Data Protection: One Size Does Not Fit All

By Paul Carpentier

April 10, 2013

In the course of IT history, many schemes have been devised and deployed to protect data against storage system failure, especially disk drive hardware. These protection mechanisms have nearly always been variants on two themes: duplication of files or objects (backup, archiving, synchronization, remote replication come to mind); or parity-based schemes at disk level (RAID) or at object level (erasure coding, often also referred to as Reed-Solomon coding). Regardless of implementation details, the latter always consists of the computation and storage of “parity” information over a number of data entities (whether disks, blocks or objects). Many different parity schemes exist, offering a wide range of protection trade-offs between capacity overhead and protection level – hence their interest.

Erasure coding:

As of late, erasure coding has received a lot of attention in the object storage field as a ‘one-size-fits-all’ approach to content protection. This is a stretch. Erasure coding is a solid approach to storage footprint reduction for an interesting but bounded field of use cases, involving BOTH large streams AND large clusters, but at the cost of sacrificing the numerous use cases that involve small streams, small clusters, or a combination of the two.

Most readers will be familiar with the concept of RAID content protection on hard disk drives. For example, the contents of a set of five drives is used to compute the contents of what is called a parity drive adding one more drive to the RAID set for a total of six drives. Of the total set of six, if any single drive fails, the content that is lost can be rebuilt from the five remaining drives. Aside such a 5+1 scheme, many others are possible, where even multiple drives can fail simultaneously and yet the full content can be rebuilt: there is a continuum in the trade-off between footprint and robustness.

More recently, the same class of algorithms that is used for RAID has been applied to the world of object storage: they are commonly called Erasure Codes. The concept is similar: imagine an object to be stored in a cluster. Now, rather than storing and replicating it wholly we will cut the incoming stream into (say) six segments in a 5:1 scheme each with parity information. Similar to the RAID mechanism above, any missing segment out of the six can be rebuilt from the five remaining ones, hence the 1. This provides a mechanism to survive a failed disk drive without making a full replica: the footprint overhead is just 20 percent here rather than 100 percent with comparable data durability.

Beyond this “5+1” scheme, many more Erasure Coding (EC) schemes are possible. They can survive as many disk failures as their number of parity segments: a 10+6 scheme can survive six simultaneous segment failures without data loss, for instance. Here the overhead will be 60 percent ((10+6)/10).

Erasure coding comes with trade-offs

The underlying objective is clear: provide protection against failure at lower footprint cost. However, as usual, there is no such thing as a ‘free lunch.’ There are trade-offs to be considered when compared to replication. The key is to have the freedom to choose the best protection for each particular use case.

When chopping up objects to store the resulting segments across a number of nodes, the “physical” object count of the underlying storage system is multiplied (e.g., for a 10:6 scheme, it’s multiplied by 16). Not all competing object storage systems handle high object count well. It is also clear that the granularity (i.e., minimum file size) of the underlying file system or object storage system will play a role in suggesting how small an object can be to be economically stored using erasure coding. It doesn’t really make sense from an efficiency perspective to store, say, a 50K object using a 10:6 erasure coding scheme if there is a file system at the core of a storage system. This is because file systems still segment files into blocks with minimum block sizes. A common threshold for this block size for a Linux file system is 32K so the resulting storage needed for a 50K file using a 10:6 erasure coding scheme would be would be 512K (32K * 16 segments) or a 10X increase in footprint. As we will see replication is a much better approach for small files.

