Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Allocating data and workload among multiple servers in a local area network
Information Systems
File Assignment in Parallel I/O Systems with Minimal Variance of Service Time
IEEE Transactions on Computers
Comparative Models of the File Assignment Problem
ACM Computing Surveys (CSUR)
Computer Algorithms: C++
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Data Allocation for Multidisk Databases
IEEE Transactions on Knowledge and Data Engineering
Data Distribution Algorithms For Load Balanced Fault-Tolerant Web Access
SRDS '97 Proceedings of the 16th Symposium on Reliable Distributed Systems
MMPacking: a load and storage balancing algorithm for distributed multimedia servers
IEEE Transactions on Circuits and Systems for Video Technology
Workload-aware storage layout for database systems
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Hi-index | 0.00 |
The rapid growth of Internet brings the need for a low cost high performance file system. Two objectives are to be pursued in building such a large scale storage system on multiple disks: load balancing and storage minimization. We investigate the optimization problem of placing variable-size data items onto multiple disks with replication to achieve the two objectives. An approximate algorithm, called LSB_Placement, is proposed for the optimization problem. The algorithm performs bin packing along with MMPacking to obtain a load balanced placement with near-optimal storage balancing. The key issue in deriving the algorithm is to find the optimal bin capacity for the bin packing to reduce storage cost. We derive the optimal bin capacity and prove that LSB_Placement algorithm is asymptotically 1-optimal on storage balancing. That is, when the problem size exceeds certain threshold, the algorithm generates a load balanced placement in which the data sizes allocated on disks are almost balanced. We demonstrate that, for various Web applications, a load balanced placement can be generated with disk capacity not exceeding 10% more than the balanced storage space. This shows that the LSB_Placement algorithm is useful in constructing a low cost and high performance storage system.