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!

UCSD Web-based Tool Tracking CA Wildfires Generates 1.5M Views

October 16, 2017

Tracking the wildfires raging in northern CA is an unpleasant but necessary part of guiding efforts to fight the fires and safely evacuate affected residents. One such tool – Firemap – is a web-based tool developed b Read more…

By John Russell

Exascale Imperative: New Movie from HPE Makes a Compelling Case

October 13, 2017

Why is pursuing exascale computing so important? In a new video – Hewlett Packard Enterprise: Eighteen Zeros – four HPE executives, a prominent national lab HPC researcher, and HPCwire managing editor Tiffany Trader Read more…

By John Russell

Intel Delivers 17-Qubit Quantum Chip to European Research Partner

October 10, 2017

On Tuesday, Intel delivered a 17-qubit superconducting test chip to research partner QuTech, the quantum research institute of Delft University of Technology (TU Delft) in the Netherlands. The announcement marks a major milestone in the 10-year, $50-million collaborative relationship with TU Delft and TNO, the Dutch Organization for Applied Research, to accelerate advancements in quantum computing. Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

“Lunch & Learn” to Explore the Growing Applications of Genomic Analytics

In the digital age of medicine, healthcare providers are rapidly transforming their approach to patient care. Traditional technologies are no longer sufficient to process vast quantities of medical data (including patient histories, treatment plans, diagnostic reports, and more), challenging organizations to invest in a new style of IT to enable faster and higher-quality care. Read more…

Fujitsu Tapped to Build 37-Petaflops ABCI System for AIST

October 10, 2017

Fujitsu announced today it will build the long-planned AI Bridging Cloud Infrastructure (ABCI) which is set to become the fastest supercomputer system in Japan and will begin operation in fiscal 2018 (starts in April). A Read more…

By John Russell

Intel Delivers 17-Qubit Quantum Chip to European Research Partner

October 10, 2017

On Tuesday, Intel delivered a 17-qubit superconducting test chip to research partner QuTech, the quantum research institute of Delft University of Technology (TU Delft) in the Netherlands. The announcement marks a major milestone in the 10-year, $50-million collaborative relationship with TU Delft and TNO, the Dutch Organization for Applied Research, to accelerate advancements in quantum computing. Read more…

By Tiffany Trader

Fujitsu Tapped to Build 37-Petaflops ABCI System for AIST

October 10, 2017

Fujitsu announced today it will build the long-planned AI Bridging Cloud Infrastructure (ABCI) which is set to become the fastest supercomputer system in Japan Read more…

By John Russell

HPC Chips – A Veritable Smorgasbord?

October 10, 2017

For the first time since AMD's ill-fated launch of Bulldozer the answer to the question, 'Which CPU will be in my next HPC system?' doesn't have to be 'Whichever variety of Intel Xeon E5 they are selling when we procure'. Read more…

By Dairsie Latimer

Delays, Smoke, Records & Markets – A Candid Conversation with Cray CEO Peter Ungaro

October 5, 2017

Earlier this month, Tom Tabor, publisher of HPCwire and I had a very personal conversation with Cray CEO Peter Ungaro. Cray has been on something of a Cinderell Read more…

By Tiffany Trader & Tom Tabor

Intel Debuts Programmable Acceleration Card

October 5, 2017

With a view toward supporting complex, data-intensive applications, such as AI inference, video streaming analytics, database acceleration and genomics, Intel i Read more…

By Doug Black

OLCF’s 200 Petaflops Summit Machine Still Slated for 2018 Start-up

October 3, 2017

The Department of Energy’s planned 200 petaflops Summit computer, which is currently being installed at Oak Ridge Leadership Computing Facility, is on track t Read more…

By John Russell

US Exascale Program – Some Additional Clarity

September 28, 2017

The last time we left the Department of Energy’s exascale computing program in July, things were looking very positive. Both the U.S. House and Senate had pas Read more…

