TR-00-1.ps.Z

Improving distributed workload performance 
by sharing both CPU and memory resources

X. Zhang, Y. Qu, and L. Xiao  

Proceedings of the 20th International Conference on Distributed Computing  
Systems, (ICDCS'00), Taipei, Taiwan, April 2000, pp. 233-241. 

Abstract 

We develop and examine job migration policies by
considering effective usage of global memory in addition
to CPU load sharing in distributed systems. When a node is 
identified for lacking sufficient memory space to serve jobs,
one or more jobs of the node
will be migrated to remote nodes with low memory allocations.
If the memory space is sufficiently large,
the jobs will be scheduled by a CPU-based load sharing policy.
Following the principle of sharing both CPU and memory resources,
we present several load sharing alternatives.
Our objective is to reduce the number of page faults
caused by unbalanced memory allocations for jobs among distributed nodes,
so that overall performance of a distributed system can be
significantly improved. We have conducted trace-driven simulations 
to compare CPU-based load sharing policies with our policies.
We show that our load sharing policies not only improve
performance of memory-bound jobs, but also maintain the same load sharing
quality as the CPU-based policies for
CPU-bound jobs. Regarding remote execution and preemptive migration
strategies, our experiments indicate that a strategy selection in load
sharing is dependent on the amount of memory demand of jobs ---
remote execution is more effective for memory-bound jobs, and
preemptive migration is more effective for CPU-bound jobs.
Our CPU-Memory-based policy using either high performance
or high throughput approach and using the remote execution strategy performs 
the best for both CPU-bound and memory-bound jobs. 

Download simulation source code click here

Download trace data files click here

Please send us a message (zhang@cse.ohio-state.edu) after you download the code/traces. Thanks.

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