Parallel
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// server code class HelloWorldActor extends UntypedActor { public void onReceive(Object msg) { getContext().replySafe(msg + " World" ); } } remote().start( "localhost" , 9999).register( "hello-service" , actorOf(HelloWorldActor.class)); // client code ActorRef actor = remote().actorFor( "hello-service" , "localhost" , 9999); Object res = actor.sendRequestReply( "Hello" ); // server code class HelloWorldActor extends Actor { def receive = { case msg => self reply (msg + " World" ) } } remote.start( "localhost" , 9999).register( "hello-service" , actorOf[ HelloWorldActor ]) // client code val actor = remote.actorFor( "hello-service" , "localhost" , 9999) val result = actor !! "Hello"
Akka Project
Rio - Welcome to the Rio Project
Rio is an open source technology that provides a dynamic architecture for developing, deploying and managing distributed systems composed of services. Rio turns a network of compute resources into a dynamic service, providing a policy based approach for fault detection and recovery, scalability and dynamic deployment. Key to the architecture are a set of dynamic capabilities and reliance on policy-based and SLA mechanisms. Developing services for use with Rio is simple. Rio provides a non-intrusive model that removes the complexity surrounding service development and deployment.
MapReduce - Hadoop Wiki
The key aspect of the MapReduce algorithm is that if every Map and Reduce is independent of all other ongoing Maps and Reduces, then the operation can be run in parallel on different keys and lists of data. On a large cluster of machines, you can go one step further, and run the Map operations on servers where the data lives. Rather than copy the data over the network to the program, you push out the program to the machines. The output list can then be saved to the distributed filesystem, and the reducers run to merge the results. Again, it may be possible to run these in parallel, each reducing different keys. A distributed filesystem spreads multiple copies of the data across different machines.This submission contains video lectures and related course materials from a series of lectures that was taught to Google software engineering interns during the Summer of 2007.
Cluster Computing and MapReduce - Google Code University - Google Code
A shared nothing architecture (SN) is a distributed computing architecture in which each node is independent and self-sufficient, and there is no single point of contention across the system. More specifically, none of the nodes share memory or disk storage. People typically contrast SN with systems that keep a large amount of centrally-stored state information, whether in a database , an application server , or any other similar single point of contention. While SN is best known in the context of web development, the concept predates the web: Michael Stonebraker at the University of California, Berkeley used the term in a 1986 database paper. [ 1 ] In it he mentions existing commercial implementations of the architecture (although none are named explicitly). Teradata , which delivered its first system in 1983, was probably one of those commercial implementations. [ 2 ]
Shared nothing architecture - Wikipedia, the free encyclopedia
