Name
Title | ||
|---|---|---|
Aggregating correlated cold data to minimize the performance degradation and power consumption of cold storage nodes. |
Abstract | ||
|---|---|---|
Under the circumstance of big data, traditional storage systems face the big challenge of energy consumption. Switching some storage nodes, which do not experience workloads, to a low-power state is a typical approach to reduce the consumption of energy. This method divides the storage nodes into an active group and a low-power one. That is, the frequently accessed data are stored into the active group which maintains the nodes in an active state to offer service, and the cold data accessed infrequently are stored into the low-power group. The storage nodes in this low-power group are normally called cold nodes, because they can be switched to a low-power state to save energy for a certain amount of time. In cold nodes, one fact, which is often neglected, is that the placement of cold data has a significant impact on the system performance and power consumption. To some extent, switching a storage node from a low-power state to an active state incurs a crucial delay and energy consumption. This paper proposes to aggregate and store the correlated cold data in the same cold node within the low-power group. Now that the correlated data are normally accessed together, our approach can greatly reduce the number of power state transitions and lengthen the idle periods that the cold nodes experience. On the other hand, it can also minimize the performance degradation and power consumption. Experimental results demonstrate that this method effectively reduces the energy consumption while maintaining system performance at an acceptable level in contrast to some state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11227-018-2366-x | The Journal of Supercomputing |
Keywords | Field | DocType |
Big data, Clustered storage system, Power state switching, Energy-aware, Data placement, Data correlation | Data correlation,Cold storage,Computer science,Idle,Degradation (geology),Big data,Energy consumption,Power consumption,Distributed computing | Journal |
Volume | Issue | ISSN |
75 | 2 | 1573-0484 |
Citations | PageRank | References |
2 | 0.36 | 36 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheng Hu | 1 | 16 | 4.65 |
| Yuhui Deng | 2 | 331 | 39.56 |