SPARK-1706: Allow multiple executors per worker in Standalone mode

resubmit of https://github.com/apache/spark/pull/636  for a totally different algorithm

https://issues.apache.org/jira/browse/SPARK-1706

In current implementation, the user has to start multiple workers in a server for starting multiple executors in a server, which introduces additional overhead due to the more JVM processes...

In this patch, I changed the scheduling logic in master to enable the user to start multiple executor processes within the same JVM process.

1. user configure spark.executor.maxCoreNumPerExecutor to suggest the maximum core he/she would like to allocate to each executor

2. Master assigns the executors to the workers with the major consideration on the memoryPerExecutor and the worker.freeMemory, and tries to allocate as many as possible cores to the executor ```min(min(memoryPerExecutor, worker.freeCore), maxLeftCoreToAssign)``` where ```maxLeftCoreToAssign = maxExecutorCanAssign * maxCoreNumPerExecutor```

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Other small changes include

change memoryPerSlave in ApplicationDescription to memoryPerExecutor, as "Slave" is overrided to represent both worker and executor in the documents... (we have some discussion on this before?)
Maybe just call this memoryPerExecutorMB.