Benchmarking Your Cloud

By John E. West

July 16, 2009

It was inevitable that with all the hype and marketing dollars directed at cloud computing these days that someone would eventually start trying to use them for real work. Of course, this puts a nasty wrinkle into marketing plans because once people starting using them for real work, then there are actual performance results. The results themselves aren’t too troubling because they are usually point cases, and negative messages are easily explained away by calling on the vagaries of a particular software stack and the giving away of snazzy memory sticks. But then the results lead the engineering-minded to wonder whether all of the available cloud computing alternatives behave in the same way, and if not which of the them might be best suited for a particular task. This leads to standardized testing and then, before you know it, we have full-fledged benchmarking on our hands.

Not great for marketing departments, but wonderful for customers. And the good news for customers — and potential customers — of cloud computing is that the community is starting to think seriously about benchmarking the performance of clouds.

There is a long history in benchmarking computer hardware or software components tests that isolate, as much as possible, a single feature of the system under test to facilitate comparisons. In order to make sure that the comparisons are valid, benchmarks like those from the TPC require testing be done in a managed environment with a fixed configuration that can be completely described and replicated for future testing. Further, since the TPC benchmarks focus on transactional database systems they also require adherence to the ACID properties, notably (as we’ll see in a moment) the coherence property.

But, when you think about it, this benchmarking model isn’t really a good match for clouds, where the service model is designed to be dynamic, distributed and robust. One of the key selling points for cloud infrastructure is that it can grow and shrink with a particular user’s demand, and workload can be shifted to wherever it is most advantageously served. In this environment the hardware may change over time, as may parts of the system software stack. Furthermore creating a reliable distributed processing environment usually means replicating parts of the data, and making these data available in the presence of communications failures means relaxing some of the traditional guarantees on data consistency (Amazon’s cloud storage offering only guarantees eventual consistency, for example).

So, while traditional approaches to benchmarking, and traditional benchmarks for that matter, will provide some useful information about the performance of clouds, the traditional testing philosophy behind most benchmarks today doesn’t lend itself to creating a test of merit that enables comparison of two clouds with one another in a way that takes into account the very features that make them interesting technology solutions for certain classes of problems in the first place.

The general topic is dealt with ably in an interesting paper [PDF] from DBTest ’09. In that paper the authors outline what they’re looking for in a cloud benchmark: something that doesn’t require a static system configuration, reflects the ability of the cloud to adapt to changing load, assesses robustness to failures of various components, and includes the full cloud software stack rather than just one component.

That’s a pretty tall order, and amounts to something akin to not just being able to demonstrate that your 1996 Porsche 993 isn’t just faster than a Corvette, but that it’s cooler. Speed you can measure; “cool” is sufficiently general that it’s pretty hard to quantify. Still, you have to have a goal, and working on the problem is certainly worthwhile (not least because then my friends Steve and John could avoid a lot of pointless bar fights). The authors do manage a pretty reasonable suggestion for a benchmark in the paper, which I commend to your summer beach reading lists.

There are some cloud benchmarking efforts already well past the paper stage. Cloudstone, for example, is a benchmark out of UC Berkeley designed to measure the performance of clouds designed to run Web 2.0 applications. And there is also MalStone, a benchmark of more direct interest to the HPC crowd since it is designed specifically to allow the comparison of clouds designed for data intensive computing.

As described by Robert Grossman, the director of the National Center for Data Mining at the University of Illinois at Chicago and chair of the Open Cloud Consortium, MalStone is a “stylized analytic computation of a type that is common in data intensive computing.” The MalStone computation starts with a very large set of distributed files that document the date and time that users visited Web pages (including a user id), and also specify whether those users’ computers later become compromised by malware. The computation then goes through the files trying to identify Web pages that are possible sources of contamination by cross-referencing the browser history for each user id with records of whether the user’s machine is compromised. Web sites that figure prominently in the average browsing history of a cohort of machines that were subsequently compromised are suspect.

As Grossman points out, the task itself need not be a good way of finding Web sites hosting malware. It only needs to be a task sufficient to measure the performance of clouds for data intensive tasks:

We call MalStone stylized since we do not argue that this is a useful or effective algorithm for finding compromised sites. Rather, we point out that if the log data is so large that it requires large numbers of disks to manage it, then computing something as simple as this ratio can be computationally challenging. For example, if the data spans 100 disks, then the computation cannot be done easily with any of the databases that are common today. On the other hand, if the data fits into a database, then this statistic can be computed easily using a few lines of SQL.

There are two benchmarks, MalStone A and MalStone B. MalStone A computes a global figure for each Web site for all times included in the logs; MalStone B computes the figures by Web site by week. The datasets involved are quite large, with up to 100 TB of data.

MalStone A-10 uses 10 billion records so that in total there is 1 TB of data. Similarly, MalStone A-100 requires 100 billion records and MalStone A-1000 requires 1 trillion records. MalStone B-10, B-100 and B-1000 are defined in the same way.

You can read more about the benchmarks and get the actual source code for them at code.google.com/p/malgen/.

Earlier this summer Grossman and his team at the Open Cloud Consortium (OCC) announced results comparing Hadoop (the environment used by Facebook, Yahoo, and others) with the open-source cloud architecture Sector. Grossman describes Sector in a blog post as “an open source cloud written in C++ for storing, sharing and processing large data sets.” The OCC uses 10 GbE circuits on the National Lambda Rail (NLR) as the backbone for its testbed, and runs its tests over the NLR between San Diego, Los Angeles, Chicago and Washington, DC.

