Effective Load Sharing on Heterogeneous Networks of Workstations

We consider networks of workstations which are not only timesharing, 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.