http://muratbuffalo.blogspot.com/2026/01/cloudspecs-cloud-hardware-evolution.html Skip to main content Search This Blog [ ] [Search] Metadata On distributed systems broadly defined and other curiosities. The opinions on this site are my own. Cloudspecs: Cloud Hardware Evolution Through the Looking Glass * Get link * Facebook * X * Pinterest * Email * Other Apps - January 09, 2026 This paper (CIDR'26) presents a comprehensive analysis of cloud hardware trends from 2015 to 2025, focusing on AWS and comparing it with other clouds and on-premise hardware. TL;DR: While network bandwidth per dollar improved by one order of magnitude (10x), CPU and DRAM gains (again in performance per dollar terms) have been much more modest. Most surprisingly, NVMe storage performance in the cloud has stagnated since 2016. Check out the NVMe SSD discussion below for data on this anomaly. CPU Trends Multi-core parallelism has skyrocketed in the cloud. Maximum core counts have increased by an order of magnitude over the last decade. The largest AWS instance u7in now boasts 448 cores. However, simply adding cores hasn't translated linearly into value. To measure real evolution, the authors normalized benchmarks (SPECint, TPC-H, TPC-C) by instance cost. SPECint benchmarking shows that cost-performance improved roughly 3x over ten years. A huge chunk of that gain comes from AWS Graviton. Without Graviton, the gain drops to roughly 2x. For in-memory database benchmarks, gains were even lower (2x-2.5x), likely due to memory and cache latency bottlenecks. [AVvXsEhjspXxlUb6cWW1Co2aJyK-hX67_A10kqDPYggxhFSxpW3sncZ1SgKYlJtECVe1Ys1jC6igt4Zs7S9oUFMTRE] On-prem hardware comparison shows that this stagnation is not cloud price gouging. Historically, Moore's Law and Dennard scaling doubled cost-performance every two years (which would have sum up to 32x gain over a decade). However, an analysis of on-premise AMD server CPUs reveals a similar slump, only a 1.7x gain from 2017 to 2025. Memory Trends DRAM capacity per dollar has effectively flatlined. The only significant improvement was the 2016 introduction of memory-optimized x instances, which offered ~3.3x more GiB-hours/$ than compute-optimized peers. While absolute single-socket bandwidth jumped ~5x (93 GiB/s to 492 GiB/s) as servers moved from DDR3 to DDR5, the cost-normalized gain is only 2x. [AVvXsEglupuA_e4V9Ag1BCCyey3vv4Z6B3f9n7L3k6ckfCq7ZwFy] Historical data suggests commodity DRAM prices dropped 3x over the decade. But in the last three months, due to AI-driven demand, DDR5 prices rose sharply, further limiting effective memory gains. Network Trends We have good news here, finally. Network bandwidth per dollar exploded by 10x. And absolute speeds went from 10 Gbit/s to 600 Gbit/ s (60x). [AVvXsEgjHrRaOUx_cB_Np3Pb4JH1C1PmvxCbO0_CuTfqgVJvOhMOi6H8] These gains were not universal though. Generic instances saw little change. The gains were driven by network-optimized n instances (starting with the c5n in 2018) powered by proprietary Nitro cards. NVMe Trends NVMe SSDs are the biggest surprise. Unlike CPUs and memory, where cloud trends mirror on-prem hardware, NVMe performance in AWS has largely stagnated. The first NVMe-backed instance family, i3, appeared in 2016. As of 2025, AWS offers 36 NVMe instance families. Yet the i3 still delivers the best I/O performance per dollar by nearly 2x. [AVvXsEic_wuVSU_xjh28SKjlfNj9VBeIfICgsx4lJa1pAaz0N_4YF8Sc] SSD capacity has stagnated since 2019 and I/O throughput since 2016. This sharply contrasts with on-prem hardware, where SSD performance doubled twice (PCIe 4 and PCIe 5) in the same timeframe. The gap between cloud and on-premise NVMe is widening rapidly. [AVvXsEhxMvBHbEny6EVqv9oNhdVBFsq0_fHv_wb7bXujavo3w3ZyNrw9] This price/performance gap likely explains the accelerating push toward disaggregated storage. When local NVMe is expensive and underperforming, remote storage starts to look attractive. The paper speculates that with network speeds exploding and NVMe stagnating, architectures may shift further. For systems like Snowflake, using local NVMe for caching might no longer be worth the complexity compared to reading directly from S3 with fast networks. Discussion I think the main takeaway is that uniform hardware scaling in the cloud is over. Moore's Law no longer lifts all boats. Performance gains now come from specialization, especially networking (e.g., Graviton, Nitro, Accelerators). In my HPTS 2024 review, I noted that contrary to the deafening AI hype, the real excitement in the hallways was about hardware/software codesign. This paper validates that sentiment. With general-purpose CPU and memory cost-performance stagnating, future databases must be tightly integrated with specialized hardware and software capabilities to provide value. I think the findings here will refuel that trend. A key open question is why massive core counts deliver so little value. Where is the performance lost? Possible explanations include memory bandwidth