By Alex R. Larzelere

US Coalesces Plans for First Exascale Supercomputer: Aurora in 2021

September 27, 2017

At the Advanced Scientific Computing Advisory Committee (ASCAC) meeting, in Arlington, Va., yesterday (Sept. 26), it was revealed that the "Aurora" supercompute Read more…

By Tiffany Trader

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

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

NERSC Scales Scientific Deep Learning to 15 Petaflops

August 28, 2017

A collaborative effort between Intel, NERSC and Stanford has delivered the first 15-petaflops deep learning software running on HPC platforms and is, according Read more…

By Rob Farber

Oracle Layoffs Reportedly Hit SPARC and Solaris Hard

September 7, 2017

Oracle’s latest layoffs have many wondering if this is the end of the line for the SPARC processor and Solaris OS development. As reported by multiple sources Read more…

By John Russell

US Coalesces Plans for First Exascale Supercomputer: Aurora in 2021

September 27, 2017

At the Advanced Scientific Computing Advisory Committee (ASCAC) meeting, in Arlington, Va., yesterday (Sept. 26), it was revealed that the "Aurora" supercompute Read more…

By Tiffany Trader

Google Releases Deeplearn.js to Further Democratize Machine Learning

August 17, 2017

Spreading the use of machine learning tools is one of the goals of Google’s PAIR (People + AI Research) initiative, which was introduced in early July. Last w Read more…

By John Russell

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in Read more…

By Tiffany Trader

GlobalFoundries Puts Wind in AMD’s Sails with 12nm FinFET

September 24, 2017

From its annual tech conference last week (Sept. 20), where GlobalFoundries welcomed more than 600 semiconductor professionals (reaching the Santa Clara venue Read more…

By Tiffany Trader

Leading Solution Providers

Amazon Debuts New AMD-based GPU Instances for Graphics Acceleration

September 12, 2017

Last week Amazon Web Services (AWS) streaming service, AppStream 2.0, introduced a new GPU instance called Graphics Design intended to accelerate graphics. The Read more…

By John Russell

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

EU Funds 20 Million Euro ARM+FPGA Exascale Project

September 7, 2017

At the Barcelona Supercomputer Centre on Wednesday (Sept. 6), 16 partners gathered to launch the EuroEXA project, which invests €20 million over three-and-a-half years into exascale-focused research and development. Led by the Horizon 2020 program, EuroEXA picks up the banner of a triad of partner projects — ExaNeSt, EcoScale and ExaNoDe — building on their work... Read more…

By Tiffany Trader

Cray Moves to Acquire the Seagate ClusterStor Line

July 28, 2017

This week Cray announced that it is picking up Seagate's ClusterStor HPC storage array business for an undisclosed sum. "In short we're effectively transitioning the bulk of the ClusterStor product line to Cray," said CEO Peter Ungaro. Read more…

By Tiffany Trader

Delays, Smoke, Records & Markets – A Candid Conversation with Cray CEO Peter Ungaro

October 5, 2017

Earlier this month, Tom Tabor, publisher of HPCwire and I had a very personal conversation with Cray CEO Peter Ungaro. Cray has been on something of a Cinderell Read more…

By Tiffany Trader & Tom Tabor

IBM Advances Web-based Quantum Programming

September 5, 2017

IBM Research is pairing its Jupyter-based Data Science Experience notebook environment with its cloud-based quantum computer, IBM Q, in hopes of encouraging a new class of entrepreneurial user to solve intractable problems that even exceed the capabilities of the best AI systems. Read more…

By Alex Woodie

Intel Launches Software Tools to Ease FPGA Programming

September 5, 2017

Field Programmable Gate Arrays (FPGAs) have a reputation for being difficult to program, requiring expertise in specialty languages, like Verilog or VHDL. Easin Read more…

By Tiffany Trader

Intel, NERSC and University Partners Launch New Big Data Center

August 17, 2017

A collaboration between the Department of Energy’s National Energy Research Scientific Computing Center (NERSC), Intel and five Intel Parallel Computing Cente Read more…

By Linda Barney

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