Titan addresses the problem of low-latency retrieval of very large volumes of data in two ways. Second, it tries to maximize disk parallelism by declustering the set of chunks onto a large disk farm. For declustering the dataset, the group developed and used the minimax declustering algorithm. The key idea of the algorithm is to extend Prim's minimal spanning tree algorithm to construct as many spanning trees as there are disks in the disk farm, and to assign all the blocks associated with a single spanning tree to a single disk. To generate the edge costs, we chose the proximity index proposed by Kamel and Faloutsos.
Titan delivers good performance for both small and large queries.
Nevertheless, there are two factors that limit further improvements in performance. In the experiments, this shows up as a large amount of time spent in communication. Second, there is considerable computational load imbalance. The loss of locality is primarily due to the hidden assumption in the declustering algorithm, as well as in the algorithm that partitions the query processing, that the cost of moving the data from a disk to a processing node is the same for all disks and all nodes. In reality, data blocks retrieved by a node must be forwarded to all consumers of the data block, resulting in a large amount of communication.