limits, poor core-to-memory balance, or configuration mismatches. But I think the most likely culprit is software. Parallel programming remains hard, synchronization is expensive, and many systems fail to scale beyond a modest number of cores. We may be leaving significant performance on the table simply because our software cannot effectively utilize the massive parallelism now available. The paper comes with an interactive tool, Cloudspecs, built on DuckDB-WASM (yay!). This allows you to run SQL queries over the dataset directly in the browser to visualize these trends. The figures in the PDF actually contain clickable link symbols that take you to the specific query used to generate that chart. Awesome reproducibility! Aleksey and I did a live-reading of the paper. As usual, we had a lot to argue about. I'll add a recording of our discussion on YouTube when it becomes available, and here is a link to my annotated paper. big-data cloud computing datacenter networking * Get link * Facebook * X * Pinterest * Email * Other Apps Comments Post a Comment Popular posts from this blog Hints for Distributed Systems Design - October 02, 2023 This is with apologies to Butler Lampson, who published the " Hints for computer system design " paper 40 years ago in SOSP'83. I don't claim to match that work of course. I just thought I could draft this post to organize my thinking about designing distributed systems and get feedback from others. I start with the same disclaimer Lampson gave. These hints are not novel, not foolproof recipes, not laws of design, not precisely formulated, and not always appropriate. They are just hints. They are context dependent, and some of them may be controversial. That being said, I have seen these hints successfully applied in distributed systems design throughout my 25 years in the field, starting from the theory of distributed systems (98-01), immersing into the practice of wireless sensor networks (01-11), and working on cloud computing systems both in the academia and industry ever since. These heuristic principles have been applied knowingly or unknowingly and has proven... Read more >> My Time at MIT - February 16, 2025 Image Twenty years ago, in 2004-2005, I spent a year at MIT's Computer Science department as a postdoc working with Professor Nancy Lynch. It was an extraordinary experience. Life at MIT felt like paradise, and leaving felt like being cast out. MIT Culture MIT's Stata Center was the best CS building in the world at the time. Designed by Frank Gehry, it was a striking abstract architecture masterpiece ( although like all abstractions it was a bit leaky ). Furniture from Herman Miller complemented this design. I remember seeing price tags of $400 on simple yellow chairs. The building buzzed with activity. Every two weeks, postdocs were invited to the faculty lunch on Thursdays, and alternating weeks we had group lunches. Free food seemed to materialize somewhere in the building almost daily, and the food trucks outside were also good. MIT thrived on constant research discussions, collaborations, and talks. Research talks were advertised on posters at the urinals, as a practical touch of M... Read more >> TLA+ modeling tips - December 15, 2025 Model minimalistically Start from a tiny core, and always keep a working model as you extend. Your default should be omission. Add a component only when you can explain why leaving it out would not work. Most models are about a slice of behavior, not the whole system in full glory: E.g., Leader election, repair, reconfiguration. Cut entire layers and components if they do not affect that slice. Abstraction is the art of knowing what to cut . Deleting should spark joy. Model specification, not implementation Write declaratively. State what must hold, not how it is achieved. If your spec mirrors control flow, loops, or helper functions, you are simulating code. Cut it out. Every variable must earn its keep. Extra variables multiply the state space (model checking time) and hide bugs. Ask yourself repeatedly: can I derive this instead of storing it? For example, you do not need to maintain a WholeSet variable if you can define it as a state function of existing variables: WholeSet =... Read more >> Foundational distributed systems papers - February 27, 2021 I talked about the importance of reading foundational papers last week. To followup, here is my compilation of foundational papers in the distributed systems area. (I focused on the core distributed systems area, and did not cover networking, security, distributed ledgers, verification work etc. I even left out distributed transactions, I hope to cover them at a later date.) I classified the papers by subject, and listed them in chronological order. I also listed expository papers and blog posts at the end of each section. Time and State in Distributed Systems Time, Clocks, and the Ordering of Events in a Distributed System. Leslie Lamport, Commn. of the ACM, 1978. Distributed