Name
Abstract | ||
|---|---|---|
The current Cloud infrastructure services (IaaS) market employs a resource-based selling model: customers rent nodes from the provider and pay per-node per-unit-time. This selling model places the burden upon customers to predict their job resource requirements and durations. Inaccurate prediction by customers can result in over-provisioning of resources, or under-provisioning and poor job performance. Thanks to improved resource virtualization and multi-tenant performance isolation, as well as common frameworks for batch jobs, such as MapReduce, Cloud providers can predict job completion times more accurately. We offer a new definition of QoS-levels in terms of job completion times and we present a new QoS-based selling mechanism for batch jobs in a multi-tenant OpenStack cluster. Our experiments show that the QoS-based solution yields up to 40% improvement over the revenue of more standard selling mechanisms based on a fixed per-node price across various demand and supply conditions in a 240-VCPU OpenStack cluster. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Revenue,Computer science,Temporal isolation among virtual machines,Scheduling (computing),Quality of service,Real-time computing,Job scheduler,Batch processing,Job performance,Cloud computing |
DocType | Volume | Citations |
Journal | abs/1504.07283 | 0 |
PageRank | References | Authors |
0.34 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Sandholm | 1 | 0 | 0.34 |
| Julie Ward | 2 | 93 | 8.86 |
| Filippo Balestrieri | 3 | 1 | 0.69 |
| Bernardo A. Huberman | 4 | 7071 | 1187.06 |