Failure-aware checkpointing in fine-grained cycle sharing systems
Proceedings of the 16th international symposium on High performance distributed computing
Predict task running time in grid environments based on CPU load predictions
Future Generation Computer Systems
Multi-state grid resource availability characterization
GRID '07 Proceedings of the 8th IEEE/ACM International Conference on Grid Computing
Scheduling on the Grid via multi-state resource availability prediction
GRID '08 Proceedings of the 2008 9th IEEE/ACM International Conference on Grid Computing
FALCON: a system for reliable checkpoint recovery in shared grid environments
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Availability Prediction Based Replication Strategies for Grid Environments
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Toward high performance computing in unconventional computing environments
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
| Hi-index | 0.00 |
Fine-Grained Cycle Sharing (FGCS) systems aim at utilizing the large amount of computational resources available on the Internet. In FGCS, host computers allow guest jobs to utilize the CPU cycles if the jobs do not significantly impact local host users. Such resources are generally provided voluntarily and their availability fluctuates highly. Guest jobs may fail unexpectedly, as resource becomes unavailable. We present empirical studies on the detection and predictability of resource availability in FGCS systems. A multi-state availability model is derived from a study of resource behavior. The model combines generic hardwaresoftware failures with domain-specific resource behavior in FGCS. To understand the predictability, we traced resource availability in a production FGCS system for three months. We found that the daily patterns of resource availability are comparable to those in recent history. This observation suggests the feasibility of predicting future resource availability, which can be applied for proactive management of guest jobs.