My problem is that I have code that need a rebooted node. I have many long running Jenkins test jobs that needs to be executed on rebooted nodes.
My existing solution is to define multiple "proxy" machines in Jenkins with the same label (TestLable) and 1 executor per machine. I bind all the test jobs to the label (TestLable). In the test execution script I detect the Jenkins machine (Jenkins env. NODE_NAME) and use that to know what physical physical machine the tests should use.
Do anybody know of a better solution?
The above works but I need to define a high number of “nodes/machines” that may not be needed. What I would like was a plugin that would be able to grant a token to a Jenkins job. This way a job would not be executed before a Jenkins executor and a token was free. The token should be a string so that my test jobs could use it to know what external node it could use.
We have written our own scheduler that allocates stuff before starting Jenkins nodes. There may be a better solution - but this works for us mostly. I've yet to come across an off-the-shelf scheduler that can deal with complicated allocation of different hardware resources. We have n box types, allocated to n build types.
Some build types we have are not compatible together without destroying all persistent data - which may be required as it takes a long time to gather. Some jobs require combinations of these hardware types. We store the details in a DB, and then use business logic to determine how it is allocated. We've often found that particular job types need additional business logic or extra data fields to account for their specific requirements.
So it may be the best way is to write your own scheduler, in your own language of choice, which takes into account your particular needs.
Related
How does a program like folding#home work? Does my computer
individually perform a unit of "work" on it completely separate to other computers running folding#home? Then send the answer back when it's completed?
Or does Folding#home see all the computers connected to it as the project having let's say 1000 cores and then when work is done it's the equivalent of saying something like make -j <total number of cores>
Projects likes Folding#Home and BOINC are examples of loosely-coupled parallel computing where each task is fully self-contained and can be completed without communication with other computing entities. They are also examples of a pattern known as controller/worker (used to be known as master/worker), in which a central controller splits a large task into a pool of small(er) subtasks and distributes it to a bunch of worker processes on a first come first served basis, which corresponds to your first point.
In F#H (and BOINC), client computers connect to the server, request a task, work on it until it's complete, then connect to the server again to return the result and request a new task. The benefits of this are automatic load balancing, fault tolerance (via redundancy), and no need for scheduling.
When you run make -j #cores, make launches a number of parallel jobs but those jobs are usually interdependent, so make has to schedule them in an optimal way. The jobs are then run as processes on the same computer which affords make full process control. If a build step fails, the entire build job aborts immediately and the user can quickly look into the problem, fix it, and restart the build. This is not a viable model for when a client computer could have an arbitrary compute speed, could connect and disconnect at any time, and/or could decide to simply stop processing tasks. There are distributed versions of make like dmake that run different parts of the build process on different remote nodes, but that still happens in a tightly controlled environment, typically on a build cluster.
Note that on a very high level of abstraction the two are basically equivalent with the main difference being whether jobs are pushed or pulled. While job pulling works fine on all kinds of systems, job pushing usually requires (tightly-coupled) systems with predictable characteristics and good scheduling algorithms to be efficient.
I've recently encountered a problem to process a pickle file of 8 Gigabytes with a Python script using VMs in Google Cloud Compute Engine. The problem is that the process takes too long and I am searching for ways to decrease the time of processing. One of possible solutions could be sharing the processes in the script or map them between CPUs of several VMs. If somebody knows how to perform it, please, share with me!))
You can use Clusters for Large-scale Technical Computing in the Google Cloud Platform (GCP). There are open source software like ElastiCluster provide cluster management and support for provisioning nodes while using Google Compute Engine (GCE).
After the cluster is operational, workload manager manages the task execution and node allocation. There are a variety of popular commercial and open source workload managers such as HTCondor from the University of Wisconsin, Slurm from SchedMD, Univa Grid Engine, and LSF Symphony from IBM.
This article is also helpful.
it looks like an HPC problem. Look at this link: https://cloud.google.com/solutions/architecture/highperformancecomputing.
There are lot of valuable solutions to your problem but it depends on the details of your case. A first simple approach could be to logically split your task in small jobs. Then you can assign a subset of these jobs to each GCE instance in your group of dedicated instances.
You can consider to create a group of a predefined number of instances. Each run could rely on a startup scripts in order to reach out the job it must execute. When the job finishes the instance can be deleted and substituted by a new one (Google Compute Engine Managed Instance Groups will create a new instance automatically). You must only manage when the group should start and stop.
Furthermore, you can consider preemptible instances (more cheaper).
Hope this helps you.
Bye
Background
The problem we're facing is that we are doing video encoding and want to distribute the load to multiple nodes in the cluster.
We would like to constrain the number of video encoding jobs on a particular node to some maximum value. We would also like to have small video encoding jobs sent to a certain grouping of nodes in the cluster, and long video encoding jobs sent to another grouping of nodes in the cluster.
The idea behind this is to help maintain fairness amongst clients by partitioning the large jobs into a separate pool of nodes. This helps ensure that the small video encoding jobs are not blocked / throttled by a single tenant running a long encoding job.
Using Service Fabric
We plan on using an ASF service for the video encoding. With this in mind we had an idea of dynamically creating a service for each job that comes in. Placement constraints could then be used to determine which pool of nodes a job would run in. Custom metrics based on memory usage, CPU usage ... could be used to limit the number of active jobs on a node.
