I'm actually confused about what ClusterdOS is and does besides glue a bunch of projects together in an opinionated way.
It sits on top of Kubernetes and seems very hand wavy about how you create and manage those clusters.
As far as I can tell and from some quick researching of the guys previous experience, that's all it is. I think the implication is that LLM's will be architecting and deploying the cluster setups at some point? Which sounds horrific so I'm assuming I am interpreting it long
The article itself reminds me of the enthusiasm I felt for plan9 when I first heard about it back in uni. I also thought everyone should have their own compute grids and that clustered computing was the future; of course now I realize there's a lot of reasons why that doesn't actually work. Considering this appears to be a start-up ad, I hope the author knows something I don't.
The article assumes there are people who want clusters. But a single Linux VM in the cloud can scale pretty far. Separate VM's for different apps works well for isolation. Why do I need a cluster?
Never understood the appeal of Kubernetes to developers, outside of a massive deployments. Always felt like a poor man's Linux for those that insist on using apple or windows desktop.
> Why do I need a cluster?
Uptime, self healing, reproducibility, separating the system from app. There's probably a half dozen more.
K8s comes with resource consumption tax certainly but for anything beyond the trivial it's usually justified.
> Separate VM's for different apps works well for isolation
Sounds inefficient along with a lot more work doing the plumbing than simply writing a 100 lines of yaml.
[deleted]
Isn't there a meaningful sense in which "separate VMs for different apps" constitutes a cluster?
The "cooperative task" they're engaged in is just, broadly, meeting your needs, whatever they are.
The isolation is a desirable property, and I agree this is much preferable to a highly inter-coupled bunch of machines, and also that thia stretches the typical sense in which we refer to a "compute cluster", but I don't think it's an entirely invalid framing of the term.
> Isn't there a meaningful sense in which "separate VMs for different apps" constitutes a cluster?
Not really. In my experience clustering implies multiple compute elements serving the same function with a coordination mechanism to provide redundancy and/or enhanced capacity.
JBOD vs. RAID.
Yeah I’ve been doing this with tailscale and a single vps and it’s been wonderful. Unless you’re planning to have millions of users I don’t think there’s any reason to have a cluster.
Maybe they’re assuming some massive amount of compute will be necessary for future tasks? Self hosted LLMs? I’m currently finding it difficult to come up with more uses for my vps beyond hosting trillium and some personal applications I’ve made
MPI is kind of fun to write.
Wouldn’t it be cheaper / less complex to scale vertically (eg a large workstation or medium size bare metal server) instead of using clusters? My understanding is that clusters are primarily useful when you want to share a resource from a pool across unpredictable usage, which becomes a moot point once the cluster is personal.
You're drawing an incorrect conclusion from that site. Aside from the fact that "fitting in RAM" is not the only criterion for needing a cluster, the fact that it's possible to fit data into RAM on a single machine doesn't mean that's the most cost-effective, practical, or sensible solution.
A big advantage of clusters, and horizontal scaling in general, is the ability to easily dynamically scale to meet demand.
If you're running a system on a single machine that has N GB of memory and you need to scale to N+1, what do you do? Provision a new machine and migrate everything over?
No-one operates online real-time systems like this. Clusters make it much easier and less expensive to handle this.
On top of that, it's probably true that in some pure numerical problem-count sense, "most problems" don't need a cluster, but that's misleading. It's like saying "most businesses are mom-and-pop shops." Perhaps true, but it ignores hundreds of thousands of larger businesses, or even small business that have big data needs.
There are plenty of problems that involve large amounts of data, and that's increasingly true with ML applications.
I'm at a company of ~100 people which you've probably never heard of (classified as a "small" company in government stats, so not included in the hundreds of thousands figure I mentioned above.) We have 1.9 PB of data for our main environment. When we run processes that deal with it all, the clusters scale to thousands of vCPUs and tens of terabytes of RAM.
Several processes that run daily scale to 500+ vCPUs and many TB of RAM. For the latter, the data itself could probably fit in RAM on a humongous machine, but the CPUs wouldn't fit on a single machine. And we'd have to size the machines carefully every time we start them up. Clusters can scale up dynamically according to the demands of the jobs they're executing.
