Incorporating job migration and network RAM to share 
cluster memory resources  

Li. Xiao, Xiaodong Zhang, and S. A. Kurbricht

Proceedings of the 9th IEEE International Symposium on
High Performance Distributed Computing (HPDC-9) 
Pittsburgh, Pennsylvania, August 1-4, 2000, pp. 71-78. 
 
Abstract 

Job migrations and network RAM are two major approaches for effectively
using global memory resources in a workstation cluster,
aiming at reducing page faults in
each local workstation and improving the overall performance of
cluster computing. Using either remote executions
or preemptive migrations, a load sharing system is able to migrate a job from
a workstation without sufficient memory space
to a lightly loaded workstation with large idle
memory space for the migrated job. In a network RAM system, if a job
cannot find sufficient memory space for its working sets, it will
utilize idle memory space from other workstations in the cluster through
remote paging. Conducting trace-driven simulations, we have compared
the performance and trade-offs of the two approaches and their impacts
on job execution time and cluster scalability.
Our study indicates that job-migration-based load sharing schemes
are able to balance executions of jobs in a cluster well,
while network RAM are able to satisfy data-intensive jobs which
may not be migratable by
sharing all the idle memory resources in a cluster.
We also show that a network RAM cluster of workstations is scalable
only if the network is sufficiently fast.
Finally, we propose an improved
load sharing scheme by combining job migrations
with the network RAM for cluster computing. This scheme uses remote execution
to initially allocate a job to the most lightly loaded workstation
and, if necessary, network RAM to provide
a larger memory space for the job than would be available otherwise.
The improved scheme has the merits of both job migrations and network
RAM. Our experiments show its effectiveness and scalability for
cluster computing.

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