distributed

I've a problem which is giving me some hard time trying to figure it out the ideal solution and, to better explain it, I'm going to expose my scenario here. I've a server that will receive orders from several clients. Each client will submit a set of recurring tasks that should be executed at some specified intervals, eg.: client A submits task AA that should be executed every minute between 2009-12-31 and 2010-12-31 ; so if my math is right that's about 525 600 operations in a year, given more clients and tasks it would be infeasible to let the server process all these tasks so I came up with

I am new to distributed tensorflow. I found this distributed mnist test in here: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/dist_test/python/mnist_replica.py But I don't know how to make it run. I used the following script: python distributed_mnist.py --num_workers=3 --num_parameter_servers=1 --worker_index=0 --worker_grpc_url="grpc://tf-worker0:2222"\ & python distributed_mnist.py --num_workers=3 --num_parameter_servers=1 --worker_index=1 --worker_grpc_url="grpc://tf-worker1:2222"\ & python distributed_mnist.py --num_workers=3 --num_parameter_servers=1 --worker

I'm learning about Kafka, reading the introduction section here https://kafka.apache.org/documentation.html#introduction specifically the portion about Consumers. In the second to last paragraph in the Introduction it reads Kafka does it better. By having a notion of parallelism—the partition—within the topics, Kafka is able to provide both ordering guarantees and load balancing over a pool of consumer processes. This is achieved by assigning the partitions in the topic to the consumers in the consumer group so that each partition is consumed by exactly one consumer in the group. By doing this

I need a simple way to monitor multiple text log files distributed over a number of HP-UX servers. They are a mix of text and XML log files from several distributed legacy systems. Currently we just ssh to the servers and use tail -f and grep , but that doesn't scale when you have many logs to keep track of. Since the logs are in different formats and just files in folders (automatically rotated when they reach a certain size) I need to both collect them remotely and parse each one differently. My initial thought was to make a simple daemon process that I can run on each server using a custom

My web server would be overloaded quickly if all the work were done there. I'm going to stand up a second server behind it, to process data. What's the advantage of EJB over RMI, or vice versa? What about web services (SOAP, REST)? duffymo EJBs are built on top of RMI. Both imply Java clients and beans. If your clients need to be written in something else (e.g., .NET, PHP, etc.) go with web services or something else that speaks a platform-agnostic wire protocol, like HTTP or XML over HTTP or SOAP. If you choose RMI, you don't need a Java EE EJB app server. You have to keep client and server