Most data problems don't need to fit in RAM.
No idea about ClusterOS, but I would recommend IncusOS if you're looking for a nice clustering solution. Incus has become indispensable in my homelab over the past few months. It's what I put on my bare metal machines and then spin up Talos Linux VMs for day job practice.
I really liked IncusOS but it still felt quite primitive compared to Proxmox. I also didn’t really like the way it bundles VMs and containers into an ‘instance’ concept, it made the UI and management via Terraform confusing. Had a lot of problems with the TF provider too.
How does the IncusOS API compare to Talos? When I first looked at it it seemed very minimal and I didn't see a lot of options for more complex installs (eg network bonding, disk partitioning).
The best part of this article is in the footnotes:
> see CEO of Tailscale apenwarr's vibe-researched thread
“Vibe-research” is now a core part of my vocabulary.
I’m not sure quite what this is trying to say. My laptop is already a personal cluster — it has 16 cores, lots of storage, a fast network, I run VMs on it. It’s been the case for a long time that you can run bursty jobs in the cloud if you need more power for a brief period than whatever is currently locally affordable. That’s kind of what the cloud is for, really. So what’s new?
It’s pretty fun to throw a thousand cores at a problem, but I guess it won’t be that long before you can get that in two-socket AMD workstation or whatever.
Imagine a Beowulf cluster of these!
I don't see how an operating system can work for a cluster.
You can have more than one CPU and more than one storage connected to one mainboard and that works because the interconnect fabric is very fast.
We don't have have the possibility to connect different computers at the same kind of speed that would let them work together seamlessly.
Check out Plan 9 and Mosix. They weren't super fast but they worked.
Buddy 90% of people can’t even open a word document without immense stress
I have an irrational soft spot for Apache Mesos. I loved the separation of the resource management from the scheduling. Note to self: do not rabbit hole on this. Hm. Maybe mesos is the manager for my agent sandboxes. No! Bad lowbloodsugar!
I'm actually confused about what ClusterdOS is and does besides glue a bunch of projects together in an opinionated way.
It sits on top of Kubernetes and seems very hand wavy about how you create and manage those clusters.
As far as I can tell and from some quick researching of the guys previous experience, that's all it is. I think the implication is that LLM's will be architecting and deploying the cluster setups at some point? Which sounds horrific so I'm assuming I am interpreting it long
The article itself reminds me of the enthusiasm I felt for plan9 when I first heard about it back in uni. I also thought everyone should have their own compute grids and that clustered computing was the future; of course now I realize there's a lot of reasons why that doesn't actually work. Considering this appears to be a start-up ad, I hope the author knows something I don't.
The article assumes there are people who want clusters. But a single Linux VM in the cloud can scale pretty far. Separate VM's for different apps works well for isolation. Why do I need a cluster?
Never understood the appeal of Kubernetes to developers, outside of a massive deployments. Always felt like a poor man's Linux for those that insist on using apple or windows desktop.
> Why do I need a cluster?
Uptime, self healing, reproducibility, separating the system from app. There's probably a half dozen more.
K8s comes with resource consumption tax certainly but for anything beyond the trivial it's usually justified.
> Separate VM's for different apps works well for isolation
Sounds inefficient along with a lot more work doing the plumbing than simply writing a 100 lines of yaml.
Isn't there a meaningful sense in which "separate VMs for different apps" constitutes a cluster?
The "cooperative task" they're engaged in is just, broadly, meeting your needs, whatever they are.
The isolation is a desirable property, and I agree this is much preferable to a highly inter-coupled bunch of machines, and also that thia stretches the typical sense in which we refer to a "compute cluster", but I don't think it's an entirely invalid framing of the term.
> Isn't there a meaningful sense in which "separate VMs for different apps" constitutes a cluster?
Not really. In my experience clustering implies multiple compute elements serving the same function with a coordination mechanism to provide redundancy and/or enhanced capacity.
JBOD vs. RAID.
Yeah I’ve been doing this with tailscale and a single vps and it’s been wonderful. Unless you’re planning to have millions of users I don’t think there’s any reason to have a cluster.