Snapshots: Determining Global States of a Distributed System. K. Mani Chandy Leslie Lamport, ACM Transactions on Computer Systems, 1985. Virtual Time and Global States of Distributed Systems. Mattern, F. 1988. Practical uses of synchronized clocks in distributed systems. B. Liskov, 1991. Exp... Read more >> Learning about distributed systems: where to start? - June 10, 2020 This is definitely not a "learn distributed systems in 21 days" post. I recommend a principled, from the foundations-up, studying of distributed systems, which will take a good three months in the first pass, and many more months to build competence after that. If you are practical and coding oriented you may not like my advice much. You may object saying, "Shouldn't I learn distributed systems with coding and hands on? Why can I not get started by deploying a Hadoop cluster, or studying the Raft code." I think that is the wrong way to go about learning distributed systems, because seeing similar code and programming language constructs will make you think this is familiar territory, and will give you a false sense of security. But, nothing can be further from the truth. Distributed systems need radically different software than centralized systems do. --A. Tannenbaum This quotation is literally the first sentence in my distributed systems syllabus. Inst... Read more >> Optimize for momentum - December 02, 2025 Progress comes from motion. Momentum is the invisible engine of any significant work. A project feels daunting when you face it as a blank page. It feels easier when you built some momentum with some next steps. So, momentum makes the difference between blocked and flowing. Think of a stalled truck on a desert road. You can't lift it with superhuman strength. But by rocking it with small periodic forces at the right rhythm (matching its natural frequency) you can get it rolling. Each tiny push adds to the previous one because the timing aligns with the system's response. The truck starts to move, and then the engine catches. Projects behave the same way. A big project has its own rhythm. If you revisit it daily, even briefly, your pushes line up. Your brain stays warm. Context stays loaded . Ideas from yesterday are still alive today. Each session amplifies the last because you are operating in phase with your own momentum. When you produce something every day, you never feel... Read more >> Advice to the young - July 30, 2024 Image I notice I haven't written any advice posts recently. Here is a collection of my advice posts pre 2020. I've been feeling all this elderly wisdom pent up in me, ready to pour at any moment. So here it goes. Get ready to quench your thirst from my fount of wisdom. No man, think for yourself, only get what works for you. It is called foundations, not theory Foundations of computer science (or rather any field of study) are the most important topics you can learn. These lay down the frame of thinking/perspective for that area of study. Yet, I am saddened to hear these called as "theory", and labeled as "unpractical". This couldn't be farther from the truth. Take a look at how I recommend studying distributed systems . Don't you dare call this "theory" and "unpractical". This lays the bedrock that you build your practice on. Don't skimp on the foundations. Don't build your home on quicksand. Keep your hands dirty, your mind cl... Read more >> The Agentic Self: Parallels Between AI and Self-Improvement - January 02, 2026 2025 was the year of the agent. The goalposts for AGI shifted; we stopped asking AI to merely "talk" and demanded that it "act". As an outsider looking at the architecture of these new agents and agentic system, I noticed something strange. The engineering tricks used to make AI smarter felt oddly familiar. They read less like computer science and more like ... self-help advice . The secret to agentic intelligence seems to lie in three very human habits: writing things down, talking to yourself, and pretending to be someone else. They are almost too simple. The Unreasonable Effectiveness of Writing One of the most profound pieces of advice I ever read as a PhD student came from Prof. Manuel Blum, a Turing Award winner. In his essay "Advice to a Beginning Graduate Student", he wrote: "Without writing, you are reduced to a finite automaton. With writing you have the extraordinary power of a Turing machine." If you try to hold a complex argument enti... Read more >> Scalable OLTP in the Cloud: What's the BIG DEAL? - January 17, 2024 Image This paper is from Pat Helland, the apostate philosopher of database systems, overall a superb person, and a good friend of mine. The paper appeared this week at CIDR'24. (Check out the program for other interesting papers). The motivating question behind this work is: " What are the asymptotic limits to scale for cloud OLTP (OnLine Transaction Processing) systems? " Pat says that the CIDR 2023 paper "Is Scalable OLTP in the Cloud a Solved