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Each carries its own:",{"data":13142,"content":13143,"nodeType":12878},{},[13144,13154,13164,13174],{"data":13145,"content":13146,"nodeType":12867},{},[13147],{"data":13148,"content":13149,"nodeType":1195},{},[13150],{"data":13151,"marks":13152,"value":13153,"nodeType":1194},{},[],"Data copies",{"data":13155,"content":13156,"nodeType":12867},{},[13157],{"data":13158,"content":13159,"nodeType":1195},{},[13160],{"data":13161,"marks":13162,"value":13163,"nodeType":1194},{},[],"Metadata layers",{"data":13165,"content":13166,"nodeType":12867},{},[13167],{"data":13168,"content":13169,"nodeType":1195},{},[13170],{"data":13171,"marks":13172,"value":13173,"nodeType":1194},{},[],"Protection models",{"data":13175,"content":13176,"nodeType":12867},{},[13177],{"data":13178,"content":13179,"nodeType":1195},{},[13180],{"data":13181,"marks":13182,"value":13183,"nodeType":1194},{},[],"Reserved capacity pools",{"data":13185,"content":13186,"nodeType":1195},{},[13187],{"data":13188,"marks":13189,"value":13190,"nodeType":1194},{},[],"This creates duplicated storage consumption across environments.",{"data":13192,"content":13193,"nodeType":1195},{},[13194],{"data":13195,"marks":13196,"value":13197,"nodeType":1194},{},[],"Fragmentation is one of the least obvious but most expensive sources of flash waste because it compounds every other layer of overhead. It also reduces data reduction efficiency by isolating datasets that could otherwise be optimized together.",{"data":13199,"content":13200,"nodeType":12760},{},[13201],{"data":13202,"marks":13203,"value":13205,"nodeType":1194},{},[13204],{"type":12758},"\nThe Simple Formula for Total Data Overhead",{"data":13207,"content":13208,"nodeType":1195},{},[13209],{"data":13210,"marks":13211,"value":13214,"nodeType":1194},{},[13212],{"type":13213},"code","Total Data Overhead = (Protection + Metadata + Buffers + Fragmentation Waste) ÷ Raw Capacity",{"data":13216,"content":13217,"nodeType":1195},{},[13218],{"data":13219,"marks":13220,"value":13221,"nodeType":1194},{},[],"This formula provides a simple way to evaluate how much storage is being consumed before workloads ever use it. It reveals the difference between what an organization bought and what it can actually use.",{"data":13223,"content":13224,"nodeType":1195},{},[13225],{"data":13226,"marks":13227,"value":13228,"nodeType":1194},{},[],"This is the gap most storage planning models fail to measure.",{"data":13230,"content":13231,"nodeType":1195},{},[13232],{"data":13233,"marks":13234,"value":13235,"nodeType":1194},{},[],"In legacy environments:",{"data":13237,"content":13238,"nodeType":12878},{},[13239,13249],{"data":13240,"content":13241,"nodeType":12867},{},[13242],{"data":13243,"content":13244,"nodeType":1195},{},[13245],{"data":13246,"marks":13247,"value":13248,"nodeType":1194},{},[],"Total overhead can reach 50–66% with replication, or 21–28% on shared-nothing flash systems with erasure coding plus system reserve",{"data":13250,"content":13251,"nodeType":12867},{},[13252],{"data":13253,"content":13254,"nodeType":1195},{},[13255],{"data":13256,"marks":13257,"value":13258,"nodeType":1194},{},[],"Usable storage becomes the minority of purchased capacity",{"data":13260,"content":13261,"nodeType":1195},{},[13262],{"data":13263,"marks":13264,"value":13265,"nodeType":1194},{},[],"Architectures designed without these legacy tradeoffs collapse total overhead by an order of magnitude, increasing usable storage without increasing purchased flash. That's why total data overhead is one of the most important efficiency metrics an enterprise can measure.\n",{"data":13267,"content":13268,"nodeType":12760},{},[13269],{"data":13270,"marks":13271,"value":13273,"nodeType":1194},{},[13272],{"type":12758},"Why Enterprises Don't See the Waste",{"data":13275,"content":13276,"nodeType":1195},{},[13277],{"data":13278,"marks":13279,"value":13280,"nodeType":1194},{},[],"Most enterprises don't measure storage inefficiency directly. 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Across the fleet, the median data reduction ratio is 1.87:1, with some datasets exceeding 8:1.\n",{"data":13467,"content":13468,"nodeType":12760},{},[13469],{"data":13470,"marks":13471,"value":13473,"nodeType":1194},{},[13472],{"type":12758},"How Total Data Overhead Connects to the SSD Shortage",{"data":13475,"content":13476,"nodeType":1195},{},[13477],{"data":13478,"marks":13479,"value":13480,"nodeType":1194},{},[],"The SSD shortage is an external constraint. Total data overhead is an internal one.",{"data":13482,"content":13483,"nodeType":1195},{},[13484],{"data":13485,"marks":13486,"value":13487,"nodeType":1194},{},[],"The shortage has made flash more expensive and harder to procure — and AI is simultaneously increasing the volume of data organizations need to retain and process, making storage efficiency more important than ever.",{"data":13489,"content":13490,"nodeType":1195},{},[13491,13495,13504],{"data":13492,"marks":13493,"value":13494,"nodeType":1194},{},[],"That's why the ",{"data":13496,"content":13498,"nodeType":1814},{"uri":13497},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fovercoming-the-ssd-shortage-with-vast",[13499],{"data":13500,"marks":13501,"value":13503,"nodeType":1194},{},[13502],{"type":13460},"enterprise SSD