Name
Abstract | ||
|---|---|---|
Cloud object stores are increasingly becoming the de facto storage choice for big data analytics platforms, mainly because they simplify the management of large blocks of data at scale. To ensure cost-effectiveness of the storage service, the object stores use hard disk drives (HDDs). However, the lower performance of HDDs affect tenants who have strict performance requirements for their big data applications. The use of faster storage devices such as solid state drives (SSDs) is thus desirable by the tenants, but incurs significant maintenance costs to the provider. We design a tiered object store for the cloud, which comprises both fast and slow storage devices. The resulting hybrid store exposes the tiering to tenants with a dynamic pricing model that is based on the tenants' usage and the provider's desire to maximize profits. The tenants leverage knowledge of their workloads and current pricing information to select a data placement strategy that would meet the application requirements at the lowest cost. Our approach allows both a service provider and its tenants to engage in a pricing game, which our results show yields a win-win situation |
Year | Venue | DocType |
|---|---|---|
2015 | USENIX Workshop on Hot Topics in Cloud Computing | Conference |
Citations | PageRank | References |
5 | 0.45 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Cheng | 1 | 75 | 9.77 |
| M. Safdar Iqbal | 2 | 93 | 4.76 |
| Aayush Gupta | 3 | 213 | 11.81 |
| Ali R. Butt | 4 | 651 | 47.51 |