Dynamic Cluster Resource Allocations for Jobs with Known and Unknown Memory Demands
IEEE Transactions on Parallel and Distributed Systems
Performance Analysis of a Distributed Question/Answering System
IEEE Transactions on Parallel and Distributed Systems
The Journal of Supercomputing
Journal of Parallel and Distributed Computing
Memory latency consideration for load sharing on heterogeneous network of workstations
Journal of Systems Architecture: the EUROMICRO Journal
Design and analysis of a load balancing strategy in data grids
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Load sharing in Call Server clusters
Computer Communications
Dynamic load balancing for I/O-intensive applications on clusters
ACM Transactions on Storage (TOS)
Analysis of coordinated load sharing for large distributed systems
International Journal of Computers and Applications
A survey of task mapping on production grids
ACM Computing Surveys (CSUR)
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We consider networks of workstations, which are not only time-sharing, but also heterogeneous with a large variation in the computing power and memory capacities of different workstations. Many load-sharing schemes mainly target sharing CPU resources, and have been intensively evaluated in homogeneous distributed environments. However, the penalties of data accesses and movement in modern computer systems, such as page faults, have grown to the point where the overall performance of distributed systems cannot be further improved without serious considerations concerning memory resources in the design of load sharing policies. Considering both system heterogeneity and effective usage of memory resources, we design and evaluate load-sharing policies in order to minimize both CPU idle times and the number of page faults in heterogeneous distributed systems. Conducting trace-driven simulations, we show that load sharing policies considering both CPU and memory resources are robust and effective in heterogeneous systems. We also show that the functionality and the nature of load sharing policies are quite independent on several memory demand distributions of workloads.