shortage",{"data":13505,"marks":13506,"value":13507,"nodeType":1194},{},[]," and total data overhead have to be understood together. One explains the supply pressure; the other explains why that pressure feels worse than it should. The fastest path to more usable storage is reducing waste, not procurement.",{"data":13509,"content":13510,"nodeType":12760},{},[13511],{"data":13512,"marks":13513,"value":13515,"nodeType":1194},{},[13514],{"type":12758},"\nWhat You Should Measure Before Buying More Storage",{"data":13517,"content":13518,"nodeType":1195},{},[13519],{"data":13520,"marks":13521,"value":13522,"nodeType":1194},{},[],"Before purchasing additional storage, measure:",{"data":13524,"content":13525,"nodeType":12878},{},[13526,13536,13546,13556,13566],{"data":13527,"content":13528,"nodeType":12867},{},[13529],{"data":13530,"content":13531,"nodeType":1195},{},[13532],{"data":13533,"marks":13534,"value":13535,"nodeType":1194},{},[],"Effective usable capacity versus raw capacity",{"data":13537,"content":13538,"nodeType":12867},{},[13539],{"data":13540,"content":13541,"nodeType":1195},{},[13542],{"data":13543,"marks":13544,"value":13545,"nodeType":1194},{},[],"Replication factors across systems",{"data":13547,"content":13548,"nodeType":12867},{},[13549],{"data":13550,"content":13551,"nodeType":1195},{},[13552],{"data":13553,"marks":13554,"value":13555,"nodeType":1194},{},[],"Duplicate datasets across silos",{"data":13557,"content":13558,"nodeType":12867},{},[13559],{"data":13560,"content":13561,"nodeType":1195},{},[13562],{"data":13563,"marks":13564,"value":13565,"nodeType":1194},{},[],"Reserved versus actively used flash",{"data":13567,"content":13568,"nodeType":12867},{},[13569],{"data":13570,"content":13571,"nodeType":1195},{},[13572],{"data":13573,"marks":13574,"value":13575,"nodeType":1194},{},[],"Storage utilization consistency across environments",{"data":13577,"content":13578,"nodeType":1195},{},[13579],{"data":13580,"marks":13581,"value":13582,"nodeType":1194},{},[],"These measurements create visibility into where flash is being consumed and where usable capacity can be recovered. 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Layer counts continue to rise, but fab throughput does not scale linearly with density. Process times increase, toolchains diverge, and new fabs take years to bring online. Even the hard drive industry, long assumed to be the safety valve during flash shortages, is facing its own mechanical and power constraints. The result is a market where buying more media is no longer a reliable strategy.",{"data":13967,"content":13968,"nodeType":1195},{},[13969],{"data":13970,"marks":13971,"value":13972,"nodeType":1194},{},[],"In conversations with customers navigating the current crunch, one pattern shows up repeatedly. Organizations are discovering that their real flash consumption has far less to do with the amount of data they generate than with how inefficiently that data is stored. Triplicated databases sitting on top of triplicated object stores. Erasure codes chosen for simplicity rather than efficiency. 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world is running out of capacity.",{"data":14544,"content":14545,"nodeType":1195},{},[14546],{"data":14547,"marks":14548,"value":14549,"nodeType":1194},{},[],"Not in some distant, theoretical future - but right now, across data centers everywhere. Flash supply is constrained. Lead times are growing. And for many organizations, infrastructure planning has quietly slipped into something uncomfortably close to rationing: get in line, wait your turn, and accept whatever capacity your traditional OEM is willing - or able - to allocate.",{"data":14551,"content":14552,"nodeType":1195},{},[14553,14557,14565],{"data":14554,"marks":14555,"value":14556,"nodeType":1194},{},[],"For too long, the industry has treated this situation as inevitable, a supply problem to be endured rather than a failure of design to be corrected. But what most organizations are facing isn’t a true shortage of storage; it’s a ",{"data":14558,"content":14560,"nodeType":1814},{"uri":14559},"www.vastdata.com\u002Fblog\u002Fovercoming-the-ssd-shortage-with-vast",[14561],{"data":14562,"marks":14563,"value":14564,"nodeType":1194},{},[],"shortage of effective capacity",{"data":14566,"marks":14567,"value":14568,"nodeType":1194},{},[],". 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New Storage Economics: Solidigm and VAST Deliver All-SSD Capacity at Half the Cost of HDD","new-storage-economics-solidigm-vast-deliver-all-ssd-capacity","2025-09-18","Brian Verkley, Product Marketing Manager",{"metadata":16508,"sys":16511,"fields":16520},{"tags":16509,"concepts":16510},[],[],{"space":16512,"id":16514,"type":14,"createdAt":16515,"updatedAt":16515,"environment":16516,"publishedVersion":900,"revision":1344,"contentType":16518,"locale":672},{"sys":16513},{"type":656,"linkType":657,"id":658},"6YIf6pfUcPfL6HcofY8bD","2025-09-15T14:27:00.539Z",{"sys":16517},{"id":664,"type":656,"linkType":665},{"sys":16519},{"type":656,"linkType":670,"id":12726},{"entryTitle":16503,"image":16521},{"metadata":16522,"sys":16525,"fields":16532},{"tags":16523,"concepts":16524},[],[],{"space":16526,"id":16528,"type":933,"createdAt":16529,"updatedAt":16529,"environment":16530,"publishedVersion":846,"revision":1344,"locale":672},{"sys":16527},{"type":656,"linkType":657,"id":658},"1nJgZvqCKZvqs3NqeNyEhi","2025-09-15T14:26:51.858Z",{"sys":16531},{"id":664,"type":656,"linkType":665},{"title":16503,"description":16503,"file":16533},{"url":16534,"details":16535,"fileName":16538,"contentType":1143},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F1nJgZvqCKZvqs3NqeNyEhi\u002F5115d442b711aec3bb49149687988423\u002Fnew-storage-economics-solidigm-vast-deliver-all-ssd-capacity.webp",{"size":16536,"image":16537},44864,{"width":1140,"height":1141},"new-storage-economics-solidigm-vast-deliver-all-ssd-capacity.webp","For