Problem?" prompted this question. The answer to the question? Pat says that the answer lies in the joint responsibility of database and the application. If you know of Pat's work, which I have summarized several in this blog , you would know that Pat has been advocating along these lines before. But this paper provides a very crisp, specific, concrete answer. Read on for my summary of the paper. Disclaimer: This is a wisdom and technical information/detail packed 13-page paper, so I will try my best to summarize the sa... Read more >> Disaggregated Database Management Systems - November 11, 2025 Image This paper is based on a panel discussion from the TPC Technology Conference 2022. It surveys how cloud hardware and software trends are reshaping database system architecture around the idea of disaggregation. For me, the core action is in Section 4: Disaggregated Database Management Systems. Here the paper discusses three case studies (Google AlloyDB, Rockset, and Nova-LSM) to give a taste of the software side of the movement. Of course there are many more. You can find Aurora , Socrates , and Taurus , and TaurusMM reviews in my blog. In addition, Amazon DSQL (which I worked on) is worth discussing soon. I'll also revisit the PolarDB series of papers , which trace a fascinating arc from active log-replay storage toward simpler, compute-driven designs. Alibaba has been prolific in this space, but the direction they are ultimately advocating remains muddled across publications, which reflect conflicting goals/ priorities. AlloyDB AlloyDB extends PostgreSQL with compute-storage disagg... Read more >> Powered by Blogger Theme images by Michael Elkan Murat Demirbas My photo Murat I am a principal research scientist at MongoDB Research. Ex-AWS. Ex-professor at SUNY Buffalo. I work on distributed systems, databases, cloud computing, formal methods, and neurosymbolic AI. You can follow me on Mastodon or Twitter. Visit profile Pageviews Recent Posts * January5 * December7 * November5 * October4 * September6 * August8 * July7 * June2 * May8 * April2 * March3 * February5 * January5 * December8 * November5 * October8 * September10 * August9 * July3 * June5 * May2 * April6 * March6 * February9 * January7 * December3 * November4 * October4 * September4 * August4 * July5 * June2 * May3 * April3 * March3 * February5 * January3 * December4 * November4 * October1 * September4 * August5 * July6 * June3 * May3 * April4 * March3 * February3 * January3 * December5 * November3 * October6 * September1 * August4 * July2 * June12 * May1 * April1 * March4 * February4 * January4 * December3 * November7 * October4 * September1 * August3 * July6 * June11 * May9 * April8 * March8 * February7 * January9 * December10 * November14 * October6 * September13 * July3 * June3 * May4 * April6 * March2 * February1 * January3 * December4 * November7 * October2 * September2 * August8 * July2 * June4 * May9 * April6 * March9 * February5 * January13 * December15 * November15 * October5 * September8 * August10 * July3 * June3 * May3 * April4 * February4 * January7 * December7 * November9 * October3 * September1 * July4 * June5 * May1 * April4 * March2 * February2 * January4 * December3 * November2 * October3 * September2 * August3 * June1 * May1 * April6 * March6 * February4 * January3 * November4 * October4 * September6 * August2 * July2 * June3 * March3 * February4 * January1 * December1 * November2 * August2 * July4 * June2 * May5 * April8 * January1 * December1 * November7 * October1 * September2 * August1 * May2 * March1 * February1 * January2 * December3 * September5 * June1 * May5 * April5 * March5 * February9 * January5 * December6 * November9 * October9 * September7 * August1 Show more Show less Topics 2PC2 abstraction8 agentic1 AI11 analytics3 atomic storage2 auditability5 automated reasoning14 aws5 Azure11 benchmarks4 bestof9 big-data32 Blockchain39 book-review55 bugs1 calm5 chaos2 cloud computing19 ConcurrencyControlBook7 consistency33 Cosmos DB11 CosmosDB12 crdts2 data warehouse2 databases63 datacenter networking3 dataflow11 dbos1 DDIA15 disaggregation8 distributed consensus53 distributed transactions41 distSQL9 facebook16 failures18 fault-tolerance48 formal methods15 graph-processing1 hpts3 htap3 humans10 indexing3 isolation levels5 linearizability1 links2 liveDiscussion8 mad-questions42 main-memory1 measuring1 metastability 3 microservices2 misc123 ML1 mlbegin7 mldl26 mobile2 mongodb9 MVCC4 my advice23 my-paper11 networking1 newsql3 NoSQL2 OLAP2 OLTP7 paper-review151 paxos52 postgres1 presenting4 privacy1 programming7 query-processing2 raft4 RDMA3 reading-group23 recent-reads2 reconfiguration4 research-advice55 research-question45 Rust3 scheduling4 security1 seminar9 serializability3 serverless1 smartphones2 snapshot isolation7 sonification1 SQL6 stabilization6 statistics3 stream-processing12 teaching33 tensorflow11 testing1 time 22 time synchronization7 timeDB8 tla58 tpbook1 transactions41 trip-report36 wpaxos6 writing30 Show more Show less