The limited performance benefits of migrating active processes for load sharing
SIGMETRICS '88 Proceedings of the 1988 ACM SIGMETRICS conference on Measurement and modeling of computer systems
The Influence of Different Workload Descriptions on a Heuristic Load Balancing Scheme
IEEE Transactions on Software Engineering
Transparent process migration: design alternatives and the sprite implementation
Software—Practice & Experience
Implementing global memory management in a workstation cluster
SOSP '95 Proceedings of the fifteenth ACM symposium on Operating systems principles
Adaptive page replacement based on memory reference behavior
SIGMETRICS '97 Proceedings of the 1997 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Exploiting process lifetime distributions for dynamic load balancing
ACM Transactions on Computer Systems (TOCS)
Coordinating parallel processes on networks of workstations
Journal of Parallel and Distributed Computing
Computer architecture (2nd ed.): a quantitative approach
Computer architecture (2nd ed.): a quantitative approach
Availability and utility of idle memory in workstation clusters
SIGMETRICS '99 Proceedings of the 1999 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Improved Strategies for Dynamic Load Balancing
IEEE Concurrency
Job Characteristics of a Production Parallel Scientivic Workload on the NASA Ames iPSC/860
IPPS '95 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Gang Scheduling with Memory Considerations
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
Reducing DRAM Latencies with an Integrated Memory Hierarchy Design
HPCA '01 Proceedings of the 7th International Symposium on High-Performance Computer Architecture
Dynamic Load Sharing With Unknown Memory Demands in Clusters
ICDCS '01 Proceedings of the The 21st International Conference on Distributed Computing Systems
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The cluster system we consider for load sharing is a compute farm which is a pool of networked server nodes providing high-performance computing for CPU-intensive, memory intensive, and I/O active jobs in a batch mode. Existing resource management systems mainly target at balancing the usage of CPU loads among server nodes. With the rapid advancement of CPU chips, memory and disk access speed improvements significantly lag behind advancement of CPU speed, increasing the penalty for data movement, such as page faults and I/O operations, relative to normal CPU operations. Aiming at reducing the memory resource contention caused by page faults and I/O activities, we have developed and examined load sharing policies by considering effective usage of global memory in addition to CPU load balancing in clusters. This paper describes memory demands are known in advance or predictable. Conducting different groups of trace-driven simulations, we show that our proposed policies can effectively improve overall job execution performance by well utilizing both CPU and memory resources.