years, enterprise IT has played a strategic, often frustrating, game of resource allocation. Flash storage devices, with their undeniable speed, were the prized assets - reserved for the \"hot tier,\" the critical applications, the workloads requiring the lowest latencies and the highest throughput. The rest of the \"warm\" or \"cold\" data was relegated to slower, more economical HDD tiers. This was the accepted compromise, a necessary balancing act between performance and budget.\n\nHowever, when analyzing the Total Cost of Ownership (TCO), that equation has been inverted. Flash now provides a superior economic advantage. A \u003Ca href=\"https:\u002F\u002Fwww.solidigm.com\u002Fproducts\u002Ftechnology\u002Feconomics-of-exabyte-data-storage.html?fbclid=IwY2xjawMCs9lleHRuA2FlbQIxMQBicmlkETBrdkJHVmZROXhWcWFVa3RWAR7Cdu2fEX4BwDCDDhbs7LoO83F0iz2-nlVbvkztCC58CiA3aPgGAKVOFDrHGw_aem_9gUqa28yTbJw3i2IvSirKQ\">whitepaper by Solidigm and VAST\u003C\u002Fa> quantifies this, revealing that flash offers a __58.9% lower TCO__ than traditional HDD tiers.\n\nRecent innovations from Solidigm and VAST are changing storage economics and eliminating the tiered storage paradigm completely.  It is not cheaper to store cold data on flash. The cheapest medium for data is flash. Gone is the need for tiers, and with them the auto-tiering policies, the need for separate storage cluster designs, and the constant moving of data.\n\n#### The Technology Driving the Economic Shift\n\nCurrent hardware and software architectures have fundamentally altered storage economics, making a universal all-flash infrastructure more cost-effective than traditional hard drive systems. This shift is driven by two key innovations working in tandem.\n\n- __Hardware Breakthroughs in Density:__ The primary hardware change is the introduction of \u003Ca href=\"https:\u002F\u002Fwww.solidigm.com\u002Fproducts\u002Fdata-center\u002Fd5\u002Fp5336.html#form=U.2%2015mm&cap=122.88TB\">Solidigm's 122-layer QLC NAND flash\u003C\u002Fa>, which provides unprecedented storage density. This allows for a massive reduction in the physical footprint of storage systems.\n- __Compounding Software Efficiencies:__ These hardware gains are amplified by VAST’s innovative software.\n  - VAST’s advanced \u003Ca href=\"https:\u002F\u002Fwww.vastdata.com\u002Fwhitepaper#a-breakthrough-approach-to-data-reduction\">Similarity data reduction\u003C\u002Fa> technique goes far beyond standard compression to find and eliminate redundancy at a granular level, meaning you need nearly 3x less raw storage capacity compared to HDD solutions. This level of granular data reduction can only be practical on flash because spinning media depends on sequential data for speed. When duplicate data is removed, many sequential reads look like random IO, which flash drives excel at, but HDDs struggle with.\n  - \u003Ca href=\"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fbreaking-resiliency-trade-offs-with-locally-decodable-erasure-codes\">Locally decodable codes\u003C\u002Fa> provide an extremely efficient and resilient form of data protection, further enhancing cost-effectiveness by reducing wasted overhead.\n  - \u003Ca href=\"https:\u002F\u002Fwww.vastdata.com\u002Fwhitepaper\u002F#VASTForesight\">VAST Foresight\u003C\u002Fa> is an intelligent data placement feature that groups data based on its predicted retention time before writing it to QLC flash, a strategy that improves the endurance and extends the lifespan of the storage system.\n  - Crucially, this entire solution is built for longevity. By offering 10-year support on QLC SSDs, the VAST platform eliminates the costly and disruptive hardware refresh cycles required by HDD systems every few years.\n\nTogether, these technologies mean a VAST system can store significantly more data on less physical hardware. An Exabyte of data that once required 52 racks of HDDs can now fit into just 5 racks, cutting data center space costs by over 90% and power and cooling costs by more than 75% leading to a total cost of ownership (TCO) that is over 50% lower than traditional HDD-based systems.\n\n#### The Proof is in the Numbers: A TCO Analysis\n\nAll of this is now documented in a \u003Ca href=\"https:\u002F\u002Fwww.solidigm.com\u002Fproducts\u002Ftechnology\u002Feconomics-of-exabyte-data-storage.html\">10-year Total Cost of Ownership (TCO) analysis\u003C\u002Fa>. While this analysis focuses on a 1EB deployment, the technical and economic advantages delivered by the platform begin at the smallest VAST cluster and extend to Zettabyte (ZB) scale. These savings come from several compounding factors:\n\n\u003Cp style=\"text-indent: -20px; margin-left: 36px;\">\u003Cb style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">1.