[sharelatex-git-integration Best effort strategy and virtual load for asynchronous...

[loba-papers.git] / loba-besteffort / review.tex

diff --git a/loba-besteffort/review.tex b/loba-besteffort/review.tex
index 465dd0886d75ab68766f0d052b8bbf7cf27c6363..a9b9591404c087c06a443e2fb632343a996d60fa 100644 (file)
--- a/loba-besteffort/review.tex
+++ b/loba-besteffort/review.tex
@@ -49,8 +49,8 @@ Nevertheless, the paper is suitable for publication.}
 
 
 \textbf{We agree with the reviewer that it would be interesting to compare the algorithm's performances with other relevant existing works in the literature.
 
 
 \textbf{We agree with the reviewer that it would be interesting to compare the algorithm's performances with other relevant existing works in the literature.

-As reported in the paper, the Bertsekas and Tsitsiklis' algorithm is, to our knowledge, the closest work to the one we propose. The focus and the load balancing models addressed in [7, 9, 24] are different: The reference [7] considers partially asynchronous and static integer load balancing in homogeneous networks. The authors' work is more on the theoretical side of the spectrum, and no experimental results
-are presented to validate their approach. In [9], the authors deal with dynamic networks where communication links between the resources of the network are intermittent. The work in [24] investigate the problem of allocating non divisible load applications on heterogeneous platforms with the goal of response time minimization of users' jobs.}
+As reported in the paper, the Bertsekas and Tsitsiklis' algorithm is, to our knowledge, the closest work to the one we propose. The focus and the load balancing models addressed in [7, 9, 24] are different: Reference [7] considers partially asynchronous and static integer load balancing in homogeneous networks. The authors' work is more on the theoretical side of the spectrum, and no experimental results
+are presented to validate their approach. In [9], the authors deal with dynamic networks where communication links between the resources of the network are intermittent. The work in [24] investigates the problem of allocating non divisible load applications on heterogeneous platforms and it also aims at minimizing the response time of users' jobs.}

 
 \textbf{
 We have tried to perform a second reading and check our paper as well as we could.
 
 \textbf{
 We have tried to perform a second reading and check our paper as well as we could.


@@ -67,7 +67,7 @@ The experiments are convincing and I like very much the discussion about the dat
 \subsection*{Request 1: My only concern is the section 5.2. I did not understand clearly what is this k-factor. The authors say, "Roughly speaking...", I do not what a fuzzy explanation but I need a correct, precise and operational description of that aspect of the work. I think the authors should present a clear explanation of what they do.     
 }
 
 \subsection*{Request 1: My only concern is the section 5.2. I did not understand clearly what is this k-factor. The authors say, "Roughly speaking...", I do not what a fuzzy explanation but I need a correct, precise and operational description of that aspect of the work. I think the authors should present a clear explanation of what they do.     
 }
 

-\textbf{The weighting system parameter k is introduced to orchestrate the right balance between the topology structure and the CCR values of the deployed application. Indeed, to speedup the convergence time of the load balancing process, we face a difficult trade-off to choose an appropriate amount of load to send between node neighbors upon load imbalance detection. On the one hand, if k is small, we expect faster convergence time for sparsely connected application and large CCR values. On the other hand, for strongly connected applications and small CCR values, a large value of k will enable us to better balance the load locally and therefore minimize the number of iterations toward the global equilibrium. In the experiments section, we observe that choosing k in 1,2 or 4, leads to good results for the considered CCR values and the targeted topology structures: a line, a torus, and an hypercube.
+\textbf{The weighting system parameter k is introduced to orchestrate the right balance between the topology structure and the CCR values of the deployed application. Indeed, to speedup the convergence time of the load balancing process, we face a difficult trade-off to choose an appropriate amount of load to send between node neighbors upon load imbalance detection. On the one hand, if k is small, we expect faster convergence times for sparsely connected applications and large CCR values. On the other hand, for strongly connected applications and small CCR values, a large value of k will enable us to better balance the load locally and therefore minimize the number of iterations toward the global equilibrium. In the experiments section, we observe that choosing k in 1,2 or 4, leads to good results for the considered CCR values and the targeted topology structures: a line, a torus, and an hypercube.

 }
 
 
 }