I was wondering if there were any best practices for restricting the amount of resources available to Mesos. Specifically, CPU resources. We have a busy mesos cluster running CPU intensive tasks within docker containers. Is it a good idea to reserve one or two cores for non-mesos tasks? Or is allowing mesos to offer all of the available CPUs on the machine just fine?
A link to some best practices regarding resources would be great as well. The almighty Google wasn't very productive. I'd just like some advice and hopefully evidence that this is a good idea before suggesting we move forward down this path.
Thanks!
Related
we are looking to build a cluster of Compute Nodes for Deep Learning model training jobs, some of them on the cloud and others locally, that have NVIDIA GPUs in them. We felt that using Mesos and the framework Marathon (M&M) would be our best options to schedule the cluster. However the documentations for (M&M) seem to be very ambiguous (or at least to me, sorry I'm an intern) and I'm running into a lot of issues concerning Zookeeper and the connections between the nodes.
Plus, it seems like Mesosphere are giving much more importance to DC/OS when it comes to tutorials and docs, and I guess it will also be patched more regularly and its interfaces (GUI and CLI) look much more user-friendly.
So I was wondering if by dropping the exploration of (M&M) and moving to DC/OS, would we lose a lot of control over the cluster? In M&M do we have perks that cannot be given in the Open Source Edition of DC/OS? like monitoring the machines, logging results etc.. If I ask my manager we might also get the Enterprise edition so that's not really a problem, but does DC/OS apply an abstraction layer that isn't really preferable to advanced users?
DC/OS is build around Apache Mesos and Marathon and gives a good default setup for zookeeper, networking, .... So IMO it is a good place to start as you can still use all M&M and Mesos features + the DC/OS features and ease of setup.
Disclaimer: I am working for Mesosphere.
Please explain me how containers are sharing hardware node resources between each other?
I feel my node is lack of CPU resources.
Even though I set maximum for cloudlets limit.
I have no real load on my node, but last month it very often stops responding for short periods and then restores with no actual reasons in logs.
I also feel my provider has poor experience with Jelastic administration.
Instead of looking for real reason why hardware node is overloaded, they just turn it off for a while and then turn it on.
See my screenshots. Zero CPU usage Zero CPU usage
Is it possible for hosting provider to "oversell" hardware node?
I am looking for support from Jelastic team here.
I know that you requested a reply from Jelastic in particular, but I suppose that it might help to get some insight from a hosting provider as well.
Is it possible for hosting provider to "oversell" hardware node?
The Jelastic platform itself does not have any limitations on this. The platform ensures that containers are distributed to the least loaded hardware nodes, but obviously if a hosting provider does not supply sufficient infrastructure / keep adding more, that distribution is worthless (i.e. all hardware is overloaded).
I feel my node is lack of CPU resources.
From those graphs it looks like you're hitting approx. 2GHz CPU, which for a LAMP application (right?) seems to be quite high. Are you sure that your bottleneck is CPU? If yes, how did you reach that conclusion / test that assertion?
I also feel my provider has poor experience with Jelastic administration.
Most of all, if you feel that your current Jelastic provider is not servicing your needs, did you consider moving to another one? The Jelastic ecosystem has over 30 different hosting providers. You can move your environment to another provider easily with the Export/Import feature and the ratings on the Jelastic Cloud Union site can help you to identify a good quality one in your preferred location.
I am checking out Apache Aurora with the scope of running scientific workflows (assuming a set of python scripts in a particular sequence). I've successfully managed to run a few of these aurora Jobs, and it looks great for my particular use-case.
I was wondering if there is a way to specify that a particular task (or job, in general) requires a number of GPU resources from my Apache Mesos cluster Of course Mesos needs to be aware of the GPU resources first, and it seems this is possible by defining these GPU resources as indicated here.
So the question is whether there is a way to communicate with Mesos via Aurora to accept offers with GPU resources available. As far as I can tell, the Resource object in Aurora is limited to CPU/Ram/Disk resources. Any hints are greatly appreciated.
Thanks!
I'm not familiar with Apache Aurora, but Mesosphere Marathon (a framework similar to Aurora in functionality) is limited to cpu, mem, and disk resources as well.
If you would like to use custom resources, you would probably need to write your own framework. Depending on your needs it may not be that difficult. For inspiration, check the RENDLER framework.
As mentioned in the thread you are referencing to, Mesos do not provide isolation for GPU (actually, for any custom) resources. Keep this is in mind when doing resource math.