\u003C\u002Fb> \u003Cspan style=\"font-weight: 700;\">Eliminating Refresh Cycles:\u003C\u002Fspan> Traditional HDD infrastructures require costly and disruptive hardware refreshes every 3-5 years. The VAST Data platform offers 10-year support on QLC SSDs, avoiding multiple hardware repurchases and the associated labor costs. While the initial outlay for the SSD\u002FVAST solution might be higher, the total 10-year hardware cost is nearly 30% lower because those expensive HDD refresh cycles are eliminated.\u003C\u002Fp>\n\n![Total 10-year Hardware Cost ($M)](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6IYwEHSOKe6RK1ThlhdQwD\u002F9713a93eb27ff55820b87e92a334a0af\u002Fnew-storage-economics-solidigm-vast-deliver-all-ssd-capacity-1.webp)\n\n\u003Cp style=\"text-indent: -20px; margin-left: 36px;\">\u003Cb style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">2.\u003C\u002Fb> \u003Cspan style=\"font-weight: 700;\">Effective Data Reduction:\u003C\u002Fspan> VAST’s Similarity data reduction technology achieves remarkable data reduction rates without the performance penalties that cripple deduplication solutions on HDDs. Because HDDs depend on sequential I\u002FO, HDD based tiers often use little to no effective data reduction (modeled at 1:1). The result: you need only one-third of the raw physical storage capacity with VAST compared to an HDD solution to achieve the same usable capacity.\u003C\u002Fp>\n\n![Calculated Raw Capacity Required (EB)](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3E6tKrtcliZ4T9MdGRdogh\u002F8fe6bbe268068d2fc2bcfc16aa0f2ef3\u002Fnew-storage-economics-solidigm-vast-deliver-all-ssd-capacity-2.webp)\n\n\u003Cp style=\"text-indent: -20px; margin-left: 36px;\">\u003Cb style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">3.\u003C\u002Fb> \u003Cspan style=\"font-weight: 700;\">Higher Density, LowerFootprint:\u003C\u002Fspan> The combination of Solidigm's ultra-dense 122TB SSDs and VAST's software efficiencies means a dramatically smaller physical presence. What might require 52 racks for an Exabyte of usable HDD storage can be consolidated into just five racks with the Solidigm + VAST solution. This translates into over 90% savings in data center space costs and a power and cooling cost reduction of over 75%. Together, the lower power and space requirements support more sustainable data center operations.\u003C\u002Fp>\n\n![Racks Needed](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F1VE9KfFfGqxbQ8rkxyUbl3\u002Fb7968b4faaa541547d72bc192d111ae8\u002Fnew-storage-economics-solidigm-vast-deliver-all-ssd-capacity-3.webp)\n\n![Total 10-Year Power Cost ($M)](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F4GG8JLYtJ9YcwCrQ58kfSI\u002Fecd0faceed24172dfb3b466f7e1e534a\u002Fnew-storage-economics-solidigm-vast-deliver-all-ssd-capacity-4.webp)\n\nThis combination of extended hardware life, superior data reduction, and higher density fundamentally rewires the economics of enterprise storage.\n\nWhen flash is no longer a scarce, premium resource, the whole equation of enterprise computing changes. IT leaders no longer have to ration performance or make uncomfortable compromises between speed and cost. Instead, they can design around possibilities:\n\n- __AI at Full Fidelity:__ With flash now the most economical tier, enterprises can finally train and analyze on their entire data history, not just today’s “hot” subset. Aquatic Capital Management, for example, uses VAST to unify decades-worth of structured and unstructured market, documentation, and proprietary data into a single high-performance flash infrastructure—giving researchers both full corpus fidelity and extended historical breadth. As their Head of Infrastructure notes: “the more different multi-variant data sources that they can assemble, the more accurate their models can become.” The result? AI models that are smarter, more contextually rich, and more accurate—made possible by collapsing the old trade-offs of cost, speed, and retention.\n- __Analytics Without Delay:__ The NHL rebuilt its media platform on VAST, shifting a massive video archive onto flash and expanding into real-time game-footage operations so editors and teams can cut highlights and move content while games are still underway. With replication across all 32 arenas, the league shortened the path from capture to distribution and set itself up for computer-vision and AI discovery on the same library.   A flash-backed foundation turned archival footage into something you can actually work with in the moment, rather than overnight.  \n- __Operational Agility:__ At spinTwo, VAST transformed months-long life sciences compute workloads into just weeks, freeing researchers to focus on discovery instead of infrastructure headaches. As their team notes: “With VAST Data, we process large datasets much faster, allowing scientists to spend more time on research rather than waiting for data to process. What used to take years, can now be completed in weeks, and we’re working to reduce that to hours.”    This kind of performance lets developers and data scientists spin up full datasets on the fly, test new pipelines, and iterate rapidly, without entangling in storage approvals or juggling quotas.