Name
Abstract | ||
|---|---|---|
Due to the limitation of resources, preemption frequently occurs in almost all the commercial cloud platforms, such as Google cluster and Amazon cluster. Since preemption can ensure that once the system is in heavy workload, high-priority tasks will be executed primarily and at the same time, some low-priority tasks will be killed immediately. Then when more resources are available, the killed tasks will restart to execute. Especially, during the peak time, some low-priority tasks could possibly be preempted and restarted repeatedly resulting in much more consuming precious resources including CPU cores, RAM and hard drives. Thanks to the checkpoint technology, it provides an efficient solution to addressing the preemption issue. But checkpoint technology has limitations, e.g., making checkpoint frequently will add redundant overhead to the cluster and cause I/O congestion. In this paper, by leveraging checkpoint technology, we designed a novel approach to improving the performance of shared clusters. Specifically, by checking the occupancy of resources periodically, making decisions to checkpoint or not and checkpointing for certain tasks, our method can reduce unnecessary checkpoints and exalt the performance of the whole cloud, especially tasks with low-priority. Extensive simulation experiments injecting tasks following the Google cloud trace logs were conducted to validate the superiority of our approach by comparing it with some baselines. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICPADS.2016.132 | 2016 IEEE 22ND INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED SYSTEMS (ICPADS) |
Keywords | Field | DocType |
Cloud Computing, Shared Cluster, Priority, Check-point, Occupancy of Resources | Cluster (physics),Preemption,Workload,Computer science,Real-time computing,Multi-core processor,Operating system,Distributed computing,Cloud computing | Conference |
ISSN | Citations | PageRank |
1521-9097 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiyang Shao | 1 | 0 | 0.34 |
| Xiaomin Zhu | 2 | 921 | 100.31 |
| Weidong Bao | 3 | 36 | 8.51 |
| Wen Zhou | 4 | 3 | 1.38 |
| Wenhua Xiao | 5 | 26 | 5.51 |