When checking the Aurora tutorial I assume you can just specify this ressource as part of you job description:
resources = Resources(cpu = 2, ram = 4*GB, disk = 8*GB, gpu = 1),
Just keep in mind that this is in artificial resource for Mesos, so Mesos will not take care of resource isolation in this case. For example if you have several GPUs on one system, your code would have to manage the isolation/scheduling between the different GPUs.
I'm trying to figure out what would be the reasons for using Mesos. Can you come up with other ones?
Running all of your services in the same cluster instead of dedicated clusters (your end-applications + DevOps such as Jenkins)
Running different maturity applications in same cluster (dev, test, production), or is this viable? Kubernetes has a similar approach with Labels
Mesos simplifies the use of traditional distributed applications such as Hadoop by easing deployment, unified API, bin-packing of resources
Full-disclosure: I currently work at Twitter and I'm involved in both Apache Mesos and Aurora.
Mesos uses cases can vary based upon a few dimensions: scale (10 servers vs 10s of thousands), available hardware (dedicated/static or in the public cloud/scalable), and workloads (primarily services, batch, or both).
Your list is a great start. Here are a few additional use cases / features to add.
Container Orchestration
As container runtimes like Docker have become popular, lots of potential users are looking at Mesos + a scheduler to manage orchestration once container images are created. Mesos is already quite mature and has been proven at scale, which I think has given it a leg up over some emergent solutions.
Increased Resource Utilization
For companies running >50 servers, a common motivation for adopting Mesos is to increase resource utilization to reduce CapEx. There are a number of examples of this in both the public and private cloud. In the case of Ebay they have been running Jenkins on Mesos and were able to reduce their VM footprint. Mesosphere has also published a case study of HubSpot (runnning on AWS), and how they've been able to replace hundreds of smaller servers with dozens of larger ones by more-efficiently using their available hardware.
Preemption
At Twitter we're running Mesos via one scheduler: Apache Aurora. One of the ways we can improve utilization relates to your use case: running different maturity applications in the same cluster. Aurora has a concept of environments, so you can run applications that are production, development, or test. Additionally, Aurora has a built-in preemption feature which allows it to prioritize production over non-production tasks, killing non-production tasks when those resources are needed to run production ones as well as a priority system within each environment.
Long-term, functionality related to preemption will also be located in the Mesos core itself -- it's a killer feature related to both increased resource utilization and running different maturity applications (dev, test, prod). There are a few Mesos tickets to follow if you're interested in keeping up to date, including MESOS-155 for preemption, and MESOS-1474 for inverse offers.
Colocating Batch and Services
Running batch and services in a shared Mesos cluster will be key to driving up utilization even further as js84 points out. Check out Project Myriad, an effort to colocate Mesos and YARN workloads in the same cluster. At this time I'm not aware of any large deployments running both batch and services, but it's certainly the direction the community is moving in as it becomes easier for multiple frameworks to run in a shared cluster.
At least one additional use case comes to mind: Development SDK for developing distributed applications. If you have a look at Mesos Frameworks you will find a number of frameworks which have been developed on top of Mesos. Also interesting Apple's Siri framework powering Siri.
Regarding your 1): One additional angle you should keep in mind here is scaling your applications in the same cluster. I.e. at peak load of your website, shift resources easily towards the webservers while scaling down the Hadoop analytical processing.
I am relatively new to all these, but I'm having troubles getting a clear picture among the listed technologies.
Though, all of these try to solve different problems, but do have things in common too. I would like to understand what are the things that are common and what is different. It is likely that the combination of few would be great fit, if so what are they?
I am listing a few of them along with questions, but it would be great if someone lists all of them in detail and answers the questions.
Kubernetes vs Mesos:
This link
What's the difference between Apache's Mesos and Google's Kubernetes
provides a good insight into the differences, but I'm unable to understand as to why Kubernetes should run on top of Mesos. Is it more to do with coming together of two opensource solutions?
Kubernetes vs Core-OS Fleet:
If I use kubernetes, is fleet required?
How does Docker-Swarm fit into all the above?
Disclosure: I'm a lead engineer on Kubernetes
I think that Mesos and Kubernetes are largely aimed at solving similar problems of running clustered applications, they have different histories and different approaches to solving the problem.
Mesos focuses its energy on very generic scheduling, and plugging in multiple different schedulers. This means that it enables systems like Hadoop and Marathon to co-exist in the same scheduling environment. Mesos is less focused on running containers. Mesos existed prior to widespread interest in containers and has been re-factored in parts to support containers.