\n\n#### A New Era of Storage Economics\n\nTiering solved an old cost problem. This Solidigm + VAST study shows that a single all-flash tier wins on the numbers: 58.9% lower 10-year TCO, 5 racks instead of 52, power and cooling down by more than 75%, and space costs down by more than 90%. Density, Similarity data reduction, locally decodable protection, and 10-year media support combine into a simpler system that stores more data on less hardware.\n\nTo see the model and assumptions, read the Solidigm + VAST TCO analysis. 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administrator’s first commandment “Thou shalt not lose thy user’s data.” The VAST Data Platform breaks those trade-offs with new math, locally decodable erasure codes that can protect against multiple device failures without the overhead and rebuild complexity conventional solutions impose.",{"data":17040,"content":17041,"nodeType":17046},{},[17042],{"data":17043,"marks":17044,"value":17045,"nodeType":1194},{},[],"Reed-Solomon protection at a price","heading-3",{"data":17048,"content":17049,"nodeType":1195},{},[17050,17054,17062],{"data":17051,"marks":17052,"value":17053,"nodeType":1194},{},[],"The vast majority (no pun intended) of storage systems that use erasure coding, including most RAID 6 implementations, today use a class of error correction codes developed in 1960 by mathematicians Irving S. Reed and Gustave Solomon. A system using ",{"data":17055,"content":17057,"nodeType":1814},{"uri":17056},"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FReed%E2%80%93Solomon_error_correction",[17058],{"data":17059,"marks":17060,"value":17061,"nodeType":1194},{},[],"Reed-Solomon",{"data":17063,"marks":17064,"value":17065,"nodeType":1194},{},[]," encoding can calculate two, three or even more ECC strips to protect a set of data strips.",{"data":17067,"content":17088,"nodeType":14766},{"target":17068},{"metadata":17069,"sys":17072,"fields":17079},{"tags":17070,"concepts":17071},[],[],{"space":17073,"id":17075,"type":933,"createdAt":17076,"updatedAt":17076,"environment":17077,"publishedVersion":846,"revision":1344,"locale":672},{"sys":17074},{"type":656,"linkType":657,"id":658},"6DWk0gvfOQWlnMdKI71ebq","2023-08-31T22:31:01.617Z",{"sys":17078},{"id":664,"type":656,"linkType":665},{"title":16982,"description":16982,"file":17080},{"url":17081,"details":17082,"fileName":17087,"contentType":1143},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6DWk0gvfOQWlnMdKI71ebq\u002Fa11bcd72ac66bb6e4b82a87a98bcf25e\u002Fbreaking-resiliency-trade-offs-with-locally-decodable-erasure-codes-1.webp",{"size":17083,"image":17084},33722,{"width":17085,"height":17086},2200,983,"breaking-resiliency-trade-offs-with-locally-decodable-erasure-codes-1.webp",[],{"data":17090,"content":17091,"nodeType":1195},{},[17092,17096,17103],{"data":17093,"marks":17094,"value":17095,"nodeType":1194},{},[],"Since parity is the simplest form of erasure code the generalized notation for describing erasure coded data stripes is nD+xP for n data strips and x ECC or parity strips. A RAID6 or similar, system using 7 data strips and 2 ECC strips would, therefore, be 7D+2P. Reed-Solomon codes aren’t limited to one or two ECC strips theoretically supporting an arbitrary number of ECC strips. Some ",{"data":17097,"content":17099,"nodeType":1814},{"uri":17098},"..\u002F..\u002Fglossary\u002Fscale-out\u002F",[17100],{"data":17101,"marks":17102,"value":364,"nodeType":1194},{},[],{"data":17104,"marks":17105,"value":17106,"nodeType":1194},{},[]," storage systems use Reed-Solomon codes with three or more protection strips.",{"data":17108,"content":17109,"nodeType":12760},{},[17110],{"data":17111,"marks":17112,"value":17113,"nodeType":1194},{},[],"Wide stripes create problems",{"data":17115,"content":17116,"nodeType":1195},{},[17117],{"data":17118,"marks":17119,"value":17120,"nodeType":1194},{},[],"The problem with Reed-Solomon codes, beyond the complex Galois field math they use, is that reconstructing an unreadable data strip requires the data from all the surviving data strips, and at least one of the ECC strips to rebuild the data. In a 7D+2P system like our example above one 4KB read request from a user-application would be amplified to a dozen I\u002FOs and rebuilding from a failed drive will keep the remaining 8 drives in the set busy for a long while.",{"data":17122,"content":17123,"nodeType":1195},{},[17124],{"data":17125,"marks":17126,"value":17127,"nodeType":1194},{},[],"As a result, storage architects have to trade-off the greater efficiency and read performance, of wide data protection stripes against the I\u002FO amplification wide stripes, can create. Should a conventional storage array receive a request to perform a 4KB write in the middle of a 16D+4P erasure code stripe (the widest I know of in common use), the system would have to read all 16 strips of data, recalculate the four protection strips and then write all 20 strips back.",{"data":17129,"content":17130,"nodeType":1195},{},[17131],{"data":17132,"marks":17133,"value":17134,"nodeType":1194},{},[],"While log structures ameliorate this read-modify-write problem to some extent rebuilding that 16D+4P system after a device failure will require reading the entire contents of at least 16 of the surviving devices. As a result, most storage architects use narrower stripes like with lower resiliency and 33% overhead vs. the 25% overhead of a 16+4 stripe, sacrificing both resilience and efficiency to the downsides of Reed-Solomon coding.",{"data":17136,"content":17137,"nodeType":17046},{},[17138],{"data":17139,"marks":17140,"value":17141,"nodeType":1194},{},[],"Locally Decodable Codes Break the Trade-Offs",{"data":17143,"content":17144,"nodeType":1195},{},[17145,17149,17157],{"data":17146,"marks":17147,"value":17148,"nodeType":1194},{},[],"VAST Data has invented a new class of error correction codes that borrow from principals that mathematicians define as locally-decodable codes to break the long-standing tradeoffs between writes stripe resilience and the cost of data protection. The locally-decodable aspect of our concept means that the ",{"data":17150,"content":17152,"nodeType":1814},{"uri":17151},"..