Name
Abstract | ||
|---|---|---|
An effective way to increase utilization and reduce costs in datacenters is to co-locate their latency-critical services and batch workloads. In this paper, we describe systems that harvest spare compute cycles and storage space for co-location purposes. The main challenge is minimizing the performance impact on the services, while accounting for their utilization and management patterns. To overcome this challenge, we propose techniques for giving the services priority over the resources, and leveraging historical information about them. Based on this information, we schedule related batch tasks on servers that exhibit similar patterns and will likely have enough available resources for the tasks' durations, and place data replicas at servers that exhibit diverse patterns. We characterize the dynamics of how services are utilized and managed in ten large-scale production datacenters. Using real experiments and simulations, we show that our techniques eliminate data loss and unavailability in many scenarios, while protecting the co-located services and improving batch job execution time. |
Year | Venue | Field |
|---|---|---|
2016 | OSDI | Spare part,Data loss,Computer science,Server,Real-time computing,Unavailability,Execution time,Batch processing,Distributed computing |
DocType | Citations | PageRank |
Conference | 9 | 0.63 |
References | Authors | |
34 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunqi Zhang | 1 | 232 | 9.91 |
| George Prekas | 2 | 159 | 6.43 |
| Giovanni Matteo Fumarola | 3 | 9 | 0.63 |
| Marcus Fontoura | 4 | 1116 | 61.74 |
| Iñigo Goiri | 5 | 1039 | 49.27 |
| Ricardo Bianchini | 6 | 2706 | 172.32 |