In contrast, Kubernetes was designed from the ground up to be an environment for building distributed applications from containers. It includes primitives for replication and service discovery as core primitives, where-as such things are added via frameworks in Mesos. The primary goal of Kubernetes is a system for building, running and managing distributed systems.
Fleet is a lower-level task distributor. It is useful for bootstrapping a cluster system, for example CoreOS uses it to distribute the kubernetes agents and binaries out to the machines in a cluster in order to turn-up a kubernetes cluster. It is not really intended to solve the same distributed application development problems, think of it more like systemd/init.d/upstart for your cluster. It's not required if you run kubernetes, you can use other tools (e.g. Salt, Puppet, Ansible, Chef, ...) to accomplish the same binary distribution.
Swarm is an effort by Docker to extend the existing Docker API to make a cluster of machines look like a single Docker API. Fundamentally, our experience at Google and elsewhere indicates that the node API is insufficient for a cluster API. You can see a bunch of discussion on this here: https://github.com/docker/docker/pull/8859 and here: https://github.com/docker/docker/issues/8781
Join us on IRC # #google-containers if you want to talk more.
I think the simplest answer is that there is no simple answer. The swift rise to power of containers, and Docker in particular has left a power vacuum for "container scheduling and orchestration", whatever that might mean. In reality, that means you have a number of technologies that can work in harmony on some levels, but with certain aspects in competition. For example, Kubernetes can be used as a one stop shop for deploying and managing containers on a compute cluster (as Google originally designed it), but could also sit atop Fleet, making use of the resilience tier that Fleet provides on CoreOS.
As this Google vid states Kubernetes is not a complete out the box container scaling solution, but is a good statement to start from. In the same way, you would at some stage expect Apache Mesos to be able to work with Kubernetes, but not with Marathon, in as much as Marathon appears to fulfil the same role as Kubernetes. Somewhere I think I've read these could become part of the same effort, but I could be wrong about that - it's really about the strategic direction of Mesosphere and the corresponding adoption of Kubernetes principles.
In the DockerCon keynote, Solomon Hykes suggested Swarm would be a tier that could provide a common interface onto the many orchestration and scheduling frameworks. From what I can see, Swarm is designed to provide a smooth Docker deployment workflow, working with some existing container workflow frameworks such as Deis, but flexible enough to yield to "heavyweight" deployment and resource management such as Mesos.
Hope this helps - this could be an enormous post. I think the key is that these are young, evolving services that will likely merge and become interoperable, but we need to ride out the next 12 months to see how it plays out. There's some very clever people on the problem, so the future looks very bright.
As far as I understand it:
Mesos, Kubernetes and Fleet are all trying to solve a very similar problem. The idea is that you abstract away all your hardware from developers and the 'cluster management tool' sorts it all out for you. Then all you need to do is give a container to the cluster, give it some info (keep it running permanently, scale up if X happens etc) and the cluster manager will make it happen.
With Mesos, it does all the cluster management for you, but it doesn't include the scheduler. The scheduler is the bit that says, ok this process needs 2 procs and 512MB RAM, and I have a machine over there with that free, so I'll run it on that machine. There are some plugin schedulers available for Mesos: Marathon and Chronos and you can write your own. This gives you a lot of power of resource distribution and cluster scaling etc.
Fleet and Kubernetes seem to abstract away those sorts of details (so you don't have to write your own scheduler basically). This means you have to define your tasks and submit them in the format/manner defined by Fleet or Kubernetes and then they take over and schedule the tasks (containers) for you.
So I guess: Using Mesos may mean a bit more work in writing your own scheduler, but potentially provides more flexibility if required.
I think the idea of running Kubernetes on top of Mesos is that Kubernetes acts as the scheduler for Mesos. Personally I'm not sure what benefits this brings over running one or the other on its own though (hopefully someone will jump in and explain!)
As MikeB said.. it's early days, and it's all up for grabs (keep an eye on Amazon's ECS as well) so there are many competing standards and a lot of overlap!
-edit- I didn't mention Docker swarm as I don't really have much experience with it.
For anyone coming to this after 2017 fleet is deprecated. Do not use it anymore.
Fleet docs say "fleet is no longer actively developed or maintained by CoreOS" and link to Container orchestration: Moving from fleet to Kubernetes. Fleet was removed from Container Linux (formerly known as CoreOS Linux) and replaced with Kubernetes kubelet (agent). This coincided with a corporate pivot to offer Tectonic (a Kubernetes distro) as their primary product.