\u002F..\u002Fglossary\u002Fvast-cluster\u002F",[17153],{"data":17154,"marks":17155,"value":17156,"nodeType":1194},{},[],"VAST cluster",{"data":17158,"marks":17159,"value":17160,"nodeType":1194},{},[]," can reconstruct unreadable data from only a “local” fraction of the total number of data strips in a protection stripe. For our codes that fraction is 1\u002FXth of the data strips where X is the number of protection strips in the set.",{"data":17162,"content":17163,"nodeType":12760},{},[17164],{"data":17165,"marks":17166,"value":17167,"nodeType":1194},{},[],"Higher Resiliency, Lower Rebuild Cost",{"data":17169,"content":17170,"nodeType":1195},{},[17171],{"data":17172,"marks":17173,"value":17174,"nodeType":1194},{},[],"Using locally decodable codes allows us to use wide stripes, with very high levels of resiliency. The codes scale dynamically with the cluster as the system scales, where both N (data) and (K) resilience can scale independently. Using Markov calculations, we’ve determined that an example 150+4 encoded stripe has an MTTDL (Mean Time To Data Loss) of over 42 million years with just 2.66% overhead. Data is protected against four or more failures - which is relatively very high as compared to how traditional systems protect a failure domain - while the system can reconstruct the data by reading from just 1\u002F4th of the remaining stripes in an encoded write stripe.",{"data":17176,"content":17177,"nodeType":1195},{},[17178],{"data":17179,"marks":17180,"value":17181,"nodeType":1194},{},[],"While 150+4 is a somewhat typical example of the scale of our codes on an average petabyte-sized system, these codes can scale to provide greater levels of protection on smaller systems and can even scale wider on larger systems to feature higher levels of data protection - such as 500+8 - resulting in a mean time to data loss of over 1 quadrillion years.",{"data":17183,"content":17184,"nodeType":12760},{},[17185],{"data":17186,"marks":17187,"value":17188,"nodeType":1194},{},[],"How Locally Decodable Codes Work",{"data":17190,"content":17191,"nodeType":1195},{},[17192],{"data":17193,"marks":17194,"value":17195,"nodeType":1194},{},[],"Reed-Solomon codes generate P protection strips from a single set of data strips. Since each Reed-Solomon protection strip is generated from all of the protected data strips the calculation for regenerating the data from a lost strip requires all the surviving data strips and one or more protection strips.",{"data":17197,"content":17198,"nodeType":1195},{},[17199],{"data":17200,"marks":17201,"value":17202,"nodeType":1194},{},[],"Our locally decodable codes, on the other hand, calculate each protection strip from slightly different sets of data strips across more than just one stripe of data. This allows protection strips to “stand in” for groups of data strips in the rebuild calculations. A VAST system using P protection strips can reconstruct data from 1\u002FPth the data strips. For our typical nD+4P encoding we need to read just 1\u002F4th the data during a reconstruction where a Reed-Solomon based system would need to read it all.",{"data":17204,"content":17225,"nodeType":14766},{"target":17205},{"metadata":17206,"sys":17209,"fields":17216},{"tags":17207,"concepts":17208},[],[],{"space":17210,"id":17212,"type":933,"createdAt":17213,"updatedAt":17213,"environment":17214,"publishedVersion":846,"revision":1344,"locale":672},{"sys":17211},{"type":656,"linkType":657,"id":658},"7f2xs9JPmdEMqjnjLBnB0V","2023-08-31T22:31:36.828Z",{"sys":17215},{"id":664,"type":656,"linkType":665},{"title":16982,"description":16982,"file":17217},{"url":17218,"details":17219,"fileName":17224,"contentType":1143},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F7f2xs9JPmdEMqjnjLBnB0V\u002F88b6932c5f2a93988792fd33bbdfe5f6\u002Fbreaking-resiliency-trade-offs-with-locally-decodable-erasure-codes-2.webp",{"size":17220,"image":17221},86276,{"width":17222,"height":17223},2126,2006,"breaking-resiliency-trade-offs-with-locally-decodable-erasure-codes-2.webp",[],{"data":17227,"content":17228,"nodeType":1195},{},[17229],{"data":17230,"marks":17231,"value":17233,"nodeType":1194},{},[17232],{"type":15052},"Reconstructing a failed drive with 1\u002F4th the I\u002FO",{"data":17235,"content":17236,"nodeType":1195},{},[17237],{"data":17238,"marks":17239,"value":17240,"nodeType":1194},{},[],"For our typical 150D+4P protection level that means the system has to read 1\u002F4th of 150 data strips or 38 strips. The four protection strips contain enough data about the remaining 112 data strips that the contents of the missing strip can be calculated without them. This allows us to break the trade-off between storage efficiency and rebuild complexity that systems using Reed-Solomon codes are forced into.",{"data":17242,"content":17243,"nodeType":17046},{},[17244],{"data":17245,"marks":17246,"value":17247,"nodeType":1194},{},[],"Empowered by DASE",{"data":17249,"content":17250,"nodeType":1195},{},[17251,17255,17263],{"data":17252,"marks":17253,"value":17254,"nodeType":1194},{},[],"The math of locally decodable codes is only part of the story. Conventional storage platforms would struggle to assemble 150 data strips in memory, and then write that data to 154 backend SSDs without bottlenecking on their SAS links or inter-node networks. 