Name
Title | ||
|---|---|---|
RS-Pooling: an adaptive data distribution strategy for fault-tolerant and large-scale storage systems |
Abstract | ||
|---|---|---|
Storage pooling is a virtualization technique used in data centers to build upgradeable storage pools and to face up the explosive growth of information. In this technique, a randomized data distribution strategy (DDS) ensures the load balancing when adding new devices to the pool by using reallocation mechanisms. However, when applying fault-tolerant schemes to the storage pools, the system produces r redundant objects from a common data source and DDS must allocate them in different devices, which increases the complexity of the reallocation operations performed during the upgrade procedures. This paper presents RS-Pooling: an adaptive DDS for fault-tolerant and large-scale storage systems. RS-Pooling builds storage pools by grouping devices into disjointed sub-pools and ensures the effectiveness of fault-tolerant schemes by performing the allocation of redundant objects from a common data source in different sub-pools. In RS-Pooling, the first redundant object is allocated in random manner whereas the rest of them are allocated by using a cyclic list of sub-pools, this procedure minimizes the amount of reallocation operations, and fosters load balancing. We performed an emulation-based evaluation of RS-Pooling and a traditional DDS for storage pooling called RUSHp. The evaluation reveals that RS-Pooling improves the time efficiency of look up operations compared to that obtained from RUSHp. The evaluation also shows that, in upgrade procedures and regardless of the initial settlement, RS-Pooling requires significantly less reallocation operations than that of RUSHp for load balancing of fault-tolerant storage pools. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s11227-015-1569-7 | The Journal of Supercomputing |
Keywords | Field | DocType |
Large-scale storage,Load balancing,Adaptability,Redundancy,Fault-tolerance,Availability,Scalability,Bins and balls model | Virtualization,Computer science,Load balancing (computing),Parallel computing,Pooling,Upgrade,Redundancy (engineering),Fault tolerance,Emulation,Distributed computing,Scalability | Journal |
Volume | Issue | ISSN |
72 | 2 | 0920-8542 |
Citations | PageRank | References |
0 | 0.34 | 29 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Moisés Quezada Naquid | 1 | 1 | 1.02 |
| Ricardo Marcelín Jiménez | 2 | 20 | 3.91 |
| José Luis González Compeán | 3 | 0 | 1.01 |
| Jesus Carretero Perez | 4 | 5 | 1.12 |