My main questions was why always store the same blocks together, and you definitely answered that. Interesting approach. It definitely achieves your goal of s

My main questions was why always store the same blocks together, and you definitely answered that. Interesting approach. It definitely achieves your goal of spreading out the load. I think this can be achieved without making sure you store the blocks always together though, and it might help move towards a less coordinated system. Here is how I solved the problem: You can arrange each instance (you call it cell in your post) so it has different pools of storage. Maybe something that might make sense is each machine at the top of each rack is one pool, all the next machines down is another pool, and so on. All replication is always done across a pool. All the machines in a pool are stored to in a round robin fashion at the client. The end user can choose how many pools they want to replicate over. Abstract each of the machines as a storage unit. Each storage unit actually contains a series of 'slabs' which are largish files (32 gb in size generally), that grow in some large increment and close out when they hit a high watermark. Each write to a machine goes to a different writable slab in a round robin fashion. Each server knows the path and the contents of each slab, and on disk failure all the individual pieces are replicated to another set of open slabs just like the blocks came in fresh (with an exclusion of the slabs that already have a copy). Since all requests are balanced across the pools, and each client has a different round robin list it manages locally the individual pieces are evenly distributed over the other machines in the other pools. I tried really hard though to optimize for a zero master situation, so my clients handle contacting all the different machines and doing caching and retrying. So it sounds like I had different goals than you guys did, so I'm not surprised your have different trade offs.