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Gartner expects at least 75% of IT organizations to face one or more ransomware attacks by 2025. And many of the IT leaders I meet operate with a “not if or when, but how many” mindset.",{"data":17643,"content":17644,"nodeType":1195},{},[17645],{"data":17646,"marks":17647,"value":17648,"nodeType":1194},{},[],"It used to be enough for organizations to develop a hardened network perimeter that would satisfy security concerns, but the interconnectedness of today’s various on-prem, hybrid cloud, and SaaS applications present a massive opportunity for bad actors to infiltrate your network.",{"data":17650,"content":17651,"nodeType":1195},{},[17652],{"data":17653,"marks":17654,"value":17655,"nodeType":1194},{},[],"That means the primary DR consideration for almost any organization is how fast you can recover. Having backup data is only part of the equation. You must ensure you can quickly restore those backups – and this is where legacy backup targets cannot help you.",{"data":17657,"content":17658,"nodeType":17046},{},[17659],{"data":17660,"marks":17661,"value":17662,"nodeType":1194},{},[],"The Evolving Nature of Restores",{"data":17664,"content":17665,"nodeType":1195},{},[17666],{"data":17667,"marks":17668,"value":17669,"nodeType":1194},{},[],"We used to think about RTO objectives in hourly timeframes, but today’s sophisticated attacks that lock up entire IT estates have shifted such intervals to weeks, if not months. And while the nature of ransomware attacks is well understood, the path to recovery is less clear. Some organizations will pay the attackers the ransom to (presumably) regain access to their data, while others may choose to immediately start restoring data to their last known non-infected state. If you can do the latter well, you can save your organization massive ransom payouts, and more importantly, protect yourself from future attacks.",{"data":17671,"content":17672,"nodeType":1195},{},[17673],{"data":17674,"marks":17675,"value":17676,"nodeType":1194},{},[],"The path to fast restores is typically dependent on a few variables:",{"data":17678,"content":17679,"nodeType":17732},{},[17680,17690,17700,17722],{"data":17681,"content":17682,"nodeType":12867},{},[17683],{"data":17684,"content":17685,"nodeType":1195},{},[17686],{"data":17687,"marks":17688,"value":17689,"nodeType":1194},{},[],"Your network topology, or the path your data takes to move from source to destination and the time it needs to get there. Organizations often optimize bandwidth through data reduction, load balancing, throttling, etc.",{"data":17691,"content":17692,"nodeType":12867},{},[17693],{"data":17694,"content":17695,"nodeType":1195},{},[17696],{"data":17697,"marks":17698,"value":17699,"nodeType":1194},{},[],"Your number of data movers (physical or virtual), i.e. the backup agents running on your servers. Increasing the number of data movers in a system enables multi-streamed restores.",{"data":17701,"content":17702,"nodeType":12867},{},[17703],{"data":17704,"content":17705,"nodeType":1195},{},[17706,17710,17718],{"data":17707,"marks":17708,"value":17709,"nodeType":1194},{},[],"Your landing zone, or a clean network destination for the recovery of your immutable data, or ",{"data":17711,"content":17713,"nodeType":1814},{"uri":17712},"..\u002F..\u002Fblog\u002Fmaking-data-indestructible\u002F",[17714],{"data":17715,"marks":17716,"value":17717,"nodeType":1194},{},[],"indestructible snapshots",{"data":17719,"marks":17720,"value":17721,"nodeType":1194},{},[],", in the event of an attack.",{"data":17723,"content":17724,"nodeType":12867},{},[17725],{"data":17726,"content":17727,"nodeType":1195},{},[17728],{"data":17729,"marks":17730,"value":17731,"nodeType":1194},{},[],"Your data protection target. In enterprise data centers this is most often the bottleneck, as traditional backup appliances read (restore) data about 20% as fast as they write (backup) data. Therefore, even if you do everything else right (steps 1-3 above), restores are only as fast as the read performance of the backup target.","ordered-list",{"data":17734,"content":17735,"nodeType":1195},{},[17736],{"data":17737,"marks":17738,"value":17739,"nodeType":1194},{},[],"Purpose-built backup appliances (PBBAs) simply cannot deliver strong read performance. While they can write data quickly to disk and deduplicate it to optimize for capacity, these same characteristics inherently limit them from restoring large-scale data sets quickly, which is the name of the game in the ransomware era. PBBAs are a bit like Hotel California in that way: easy to get data in for a backup, but hard to get it out for a restore.",{"data":17741,"content":17742,"nodeType":1195},{},[17743,17747,17755],{"data":17744,"marks":17745,"value":17746,"nodeType":1194},{},[],"In fact, the smaller the system block size (Dell EMC PowerProtect\u002FData Domain has variable block lengths between 4kb and 8kb, for example) the more individual blocks that must be rehydrated via random IO when restoring data. Disk doesn’t do random IO well, thanks to time-consuming disk head movement. (Read more about the “rehydration tax” in conventional storage systems in our ",{"data":17748,"content":17750,"nodeType":1814},{"uri":17749},"..