Generating representative Web workloads for network and server performance evaluation
SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
User-level process checkpoint and restore for migration
ACM SIGOPS Operating Systems Review
IEEE/ACM Transactions on Networking (TON)
Information and control in gray-box systems
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
ICPP '00 Proceedings of the Proceedings of the 2000 International Conference on Parallel Processing
The dawning of the autonomic computing era
IBM Systems Journal
Request Distribution-Aware Caching in Cluster-Based Web Servers
NCA '04 Proceedings of the Network Computing and Applications, Third IEEE International Symposium
Benefits of Global Grid Computing for Job Scheduling
GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
Modeling and Taming Parallel TCP on the Wide Area Network
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
Designing an Enhanced PC Cluster System for Scalable Network Services
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 2
Dynamic Black-Box Performance Model Estimation for Self-Tuning Regulators
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
MOSIX: how Linux clusters solve real world problems
ATEC '00 Proceedings of the annual conference on USENIX Annual Technical Conference
Architecture of the IBM system/360
IBM Journal of Research and Development
Dynamic information-based scalable hashing on a cluster of web cache servers
Concurrency and Computation: Practice & Experience
Hi-index | 0.00 |
Adaptive clustering aims at improving cluster utilization for varying load and traffic patterns. Locality-based least-connection with replication (LBLCR) scheduling that comes with Linux is designed to help improve cluster utilization through adaptive clustering. A key issue with LBLCR, however, is that cluster performance depends much on a single threshold value that is used to determine adaptation. Once set, the threshold remains fixed regardless of the load and traffic patterns. If a cluster of PCs is to adapt to different traffic patterns for high utilization, a good threshold has to be selected and used dynamically. We present in this report an adaptive clustering framework that autonomously learns and adapts to different load and traffic patterns at runtime with no administrator intervention. Cluster is configured once and for all. As the patterns change the cluster automatically expands/contracts to meet the changing demands. At the same time, the patterns are proactively learned that when similar patterns emerge in the future, the cluster knows what to do to improve utilization. We have implemented this autonomous learning method and compared with LBLCR using published Web traces. Experimental results indicate that our autonomous learning method shows high performance scalability and adaptability for different patterns. On the other hand LBLCR-based clustering suffers from performance scalability and adaptability for different traffic patterns since it is not designed to obtain good threshold values and use them at runtime.