The preliminary results are interesting. They show significant differences between Hadoop and Sector, but also differences between Hadoop with Hadoop’s implementation of MapReduce, and Hadoop using Streams and coding MalStone in Python. The most significant differences are for MalStone B, where performance ranges from 841 minutes with Hadoop/MapReduce to 44 minutes with Sector. Even the Hadoop/Streams implementation, which is considerably faster than the MapReduce, comes in at nearly 143 minutes. The range there is 14 hours to 44 minutes, worst-case to best.

These results highlight the importance of making sure your cloud is designed to solve the problem at hand. And as MalStone and other cloud benchmarking efforts continue to evolve users will have even more robust tools to make informed decisions.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Trinity Supercomputer’s Haswell and KNL Partitions Are Merged

July 19, 2017

Trinity supercomputer’s two partitions – one based on Intel Xeon Haswell processors and the other on Xeon Phi Knights Landing – have been fully integrated are now available for use on classified work in the Nationa Read more…

By HPCwire Staff

Fujitsu Continues HPC, AI Push

July 19, 2017

Summer is well under way, but the so-called summertime slowdown, linked with hot temperatures and longer vacations, does not seem to have impacted Fujitsu's output. The Japanese multinational has made a raft of HPC and A Read more…

By Tiffany Trader

Researchers Use DNA to Store and Retrieve Digital Movie

July 18, 2017

From abacus to pencil and paper to semiconductor chips, the technology of computing has always been an ever-changing target. The human brain is probably the computer we use most (hopefully) and understand least. This mon Read more…

By John Russell

The Exascale FY18 Budget – The Next Step

July 17, 2017

On July 12, 2017, the U.S. federal budget for its Exascale Computing Initiative (ECI) took its next step forward. On that day, the full Appropriations Committee of the House of Representatives voted to accept the recomme Read more…

By Alex R. Larzelere

HPE Extreme Performance Solutions

HPE Servers Deliver High Performance Remote Visualization

Whether generating seismic simulations, locating new productive oil reservoirs, or constructing complex models of the earth’s subsurface, energy, oil, and gas (EO&G) is a highly data-driven industry. Read more…

Summer Reading: IEEE Spectrum’s Chip Hall of Fame

July 17, 2017

Take a trip down memory lane – the Mostek MK4096 4-kilobit DRAM, for instance. Perhaps processors are more to your liking. Remember the Sh-Boom processor (1988), created by Russell Fish and Chuck Moore, and named after Read more…

By John Russell

Women in HPC Luncheon Shines Light on Female-Friendly Hiring Practices

July 13, 2017

The second annual Women in HPC luncheon was held on June 20, 2017, during the International Supercomputing Conference in Frankfurt, Germany. The luncheon provides participants the opportunity to network with industry lea Read more…

By Tiffany Trader

Satellite Advances, NSF Computation Power Rapid Mapping of Earth’s Surface

July 13, 2017

New satellite technologies have completely changed the game in mapping and geographical data gathering, reducing costs and placing a new emphasis on time series and timeliness in general, according to Paul Morin, directo Read more…

By Ken Chiacchia and Tiffany Jolley

Intel Skylake: Xeon Goes from Chip to Platform

July 13, 2017

With yesterday’s New York unveiling of the new “Skylake” Xeon Scalable processors, Intel made multiple runs at multiple competitive threats and strategic markets. Skylake will carry Intel's flag in the fight for le Read more…

By Doug Black

Fujitsu Continues HPC, AI Push

July 19, 2017

Summer is well under way, but the so-called summertime slowdown, linked with hot temperatures and longer vacations, does not seem to have impacted Fujitsu's out Read more…

By Tiffany Trader

Researchers Use DNA to Store and Retrieve Digital Movie

July 18, 2017

From abacus to pencil and paper to semiconductor chips, the technology of computing has always been an ever-changing target. The human brain is probably the com Read more…

By John Russell

The Exascale FY18 Budget – The Next Step

July 17, 2017

On July 12, 2017, the U.S. federal budget for its Exascale Computing Initiative (ECI) took its next step forward. On that day, the full Appropriations Committee Read more…

By Alex R. Larzelere

Women in HPC Luncheon Shines Light on Female-Friendly Hiring Practices

July 13, 2017

The second annual Women in HPC luncheon was held on June 20, 2017, during the International Supercomputing Conference in Frankfurt, Germany. The luncheon provid Read more…

By Tiffany Trader

Satellite Advances, NSF Computation Power Rapid Mapping of Earth’s Surface

July 13, 2017

New satellite technologies have completely changed the game in mapping and geographical data gathering, reducing costs and placing a new emphasis on time series Read more…

By Ken Chiacchia and Tiffany Jolley

Intel Skylake: Xeon Goes from Chip to Platform

July 13, 2017

With yesterday’s New York unveiling of the new “Skylake” Xeon Scalable processors, Intel made multiple runs at multiple competitive threats and strategic Read more…

By Doug Black

Perverse Incentives? How Economics (Mis-)shaped Academic Science

July 12, 2017

The unintended consequences of how we fund academic research—in the U.S. and elsewhere—are strangling innovation, putting universities into debt and creatin Read more…

By Ken Chiacchia, Senior Science Writer, Pittsburgh Supercomputing Center

Why Tech is Failing at Diversity and How It Can Succeed

July 11, 2017

The sectors that are supposed to be all about innovation and the future continue to fail spectacularly at gender equity and diversity. UK, US and Canada still haven’t managed to break the average 20 percent threshold for gender equity across STEM academic disciplines. Read more…

By Kelly Nolan

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

Leading Solution Providers

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of Read more…

By Alex Woodie

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major Read more…

By Tiffany Trader

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Share This