A case for redundant arrays of inexpensive disks (RAID)
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
A class of generalized greedy algorithms for the multi-knapsack problem
Discrete Applied Mathematics - Special issue: combinatorial structures and algorithms
Petal: distributed virtual disks
Proceedings of the seventh international conference on Architectural support for programming languages and operating systems
Self-tuning histograms: building histograms without looking at data
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
The Vision of Autonomic Computing
Computer
Toward an Accurate Analysis of Range Queries on Spatial Data
IEEE Transactions on Knowledge and Data Engineering
Aqueduct: Online Data Migration with Performance Guarantees
FAST '02 Proceedings of the Conference on File and Storage Technologies
Mariposa: A New Architecture for Distributed Data
Proceedings of the Tenth International Conference on Data Engineering
An Adaptive Data Placement Scheme for Parallel Database Computer Systems
VLDB '90 Proceedings of the 16th International Conference on Very Large Data Bases
Data placement in shared-nothing parallel database systems
The VLDB Journal — The International Journal on Very Large Data Bases
Algorithms for data migration with cloning
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
IBM Storage Tank-- A heterogeneous scalable SAN file system
IBM Systems Journal
Experimental evidence on partitioning in parallel data warehouses
Proceedings of the 7th ACM international workshop on Data warehousing and OLAP
Tunable randomization for load management in shared-disk clusters
ACM Transactions on Storage (TOS)
Virtual Disk Reconfiguration with Performance Guarantees in Shared Storage Environment
ICITA '05 Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02
Automated Storage Management with QoS Guarantees
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
SLAS: An efficient approach to scaling round-robin striped volumes
ACM Transactions on Storage (TOS)
A Local-Search-Based Heuristic for the Demand-Constrained Multidimensional Knapsack Problem
INFORMS Journal on Computing
Database-aware semantically-smart storage
FAST'05 Proceedings of the 4th conference on USENIX Conference on File and Storage Technologies - Volume 4
IEEE Communications Magazine
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Database storage management at data centers is a manual, time-consuming, and error-prone task. Such management involves regular movement of database objects across storage nodes in an attempt to balance the I/O bandwidth utilization across disk drives. Achieving such balance is critical for avoiding I/O bottlenecks and thereby maximizing the utilization of the storage system. However, manual management of the aforesaid task, apart from increasing administrative costs, encumbers the greater risks of untimely and erroneous operations. We address the preceding concerns with STORM, an automated approach that combines low-overhead information gathering of database access and storage usage patterns with efficient analysis to generate accurate and timely hints for the administrator regarding data movement operations. STORM's primary objective is minimizing the volume of data movement required (to minimize potential down-time or reduction in performance) during the reconfiguration operation, with the secondary constraints of space and balanced I/O-bandwidth-utilization across the storage devices. We analyze and evaluate STORM theoretically, using a simulation framework, as well as experimentally. We show that the dynamic data layout reconfiguration problem is NP-hard and we present a heuristic that provides an approximate solution in O(Nlog(N/M) + (N/M)2) time, where M is the number of storage devices and N is the total number of database objects residing in the storage devices. A simulation study shows that the heuristic converges to an acceptable solution that is successful in balancing storage utilization with an accuracy that lies within 7% of the ideal solution. Finally, an experimental study demonstrates that the STORM approach can improve the overall performance of the TPC-C benchmark by as much as 22%, by reconfiguring an initial random, but evenly distributed, placement of database objects.