\u002F..\u002Fresources\u002Fforms\u002Fwhite-papers\u002Fall-data-reduction-is-not-created-equal",[17751],{"data":17752,"marks":17753,"value":17754,"nodeType":1194},{},[],"data reduction white paper",{"data":17756,"marks":17757,"value":17758,"nodeType":1194},{},[],".) And when you consider that a large dataset was 500GB when PBBAs came to market in the early 2000s, it’s no wonder that today’s backups weigh heavily upon the limitations of the architecture. The result is that large, full system recoveries via legacy backup appliances can last weeks.",{"data":17760,"content":17761,"nodeType":1195},{},[17762,17766,17771,17775,17782],{"data":17763,"marks":17764,"value":17765,"nodeType":1194},{},[],"In fact, let’s consider it a modern data protection truth: ",{"data":17767,"marks":17768,"value":17770,"nodeType":1194},{},[17769],{"type":12758},"deduplication on hard drives will never work for fast restores",{"data":17772,"marks":17773,"value":17774,"nodeType":1194},{},[],". It’s for this reason that Data Domain and other legacy PBBAs never discuss restore performance in their spec sheets, leaving customers to discover their recovery limitations at the worst possible times. Backup throughput and fitting your backup jobs into tight windows is important, but as my colleague ",{"data":17776,"content":17778,"nodeType":1814},{"uri":17777},"..\u002F..\u002Fresources\u002Fvideos\u002Fhow-to-enable-99-9999-uptime-across-100s-of-petabytes-vast-forward-ep-4",[17779],{"data":17780,"marks":17781,"value":16985,"nodeType":1194},{},[],{"data":17783,"marks":17784,"value":17785,"nodeType":1194},{},[]," likes to say, you only back up to restore.",{"data":17787,"content":17788,"nodeType":1195},{},[17789],{"data":17790,"marks":17791,"value":17792,"nodeType":1194},{},[],"Therefore, it’s imperative to ensure that your backup storage has the performance to meet your RTO needs. For many organizations, this means a backup target built on all-flash storage while leveraging global data algorithms that optimize cost without impacting performance.",{"data":17794,"content":17795,"nodeType":17046},{},[17796],{"data":17797,"marks":17798,"value":17799,"nodeType":1194},{},[],"All-Flash Restores. Archive Economics.",{"data":17801,"content":17802,"nodeType":1195},{},[17803],{"data":17804,"marks":17805,"value":17806,"nodeType":1194},{},[],"I joined VAST to lead our data protection division and help customers understand how they could use an all-flash storage system for all of their data – backups and archives included.",{"data":17808,"content":17809,"nodeType":1195},{},[17810,17814,17822],{"data":17811,"marks":17812,"value":17813,"nodeType":1194},{},[],"VAST has flipped the script on backup target restore performance: the fundamental differences in our ",{"data":17815,"content":17817,"nodeType":1814},{"uri":17816},"..\u002F..\u002Fdisaggregated-shared-everything-architecture-dase",[17818],{"data":17819,"marks":17820,"value":17821,"nodeType":1194},{},[],"DASE",{"data":17823,"marks":17824,"value":17825,"nodeType":1194},{},[]," architecture allows our system to read data 8x faster than we write it. And so whereas legacy PBBAs like Dell PowerProtect\u002FData Domain are almost always where restores bottleneck, the VAST Data Platform is the fastest part of the restore function.",{"data":17827,"content":17828,"nodeType":1195},{},[17829],{"data":17830,"marks":17831,"value":17832,"nodeType":1194},{},[],"But performance is just one part of the equation. With VAST customers enjoy an exabyte-scale platform with a single global data reduction pool, rather than having multiple dedupe pools with the same data on each pool.",{"data":17834,"content":17835,"nodeType":1195},{},[17836],{"data":17837,"marks":17838,"value":17839,"nodeType":1194},{},[],"Moreover, our architecture and data algorithms enable unprecedented levels of flash affordability and system longevity that eliminate the complexity of buying, deploying, and managing backup infrastructure. No longer do customers have to balance cost versus restore performance or worry about capacity and scale.",{"data":17841,"content":17842,"nodeType":1195},{},[17843,17847,17854],{"data":17844,"marks":17845,"value":17846,"nodeType":1194},{},[],"Don’t wait to get protected from the scourge of ransomware. Learn more about how VAST Data can help you protect your environment and meet your goals by scheduling a demo, or reaching out to ",{"data":17848,"content":17849,"nodeType":1814},{"uri":776},[17850],{"data":17851,"marks":17852,"value":17853,"nodeType":1194},{},[],"hello@vastdata.com",{"data":17855,"marks":17856,"value":17857,"nodeType":1194},{},[]," to learn 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