[SPARK-1777] Prevent OOMs from single partitions

**Problem.** When caching, we currently unroll the entire RDD partition before making sure we have enough free memory. This is a common cause for OOMs especially when (1) the BlockManager has little free space left in memory, and (2) the partition is large.

**Solution.** We maintain a global memory pool of `M` bytes shared across all threads, similar to the way we currently manage memory for shuffle aggregation. Then, while we unroll each partition, periodically check if there is enough space to continue. If not, drop enough RDD blocks to ensure we have at least `M` bytes to work with, then try again. If we still don't have enough space to unroll the partition, give up and drop the block to disk directly if applicable.

**New configurations.**
- `spark.storage.bufferFraction` - the value of `M` as a fraction of the storage memory. (default: 0.2)
- `spark.storage.safetyFraction` - a margin of safety in case size estimation is slightly off. This is the equivalent of the existing `spark.shuffle.safetyFraction`. (default 0.9)

For more detail, see the [design document](https://issues.apache.org/jira/secure/attachment/12651793/spark-1777-design-doc.pdf). Tests pending for performance and memory usage patterns.

Looks like a code changed a bit since I last reviewed.
My point was rather simple : if we do not have offheap/disk enabled in the storage level - what is the behavioral expectation in case size estimation says it cant be cached due to memory limit ?

1) Do we abort the task (so that it might be cached on some other node), 
2) Do we continue with it un-cached in memory (which seems to be the approach after the recent changes) - which is a violation of the StorageLevel contract. 
3) Do we attempt to cache in memory anyway and risk OOM (and so (1) ).

Also note that the size estimation is just estimation and can be off mark - so it is possible for the iterator to be cache-able, and yet estimated to be not.