Implementing global memory management in a workstation cluster
SOSP '95 Proceedings of the fifteenth ACM symposium on Operating systems principles
Memory Hierarchy Considerations for Cost-Effective Cluster Computing
IEEE Transactions on Computers
High Performance Cluster Computing: Architectures and Systems
High Performance Cluster Computing: Architectures and Systems
Dynamic Load Balancing on Web-Server Systems
IEEE Internet Computing
Memory Management for Scalable Web Data Servers
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
The Impact of Data Placement on Memory Management for Multi-Server OODBMS
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Adaptive Load Sharing for Clustered Digital Library Servers
HPDC '98 Proceedings of the 7th IEEE International Symposium on High Performance Distributed Computing
Dodo: A User-Level System for Exploiting Idle Memory in Workstation Clusters
HPDC '99 Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
Cooperative caching: using remote client memory to improve file system performance
OSDI '94 Proceedings of the 1st USENIX conference on Operating Systems Design and Implementation
Content-Based distribution for load sharing in locally clustered web servers
ADVIS'04 Proceedings of the Third international conference on Advances in Information Systems
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The concept of network memory was introduced for the efficient exploitation of main memory in a cluster. Network memory can be used to speed up applications that frequently access large amount of disk data. In this paper, we present a memory management algorithm that does not require prior knowledge of access patterns and that is practical to implement under the web server cluster. In addition, our scheme has a good user response time for various access distributions of web documents. Through a detailed simulation, we evaluate the performance of our memory management algorithms.