Name
Abstract | ||
|---|---|---|
Nowadays, data-centers are largely under-utilized because resource allocation is based on reservation mechanisms which ignore actual resource utilization. Indeed, it is common to reserve resources for peak demand, which may occur only for a small portion of the application life time. As a consequence, cluster resources often go under-utilized. In this work, we propose a mechanism that improves compute cluster utilization and their responsiveness, while preventing application failures due to contention in accessing finite resources such as RAM. Our method monitors resource utilization and employs a data-driven approach to resource demand forecasting, featuring quantification of uncertainty in the predictions. Using demand forecast and its confidence, our mechanism modulates cluster resources assigned to running applications, and reduces the turnaround time by more than one order of magnitude while keeping application failures under control. Thus, tenants enjoy a responsive system and providers benefit from an efficient cluster utilization. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BigDataCongress.2019.00019 | 2019 IEEE International Congress on Big Data (BigDataCongress) |
Keywords | Field | DocType |
online forecast,resource utilization,Gaussian processes | Reservation,Cluster (physics),Demand forecasting,Computer science,Resource allocation,Peak demand,Gaussian process,Turnaround time,Database,Computer cluster,Distributed computing | Conference |
ISSN | ISBN | Citations |
2379-7703 | 978-1-7281-2773-6 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francesco Pace | 1 | 4 | 2.13 |
| dimitrios milios | 2 | 3 | 2.75 |
| Damiano Carra | 3 | 124 | 19.56 |
| Pietro Michiardi | 4 | 1512 | 111.53 |