Update the README in response to #1.

Author: bamos

README.md

@@ -337,7 +337,7 @@ modify `config.yaml` to have your server configurations,
 and build the application with `ss-a`, send the JAR to your cluster
 with `ss-sy`, and start Spindle with `ss-st`.
 
-# Benchmarking Results
+# Experimental Results
 All experiments leverage a homogeneous six node production cluster
 of HP ProLiant DL360p Gen8 blades.
 Each node has 32GB of DDR3 memory at 1333MHz,
@@ -354,6 +354,39 @@ in Parquet.
 The YAML formatted results, scripts, and resulting figures
 are in the [benchmark-scripts][benchmark-scripts] directory.
 
+## Scaling HDFS and Spark workers.
+Predicting the optimal resource allocation to minimize query latency for
+distributed applications is difficult. No production software can accurately
+predict the optimal number of Spark and HDFS nodes for a given application.
+This experiment observes the execution time of queries as the number of Spark
+and HDFS workers is increased. We manually scale and rebalance the HDFS data.
+
+The following figure shows the time to load all columns the queries
+use for the week of data as the Spark and HDFS workers are scaled. The data is
+loaded by caching the Spark RDD and performing a null operation on them, such
+as `rdd.cache.foreach{x` =>{}}. The downward trend of the data load times
+indicate that using more Spark or HDFS workers will decrease the time to load
+data.
+
+![](https://raw.githubusercontent.com/adobe-research/spindle/master/benchmark-scripts/scaling/dataLoad.png)
+
+The following table shows the execution time of the queries
+with cached data when scaling the HDFS and Spark workers.
+The bold data indicates where adding a
+Spark and HDFS worker hurts performance. The surprising results show that
+adding a single Spark or HDFS worker commonly hurts query performance, and
+interestingly, no query experiences minimal execution time when using all 6
+workers. Our future work is to further experiment by tuning Spark to understand
+the performance degradation, which might be caused by network traffic or
+imbalanced workloads.
+
+Q2 and Q3 are similar queries and consequently have similar performance as
+scaling the Spark and HDFS workers, but has an anomaly when using 3 workers
+where Q2 executes in 17.10s and Q3 executes in 55.15s. Q6’s execution time
+increases by 10.67 seconds between three and six Spark and HDFS workers.
+
+![](https://github.com/adobe-research/spindle/raw/master/images/scaling-spark-hdfs.png)
+
 ## Intermediate data partitioning.
 Spark cannot optimize the number of records in the partitions
 because counting the number of records in the initial and
@@ -458,6 +491,11 @@ The slowdowns for two concurrent queries indicate further query optimizations
 could better balance the work between all Spark workers and
 likely result in better query execution time.
 
+# Contributing and Development Status
+Spindle is not currently under active development by Adobe.
+However, we are happy to review and respond to issues,
+questions, and pull requests.
+
 # License
 Bundled applications are copyright their respective owners.
 [Twitter Bootstrap][bootstrap] and