Comments on ``Design and performance evaluation of load distribution strategies for multiple loads on heterogeneous linear daisy chain networks''

Computer Science – Distributed – Parallel – and Cluster Computing

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

Min, Veeravalli, and Barlas proposed strategies to minimize the overall execution time of one or several divisible loads on a heterogeneous linear network, using one or more installments. We show on a very simple example that the proposed approach does not always produce a solution and that, when it does, the solution is often suboptimal. We also show how to find an optimal scheduling for any instance, once the number of installments per load is given. Finally, we formally prove that under a linear cost model, as in the original paper, an optimal schedule has an infinite number of installments. Such a cost model can therefore not be sed to design practical multi-installment strategies.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Comments on ``Design and performance evaluation of load distribution strategies for multiple loads on heterogeneous linear daisy chain networks'' does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Comments on ``Design and performance evaluation of load distribution strategies for multiple loads on heterogeneous linear daisy chain networks'', we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Comments on ``Design and performance evaluation of load distribution strategies for multiple loads on heterogeneous linear daisy chain networks'' will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-443722

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.