cluster-computing

Would it be possible to make a python cluster, by writing a telnet server, then telnet-ing the commands and output back-and-forth? Has anyone got a better idea for a python compute cluster? PS. Preferably for python 3.x, if anyone knows how. The Python wiki hosts a very comprehensive list of Python cluster computing libraries and tools . You might be especially interested in Parallel Python . Edit: There is a new library that is IMHO especially good at clustering: execnet . It is small and simple. And it appears to have less bugs than, say, the standard multiprocessing module. You can see most

I have created a Spark cluster on Openstack running on Ubuntu14.04 with 8gb of ram. I created two virtual machines with 3gb each (keeping 2 gb for the parent OS). Further, i create a master and 2 workers from first virtual machine and 3 workers from second machine. The spark-env.sh file has basic setting with export SPARK_MASTER_IP=10.0.0.30 export SPARK_WORKER_INSTANCES=2 export SPARK_WORKER_MEMORY=1g export SPARK_WORKER_CORES=1 Whenever i deploy the cluster with start-all.sh, i get "failed to launch org.apache.spark.deploy.worker.Worker" and some times "failed to launch org.apache.spark

Is there any easy way in a Java EE application (running on Websphere) to share an object in an application-wide scope across the entire cluster? Something maybe similar to Servlet Context parameters, but that is shared across the cluster. For example, in a cluster of servers "A" and "B", if a value is set on server A (key=value), that value should immediately (or nearly so) be available to requests on server B. (Note: Would like to avoid distributed caching solutions if possible. This really isn't a caching scenario as the objects being stored are fairly dynamic) I'm watching this to see if

I'm trying to move away from SQS to RabbitMQ for messaging service. I'm looking to build a stable high availability queuing service. For now I'm going with cluster. Current Implementation , I have three EC2 machines with RabbitMQ with management plugin installed in a AMI , and then I explicitly go to each of the machine and add sudo rabbitmqctl join_cluster rabbit@<hostnameOfParentMachine> With HA property set to all and the synchronization works. And a load balancer on top it with a DNS assigned. So far this thing works. Expected Implementation : Create an autoscaling clustered environment

I am using Express js and Node-cluster for taking the advantage of clustering I am also using PM2 for process and memory management. For a single machine, it is working fine, but my machine having 2 cores and I want to make available more cores. So I decided to join 3 more machines and now all 4 machines are connected using LAN. I am able to access the other machines using IP address in web browser also. Now I want to connect all the machines and want to share their cores so that I will finally have 2 + 6 = 8 cores for my application. How can it possible? Is there any node module available to

As far as i know SQL Server provides 4 techniques for better availability. I think these are the primary usage scenarios, in summary :- 1) Replication would be primarily suited for online-offline data synchronization scenarios (laptop , mobile devices, remote servers). 2) Log shipping could be used to have a failover server with manual switching, whereas 3) Database Mirroring is an automatic failover technique 4) Failover Clustering is an advanced type of database mirroring. Am i right ? Thanks. Failover clustering is an availability technology that provides redundancy at the hardware level

I'm confused about the purpose of having both hard and soft memory limits for ECS task definitions. IIRC the soft limit is how much memory the scheduler reserves on an instance for the task to run, and the hard limit is how much memory a container can use before it is murdered. My issue is that if the ECS scheduler allocates tasks to instances based on the soft limit, you could have a situation where a task that is using memory above the soft limit but below the hard limit could cause the instance to exceed its max memory (assuming all other tasks are using memory slightly below or equal to

I'm going to have 3 Tomcat servers and a Load Balancer that dispatches the requests without using ' sticky sessions '. I want to share sessions' data between the servers and I'm thinking in persisting them in DB. I'd like to use memcached as a layer in front of my DB to serve the requests faster and to don't put my db under heavy load . I'm thinking in providing my customized tomcat Manager that uses memcached before getting/persisting session data to DB as at the moment I don't see a transparent way of doing it (it means that I'll have to manage it again in the case I switch to another app

I'm working on some python code that extracts some image data from an ECW file using GDAL ( http://www.gdal.org/ ) and its python bindings. GDAL was built from source to have ECW support. The program is run on a cluster server that I ssh into. I have tested the program through the ssh terminal and it runs fine. However, I would now like to submit a job to the cluster using qsub, but it reports the following: Traceback (most recent call last): File "./gdal-test.py", line 5, in <module> from osgeo import gdal File "/home/h3/ctargett/.local/lib/python2.6/site-packages/GDAL-1.11.1-py2.6-linux-x86