Time-shared Systems: a theoretical treatment
Journal of the ACM (JACM)
Host load prediction using linear models
Cluster Computing
Online Prediction of the Running Time of Tasks
Cluster Computing
A Prediction-Based Real-Time Scheduling Advisor
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Experiences with predicting resource performance on-line in computational grid settings
ACM SIGMETRICS Performance Evaluation Review
Application-Aware Scheduling of a Magnetohydrodynamics Application in the Legion Metasystem
HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
Forecasting network performance to support dynamic scheduling using the network weather service
HPDC '97 Proceedings of the 6th IEEE International Symposium on High Performance Distributed Computing
Design and Evaluation of a Resource Selection Framework for Grid Applications
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
Homeostatic and Tendency-Based CPU Load Predictions
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Adaptive Distributed Computing through Competition
ICCDS '96 Proceedings of the 3rd International Conference on Configurable Distributed Systems
Statistical Properties of Task Running Times in a Global-Scale Grid Environment
CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
Dynamic load balancing experiments in a grid
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid (CCGrid'05) - Volume 2 - Volume 02
Dynamic load balancing for a grid application
HiPC'04 Proceedings of the 11th international conference on High Performance Computing
Trace-based evaluation of job runtime and queue wait time predictions in grids
Proceedings of the 18th ACM international symposium on High performance distributed computing
Task profiling model for load profile prediction
Future Generation Computer Systems
Adaps - A three-phase adaptive prediction system for the run-time of jobs based on user behaviour
Journal of Computer and System Sciences
Journal of Systems and Software
Network-aware meta-scheduling in advance with autonomous self-tuning system
Future Generation Computer Systems
GRID '11 Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing
An adaptive model for online detection of relevant state changes in Internet-based systems
Performance Evaluation
A GridWay-based autonomic network-aware metascheduler
Future Generation Computer Systems
Resource optimization in distributed real-time multimedia applications
Multimedia Tools and Applications
Journal of Grid Computing
On the Improvement of Grid Resource Utilization: Preventive and Reactive Rescheduling Approaches
Journal of Grid Computing
Behavioral model for cloud aware load and power management
Proceedings of the 2013 international workshop on Hot topics in cloud services
Improving cloud infrastructure utilization through overbooking
Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference
Data clustering based on correlation analysis applied to highly variable domains
Computer Networks: The International Journal of Computer and Telecommunications Networking
Detecting correlation between server resources for system management
Journal of Computer and System Sciences
Cloudy with a Chance of Load Spikes: Admission Control with Fuzzy Risk Assessments
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
Hi-index | 0.00 |
Grid computing is an emerging technology by which huge numbers of processors over the world create a global source of processing power. Their collaboration makes it possible to perform computations that are too extensive to perform on a single processor. On a grid, processors may connect and disconnect at any time, and the load on the computers can be highly bursty. These characteristics raise the need for the development of techniques that make grid applications robust against the dynamics of the grid environment. In particular, applications that use significant amounts of processor power for running jobs need effective predictions of the expected computation times of those jobs on remote hosts. Currently, there are no effective prediction methods available that cope with the ever-changing running times of jobs on a grid environment. Motivated by this, we develop the Dynamic Exponential Smoothing (DES) method to predict running times in a grid environment. The main idea behind DES is that it dynamically adapts its prediction strategy to the height of the fluctuations in those running times. We have performed extensive experiments in a real global-scale grid environment to compare the effectiveness of DES. The results demonstrate that DES strongly and consistently outperforms existing prediction methods.