Contention at the storage nodes is the main cause of long and variable data access times in distributed storage systems . Offered load on the system must be balanced across the storage nodes in order to minimize contention, and load balance in the system should be robust against the skews and fluctuations in content popularities. Data objects are replicated across multiple nodes in practice to allow for load balancing. However redundancy increases the storage requirement and should be used efficiently. We evaluate load balancing performance of natural storage schemes in which each data object is stored at d different nodes  and each node stores the same number of objects.  We find that  load balance in a system of n nodes improves multiplicatively with d as long as d = o\left(\log(n)\right), and improves exponentially as soon as d = \Theta\left(\log(n)\right). We show that the load balance in the system improves the same way with d when the service choices are created with XOR's of r objects rather than object replicas , which also reduces the storage overhead multiplicatively by r. However, unlike accessing an object replica, access through a recovery set composed by an XOR'ed object copy requires downloading content from r nodes , which increases the load imbalance in the system additively by r  .
To facilitate load balancing, distributed systems store data redundantly. We evaluate the load balancing performance of storage schemes in which each object is stored at d different nodes , and each node stores the same number of objects. In our model, the load offered for the objects is sampled uniformly at random from all the load vectors with a fixed cumulative value. We find that the load balance in a system of n nodes improves multiplicatively with d as long as d = o\left(\log(n)\right), and improves exponentially once d = \Theta\left(\log(n)\right). We show that the load balance improves in the same way with d when the service choices are created with XOR's of r objects rather than object replicas . In such redundancy schemes, storage overhead is reduced multiplicatively by r. However, recovery of an object requires downloading content from r nodes . At the same time, the load balance increases additively by r . We express the system's load balance in terms of the maximal spacing or maximum of d consecutive spacings between the ordered statistics of uniform random variables. Using this connection and the limit results on the maximal d-spacings, we derive our main results .
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Contention at the storage nodes is the main cause of long and variable data access times in distributed storage systems . Offered load on the system must be balanced across the storage nodes in order to minimize contention, and load balance in the system should be robust against the skews and fluctuations in content popularities. Data objects are replicated across multiple nodes in practice to allow for load balancing. However redundancy increases the storage requirement and should be used efficiently. We evaluate
To facilitate load balancing, distributed systems store data redundantly. We evaluate the
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In our model, the load offered for the objects is sampled uniformly at random from all the load vectors with a fixed cumulative value.
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unlike accessing an object replica, access through a recovery set composed by an XOR'ed object copy
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. We express the system's load balance in terms of the maximal spacing or maximum of d consecutive spacings between the ordered statistics of uniform random variables. Using this connection and the limit results on the maximal d-spacings, we derive our main results