With this method the node distributing the jobs would have to poll whether a new service could currently be created that satisfies the placement constraints and metrics.
Questions
What happens when a service can't be placed on a node? (Using CreateServiceAsync I assume?)
Will this polling be prohibitively expensive?
Our video encoding executable is packaged along with the service which is approximately 80MB. Will this make the spinning up of a new service take a long time? (Minutes vs seconds)
As an alternative to this we could use a reliable queue based system, where the large jobs pool pulls from one queue and the small jobs pool pulls from another queue. This seems like the simpler way, but I want to explore all options to make sure I'm not missing out on some of the features of Service Fabric. Is there another better way you would suggest?
I have no experience with placement constraints and dynamic services, so I can't speak to that.
The polling of the perf counters isn't terribly expensive, that being said it's not a free operation. A one second poll interval shouldn't cause any huge perf impact while still providing a decent degree of resolution.
The service packages get copied to each node at deployment time rather than when services get spun up, so it'll make the deployment a bit slower but not affect service creation.
You're going to want to put the job data in reliable collections any way you structure it, but the question is how. One idea I just had that might be worth considering is making the job processing service a partitioned service and base your partitioning strategy based off encoding job size and/or tenant so that large jobs from the same tenant get stuck in the same queue, and smaller jobs for others go elsewhere.
As an aside, one thing I've dealt with in the past is SF remoting limits the size of the messages sent and throws if its too big, so if your video files are being passed from service to service you're going to want to consider a paging strategy for inter service communication.
Few questions regarding HDInsight jobs approach.
1) How to schedule HDInsight job? Is there any ready solution for it? For example if my system will constantly get a large number of new input files collected that we need to run map/reduce job upon, what is the recommended way to implemented on-going processing?
2) From the price perspective, it is recommended to remove the HDInsight cluster for the time when there is no job running. As I understand there is no way to automate this process if we decide to run the job daily? Any recommendations here?
3) Is there a way to ensure that the same files are not processed more than once? How do you solve this issue?
4) I might be mistaken, but it looks like every hdinsight job requires a new output storage folder to store reducer results into. What is the best practice for merging of those results so that reporting always works on the whole data set?
Ok, there's a lot of questions in there! Here are I hope a few quick answers.
There isn't really a way of scheduling job submission in HDInsight, though of course you can schedule a program to run the job submissions for you. Depending on your workflow, it may be worth taking a look at Oozie, which can be a little awkward to get going on HDInsight, but should help.
On the price front, I would recommend that if you're not using the cluster, you should destroy it and bring it back again when you need it (those compute hours can really add up!). Note that this will lose anything you have in the HDFS, which should be mainly intermediate results, any output or input data held in the asv storage will persist in and Azure Storage account. You can certainly automate this by using the CLI tools, or the rest interface used by the CLI tools. (see my answer on Hadoop on Azure Create New Cluster, the first one is out of date).
I would do this by making sure I only submitted the job once for each file, and rely on Hadoop to handle the retry and reliability side, so removing the need to manage any retries in your application.
Once you have the outputs from your initial processes, if you want to reduce them to a single output for reporting the best bet is probably a secondary MapReduce job with the outputs as its inputs.
If you don't care about the individual intermediate jobs, you can just chain these directly in the one MapReduce job (which can contain as many map and reduce steps as you like) through Job chaining see Chaining multiple MapReduce jobs in Hadoop for a java based example. Sadly the .NET api does not currently support this form of job chaining.
However, you may be able to just use the ReducerCombinerBase class if your case allows for a Reducer->Combiner approach.
The SPOJ is a website that lists programming puzzles, then allows users to write code to solve those puzzles and upload their source code to the server. The server then compiles that source code (or interprets it if it's an interpreted language), runs a battery of unit tests against the code, and verifies that it correctly solves the problem.
What's the best way to implement something like this - how do you sandbox the user input so that it can not compromise the server? Should you use SELinux, chroot, or virtualization? All three plus something else I haven't thought of?
How does the application reliably communicate results outside of the jail while also insuring that the results are not compromised? How would you prevent, for instance, an application from writing huge chunks of nonsense data to disk, or other malicious activities?
I'm genuinely curious, as this just seems like a very risky sort of application to run.
A chroot jail executed from a limited user account sounds like the best starting point (i.e. NOT root or the same user that runs your webserver)
To prevent huge chunks of nonsense data being written to disk, you could use disk quotas or a separate volume that you don't mind filling up (assuming you're not testing in parallel under the same user - or you'll end up dealing with annoying race conditions)
If you wanted to do something more scalable and secure, you could use dynamic virtualized hosts with your own server/client solution for communication - you have a pool of 'agents' that receive instructions to copy and compile from X repository or share, then execute a battery of tests, and log the output back via the same server/client protocol. Your host process can watch for excessive disk usage and report warnings if required, the agents may or may not execute the code under a chroot jail, and if you're super paranoid you would destroy the agent after each run and spin up a new VM when the next sample is ready for testing. If you're doing this large scale in the cloud (e.g. 100+ agents running on EC2) you only ever have enough spun up to accommodate demand and therefore reduce your costs. Again, if you're going for scale you can use something like Amazon SQS to buffer requests, or if you're doing a experimental sample project then you could do something much simpler (just think distributed parallel processing systems, e.g. seti#home)