenger – is that we’ve taken the option of using “street price” memory where “street price” is better than the server manufacturer’s memory price.

Here’s our line-up:

System Processor Qty. Speed (GHz) Speed (GHz, Opt) Memory Configuration Street Price
Inspur NF5280 X5570 2 2.93 3.2 96GB (12x8GB) DDR3 1066 $18,668.58
Dell PowerEdge R710 X5570 2 2.93 3.2 96GB (12x8GB) DDR3 1066 $16,893.00
IBM System x 3650M2 X5570 2 2.93 3.2 96GB (12x8GB) DDR3 1066 $21,546.00
Dell PowerEdge M610 X5570 2 2.93 3.2 96GB (12x8GB) DDR3 1066 $21,561.00
HP ProLiant DL370 G6 W5580 2 3.2 3.2 96GB (12x8GB) DDR3 1066 $18,636.00
Dell PowerEdge R710 X5570 2 2.93 3.2 96GB (12x8GB) DDR3 1066 $16,893.00
Dell PowerEdge R805 2384 2 2.7 2.7 64GB (8x8GB) DDR2 533 $6,955.00
Dell PowerEdge R905 8384 4 2.7 2.7 128GB (16x8GB) DDR2 667 $11,385.00

Here we see Dell offering very aggressive DDR3/1066 pricing [for the R710] allowing us to go with on-line configurations, and HP offering overly expensive DDR2/667 memory prices (factor of 2) forcing us to go with 3rd party memory. In fact, IBM did not allow us to configure their memory configuration – as tested [with the 3650M2] – with their on-line configuration tool [neither did Dell with the M610] so we had to apply street memory prices. [Note: the So here’s how they rank with respect to VMmark:

System VMware Version Vmmark Score Vmmark Tiles Score/Tile Cost/Tile
Inspur NF5280 ESX Server 4.0 build 148592 23.45 17 1.38 $1,098.15
Dell PowerEdge R710 ESX Server 4.0 build 150817 23.55 16 1.47 $1,055.81
IBM System x 3650M2 ESX Server 4.0 build 148592 23.89 17 1.41 $1,267.41
Dell PowerEdge M610 ESX Server 4.0 23.9 17 1.41 $1,273.59
HP ProLiant DL370 G6 ESX Server 4.0 build 148783 23.96 16 1.50 $1,164.75
Dell PowerEdge R710 ESX Server 4.0 24 17 1.41 $993.71
Dell PowerEdge R805 ESX Server 3.5 U4 build 120079 11.22 8 1.40 $869.38
Dell PowerEdge R905 ESX Server 3.5 U3 build 120079 20.35 14 1.45 $813.21

As you can easily see, the cost-per-tile (analogous to $/VM) favors the Shanghai systems. In fact, the one system that we’ve taken criticism for including in our previous comparisons – the Supermicro 6026T-NTR+ with 72GB of DDR3/1066 (running at DDR3/800) – actually leads the pack in Nehalem-EP $/tile, but we’ve excluded it from our tables since it has been argued to be a “sub-optimal” configuration and out-lier. Again, the sweet spot for price-performance for Nehalem, Shanghai and Istanbul is in the 48GB to 80GB range with inexpensive memory: simple economics.

Please note, that not one of the 2P VMmark scores listed on VMware’s official VMmark results tally carry the Opteron 2393SE version of the processor (3.1GHz) or HT3-enabled motherboards. It is likely that we’ll not see HT3-enabled scores nor 2P ESX 4.0 scores until Istanbul’s release in the coming month. Again, if Shanghai’s $/tile is competitive with Nehalem’s today (again, in the 48GB to 80GB configurations), Istanbul – with the same memory and system costs – will be even more so.

Update: AMD’s Margaret Lewis has a similar take with comparison prices for AMD using DDR2/533 configurations. Her numbers – like our previous posts – resolve to $/VM, however she provides some good “street prices” for more “mainstream” configurations of Intel Nehalem-EP and AMD Shanghai systems. See her results and conclusions on AMD’s blog.

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Shanghai Economics 101 – Conclusion

May 6, 2009

In the past entries, we’ve looked only at the high-end processors as applied to system prices, and we’ll continue to use those as references through the end of this one. We’ll take a look at other price/performance tiers in a later blog, but we want to finish-up on the same footing as we began; again, with an eye to how these systems play in a virtualization environment.

We decided to finish this series with an analysis of  real world application instead of just theory. We keep seeing 8-to-1, 16-to-1 and 20-to-1 consolidation ratios (VM-to-host) being offered as “real world” in today’s environment so we wanted to analyze what that meant from an economic side.

The Fallacy of Consolidation Ratios

First, consolidation ratios that speak in terms of VM-to-host are not very informative. For instance, a 16-to-1 consolidation ratio sounds good until you realize it was achieved on an $16,000 4Px4C platform. This ratio results in a $1,000-per-VM cost to the consolidator.

In contrast, let’s take the same 16-to-1 ratio on a $6,000 2Px4C platform and it results in a $375-per-VM cost to the consolidator: a savings of nearly 60%. The key to the savings is in vCPU-to-Core consolidation ratio (provided sufficient memory exists to support it). In the first example that ratio was 1:1, but in the last example the ratio is 2:1. Can we find 16:1 vCPU-to-Core ratios out there? Sure, in test labs, but in the enterprise we think the valid range of vCPU-to-Core consolidation ratios is much more conservative, ranging from 1:1 to 8:1 with the average (or sweet spot) falling somewhere between 3:1 and 4:1.

Second, we must note that memory is a growing aspect of the virtualization equation. Modern operating systems no longer “sip” memory and 512MB for a Windows or Linux VM is becoming more an exception than a rule. That puts pressure on both CPU and memory capacity as driving forces for consolidation costs. As operating system “bloat” increases, administrative pressure to satisfy their needs will mount, pushing the “provisioned” amount of memory per VM ever higher.

Until “hot add” memory is part of DRS planning and the requisite operating systems support it, system admins will be forced to either over commit memory, purchase memory based on peak needs or purchase memory based on average memory needs and trust DRS systems to handle the balancing act. In any case, memory is a growing factor in systems consolidation and virtualization.

Modeling the Future

Using data from the Univerity of Chicago and as a baseline and extrapolating forward through 2010, we’ve developed a simple model to predict vMEM and vCPU allocation trends. This approach establishes three key metrics (already used in previous entries) that determine/predict system capacity: Average Memory/VM (vMVa), Average vCPU/VM (vCVa) and Average vCPU/Core (vCCa).

Average Memory per VM (vMVa)

Average memory per VM is determined by taking the allocated memory of all VM’s in a virtualized system – across all hosts – and dividing that by the total number of VM’s in the system (not including non-active templates.) This number is assumed to grow as virtualization moves from consolidation to standardized deployment. Read the rest of this entry ?