Maybe they’re assuming some massive amount of compute will be necessary for future tasks? Self hosted LLMs? I’m currently finding it difficult to come up with more uses for my vps beyond hosting trillium and some personal applications I’ve made
MPI is kind of fun to write.
Wouldn’t it be cheaper / less complex to scale vertically (eg a large workstation or medium size bare metal server) instead of using clusters? My understanding is that clusters are primarily useful when you want to share a resource from a pool across unpredictable usage, which becomes a moot point once the cluster is personal.
Clusters are almost never the right answer for most problems: https://yourdatafitsinram.net/
You're drawing an incorrect conclusion from that site. Aside from the fact that "fitting in RAM" is not the only criterion for needing a cluster, the fact that it's possible to fit data into RAM on a single machine doesn't mean that's the most cost-effective, practical, or sensible solution.
A big advantage of clusters, and horizontal scaling in general, is the ability to easily dynamically scale to meet demand.
If you're running a system on a single machine that has N GB of memory and you need to scale to N+1, what do you do? Provision a new machine and migrate everything over?
No-one operates online real-time systems like this. Clusters make it much easier and less expensive to handle this.
On top of that, it's probably true that in some pure numerical problem-count sense, "most problems" don't need a cluster, but that's misleading. It's like saying "most businesses are mom-and-pop shops." Perhaps true, but it ignores hundreds of thousands of larger businesses, or even small business that have big data needs.
There are plenty of problems that involve large amounts of data, and that's increasingly true with ML applications.
I'm at a company of ~100 people which you've probably never heard of (classified as a "small" company in government stats, so not included in the hundreds of thousands figure I mentioned above.) We have 1.9 PB of data for our main environment. When we run processes that deal with it all, the clusters scale to thousands of vCPUs and tens of terabytes of RAM.
Several processes that run daily scale to 500+ vCPUs and many TB of RAM. For the latter, the data itself could probably fit in RAM on a humongous machine, but the CPUs wouldn't fit on a single machine. And we'd have to size the machines carefully every time we start them up. Clusters can scale up dynamically according to the demands of the jobs they're executing.
Most data problems don't need to fit in RAM.
No idea about ClusterOS, but I would recommend IncusOS if you're looking for a nice clustering solution. Incus has become indispensable in my homelab over the past few months. It's what I put on my bare metal machines and then spin up Talos Linux VMs for day job practice.
I really liked IncusOS but it still felt quite primitive compared to Proxmox. I also didn’t really like the way it bundles VMs and containers into an ‘instance’ concept, it made the UI and management via Terraform confusing. Had a lot of problems with the TF provider too.
How does the IncusOS API compare to Talos? When I first looked at it it seemed very minimal and I didn't see a lot of options for more complex installs (eg network bonding, disk partitioning).
The best part of this article is in the footnotes:
> see CEO of Tailscale apenwarr's vibe-researched thread
“Vibe-research” is now a core part of my vocabulary.
I’m not sure quite what this is trying to say. My laptop is already a personal cluster — it has 16 cores, lots of storage, a fast network, I run VMs on it. It’s been the case for a long time that you can run bursty jobs in the cloud if you need more power for a brief period than whatever is currently locally affordable. That’s kind of what the cloud is for, really. So what’s new?
It’s pretty fun to throw a thousand cores at a problem, but I guess it won’t be that long before you can get that in two-socket AMD workstation or whatever.
Imagine a Beowulf cluster of these!
I don't see how an operating system can work for a cluster.
You can have more than one CPU and more than one storage connected to one mainboard and that works because the interconnect fabric is very fast.
We don't have have the possibility to connect different computers at the same kind of speed that would let them work together seamlessly.
Check out Plan 9 and Mosix. They weren't super fast but they worked.
Buddy 90% of people can’t even open a word document without immense stress
I have an irrational soft spot for Apache Mesos. I loved the separation of the resource management from the scheduling. Note to self: do not rabbit hole on this. Hm. Maybe mesos is the manager for my agent sandboxes. No! Bad lowbloodsugar!