Replication:

The simplest form of protective data redundancy is replication, with one or more additional copies of an “original” object being created and maintained to be available if that original somehow gets damaged or lost. In spite of the recent hype around erasure coding, we will see that there still are substantial use case areas where replication clearly is the superior option. For the sake of the example, imagine a cluster of a 100 CPUs with one disk drive each, and 50 million objects with 2 replicas each, 100 million objects grand total. When we speak of replicas in this context, we mean an instance – any instance – of an object; there is no notion of “original” or “copy.” Two replicas equal a grand total of two instances of a given object, somewhere in the cluster, on two randomly different nodes. When an object loss is detected a recovery cycle begins. Data loss only occurs if both replicas are lost which is why it is important to store replicas on different nodes and if possible different locations. It is also important to have efficient and rapid recovery cycles; you want to ensure that your objects are quickly replicated in case of an overlapping recovery cycle which may lead to data loss. If there are three replicas per object, three overlapping recovery cycles (a very low probability event) will be required to cause any data loss.

Replication and Erasure combined is the answer

As so often in IT, there is no single perfect solution to a wide array of use cases. In object storage applications, cluster sizes run the gamut between just a few nodes built into a medical imaging modality to thousands of nodes spanning multiple datacenters, with object sizes ranging between just a few kilobytes for an email message and hundreds of gigabytes for seismic activity data sets. If we want to fulfill the economic and manageability promises of the single unified storage, we need technology that is fully capable of seamlessly adapting between those use cases.

To deal with the velocity and variability of unstructured information, organizations are increasingly turning to cloud storage infrastructures to manage their data in a cost-effective, just-in-time manner, while others may need the robustness of big data repositories to handle the volume that today’s boundless storage requires. A combination of both replication and erasure coding, combined into a singular object storage solution, will provide the best option to access and analyze data regardless of object size, object count or storage amount while ensuring data integrity aligned with business value. Traditional file systems simply cannot provide the ease of management and accessibility required for cloud storage, nor will they provide the massive scalability and footprint efficiency required for big data repositories. The future of both cloud storage and big data remain firmly entrenched in an object storage solution that incorporates both replication and erasure coding into its architecture to overcome the limitations of either one technology.

To see an in depth paper on “Replication and Erasure Coding Explained,” visit http://www.caringo.com/.

About the Author

Known as the father of the Content Addressing concept, Paul Carpentier invented the patent pending scalable and upgradeable security that is at the heart of Caringo. He was the architect of SequeLink – the first client/server middleware product to connect heterogeneous front ends running over multiple networks to multiple databases on the server side. Paul founded Wave Research and conceived FileWave, the first fully automated, model-driven software distribution and management system. At FilePool, he invented the technology that created the Content Addressed Storage industry. FilePool, was sold to EMC who turned CAS into a multi-billion dollar marketplace. Caringo CAStor, based on two of Mr. Carpentier’s six patents promises to revolutionize the data storage business in much the same manner that CAS created a whole new marketplace.

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!

GDPR’s Impact on Scientific Research Uncertain

May 24, 2018

Amid the angst over preparations—or lack thereof—for new European Union data protections entering into force at week’s end is the equally worrisome issue of the rules’ impact on scientific research. Among the Read more…

By George Leopold

Intel Pledges First Commercial Nervana Product ‘Spring Crest’ in 2019

May 24, 2018

At its AI developer conference in San Francisco yesterday, Intel embraced a holistic approach to AI and showed off a broad AI portfolio that includes Xeon processors, Movidius technologies, FPGAs and Intel’s Nervana Neural Network Processors (NNPs), based on the technology it acquired in 2016. Read more…

By Tiffany Trader

Pattern Computer – Startup Claims Breakthrough in ‘Pattern Discovery’ Technology

May 23, 2018

If it weren’t for the heavy-hitter technology team behind start-up Pattern Computer, which emerged from stealth today in a live-streamed event from San Francisco, one would be tempted to dismiss its claims of inventing Read more…

By John Russell

HPE Extreme Performance Solutions

HPC and AI Convergence is Accelerating New Levels of Intelligence

Data analytics is the most valuable tool in the digital marketplace – so much so that organizations are employing high performance computing (HPC) capabilities to rapidly collect, share, and analyze endless streams of data. Read more…

