What can be the reasons for 90% of samples belong to one cluster when there is 8 clusters?
K-means is indeed sensitive to noise BUT investigate your data! 
Have you pre-processed your "real-data" before applying the distance measure on it? 
Are you sure your distance metric represents proximity as you'll expected?  
There are a lot of possible "bugs" that may cause this scenario.. not necessary k-means fault
I use the k-means algorithm to clustering set of documents.  
(parameters are - number of clusters=8, number of runs for different centroids =10)
The number of documents are 5800
Surprisingly the result for the clustering is 
90% of documents belong to cluster - 7 (final cluster)
9% of documents belong to cluster - 0 (first cluster)
and the rest 6 clusters have only a single sample. What might be the reason for this?