IBM Accelerated Insights

Mastering the Big Data Challenge in Cognitive Healthcare

Patrick Chain, genomics researcher at Los Alamos National Laboratory, posed a question in a recent blog: What if a nurse could swipe a patient’s saliva and run a quick genetic test to determine if the patient’s sore throat was caused by a cold virus or a bacterial infection? Read more…

Silicon Startup Raises ‘Prodigy’ for Hyperscale/AI Workloads

May 23, 2018

There's another silicon startup coming onto the HPC/hyperscale scene with some intriguing and bold claims. Silicon Valley-based Tachyum Inc., which has been emerging from stealth over the last year and a half, is unveili Read more…

By Tiffany Trader

Intel Pledges First Commercial Nervana Product ‘Spring Crest’ in 2019

May 24, 2018

At its AI developer conference in San Francisco yesterday, Intel embraced a holistic approach to AI and showed off a broad AI portfolio that includes Xeon processors, Movidius technologies, FPGAs and Intel’s Nervana Neural Network Processors (NNPs), based on the technology it acquired in 2016. Read more…

By Tiffany Trader

Pattern Computer – Startup Claims Breakthrough in ‘Pattern Discovery’ Technology

May 23, 2018

If it weren’t for the heavy-hitter technology team behind start-up Pattern Computer, which emerged from stealth today in a live-streamed event from San Franci Read more…

By John Russell

Silicon Startup Raises ‘Prodigy’ for Hyperscale/AI Workloads

May 23, 2018

There's another silicon startup coming onto the HPC/hyperscale scene with some intriguing and bold claims. Silicon Valley-based Tachyum Inc., which has been eme Read more…

By Tiffany Trader

Japan Meteorological Agency Takes Delivery of Pair of Crays

May 21, 2018

Cray has supplied two identical Cray XC50 supercomputers to the Japan Meteorological Agency (JMA) in northwestern Tokyo. Boasting more than 18 petaflops combine Read more…

By Tiffany Trader

ASC18: Final Results Revealed & Wrapped Up

May 17, 2018

It was an exciting week at ASC18 in Nanyang, China. The student teams braved extreme heat, extremely difficult applications, and extreme competition in order to cross the cluster competition finish line. The gala awards ceremony took place on Wednesday. The auditorium was packed with student teams, various dignitaries, the media, and other interested parties. So what happened? Read more…

By Dan Olds

Spring Meetings Underscore Quantum Computing’s Rise

May 17, 2018

The month of April 2018 saw four very important and interesting meetings to discuss the state of quantum computing technologies, their potential impacts, and th Read more…

By Alex R. Larzelere

Quantum Network Hub Opens in Japan

May 17, 2018

Following on the launch of its Q Commercial quantum network last December with 12 industrial and academic partners, the official Japanese hub at Keio University is now open to facilitate the exploration of quantum applications important to science and business. The news comes a week after IBM announced that North Carolina State University was the first U.S. university to join its Q Network. Read more…

By Tiffany Trader

Democratizing HPC: OSC Releases Version 1.3 of OnDemand

May 16, 2018

Making HPC resources readily available and easier to use for scientists who may have less HPC expertise is an ongoing challenge. Open OnDemand is a project by t Read more…

By John Russell

MLPerf – Will New Machine Learning Benchmark Help Propel AI Forward?

May 2, 2018

Let the AI benchmarking wars begin. Today, a diverse group from academia and industry – Google, Baidu, Intel, AMD, Harvard, and Stanford among them – releas Read more…

By John Russell

How the Cloud Is Falling Short for HPC

March 15, 2018

The last couple of years have seen cloud computing gradually build some legitimacy within the HPC world, but still the HPC industry lies far behind enterprise I Read more…

By Chris Downing

Russian Nuclear Engineers Caught Cryptomining on Lab Supercomputer

February 12, 2018

Nuclear scientists working at the All-Russian Research Institute of Experimental Physics (RFNC-VNIIEF) have been arrested for using lab supercomputing resources to mine crypto-currency, according to a report in Russia’s Interfax News Agency. 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

Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate Read more…

By Rob Farber

AI Cloud Competition Heats Up: Google’s TPUs, Amazon Building AI Chip

February 12, 2018

Competition in the white hot AI (and public cloud) market pits Google against Amazon this week, with Google offering AI hardware on its cloud platform intended Read more…

By Doug Black

US Plans $1.8 Billion Spend on DOE Exascale Supercomputing

April 11, 2018

On Monday, the United States Department of Energy announced its intention to procure up to three exascale supercomputers at a cost of up to $1.8 billion with th Read more…

By Tiffany Trader

Lenovo Unveils Warm Water Cooled ThinkSystem SD650 in Rampup to LRZ Install

February 22, 2018

This week Lenovo took the wraps off the ThinkSystem SD650 high-density server with third-generation direct water cooling technology developed in tandem with par Read more…

By Tiffany Trader

Leading Solution Providers

SC17 Booth Video Tours Playlist

Altair @ SC17

Altair

AMD @ SC17

AMD

ASRock Rack @ SC17

ASRock Rack

CEJN @ SC17

CEJN

DDN Storage @ SC17

DDN Storage

Huawei @ SC17

Huawei

IBM @ SC17

IBM

IBM Power Systems @ SC17

IBM Power Systems

Intel @ SC17

Intel

Lenovo @ SC17

Lenovo

Mellanox Technologies @ SC17

Mellanox Technologies

Microsoft @ SC17

Microsoft

Penguin Computing @ SC17

Penguin Computing

Pure Storage @ SC17

Pure Storage

Supericro @ SC17

Supericro

Tyan @ SC17

Tyan

Univa @ SC17

Univa

HPC and AI – Two Communities Same Future

January 25, 2018

According to Al Gara (Intel Fellow, Data Center Group), high performance computing and artificial intelligence will increasingly intertwine as we transition to Read more…

By Rob Farber

Google Chases Quantum Supremacy with 72-Qubit Processor

March 7, 2018

Google pulled ahead of the pack this week in the race toward "quantum supremacy," with the introduction of a new 72-qubit quantum processor called Bristlecone. Read more…

By Tiffany Trader

CFO Steps down in Executive Shuffle at Supermicro

January 31, 2018

Supermicro yesterday announced senior management shuffling including prominent departures, the completion of an audit linked to its delayed Nasdaq filings, and Read more…

By John Russell

HPE Wins $57 Million DoD Supercomputing Contract

February 20, 2018

Hewlett Packard Enterprise (HPE) today revealed details of its massive $57 million HPC contract with the U.S. Department of Defense (DoD). The deal calls for HP Read more…

By Tiffany Trader

Deep Learning Portends ‘Sea Change’ for Oil and Gas Sector

February 1, 2018

The billowing compute and data demands that spurred the oil and gas industry to be the largest commercial users of high-performance computing are now propelling Read more…

By Tiffany Trader

Nvidia Ups Hardware Game with 16-GPU DGX-2 Server and 18-Port NVSwitch

March 27, 2018

Nvidia unveiled a raft of new products from its annual technology conference in San Jose today, and despite not offering up a new chip architecture, there were still a few surprises in store for HPC hardware aficionados. Read more…

By Tiffany Trader

Hennessy & Patterson: A New Golden Age for Computer Architecture

April 17, 2018

On Monday June 4, 2018, 2017 A.M. Turing Award Winners John L. Hennessy and David A. Patterson will deliver the Turing Lecture at the 45th International Sympo Read more…

By Staff

Part One: Deep Dive into 2018 Trends in Life Sciences HPC

March 1, 2018

Life sciences is an interesting lens through which to see HPC. It is perhaps not an obvious choice, given life sciences’ relative newness as a heavy user of H Read more…

By John Russell

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