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Reviewer #2: This paper proposes a scheduling mechanism for balancing the loads on virtual machines (VMs) and physical machines (PMs) in a cloud computing setting. The task scheduling includes several novel aspects: a novel heuristic VM to PM mapping, a bio-geography based optimization among VMs, and a banker's algorithm based scheduler.

The performance evaluation is reasonable and includes comparisons with genetic algorithms, MinMin, MaxMin, and Round Robin scheduling as well as TBSLB-PSO.

A potential drawback of the proposed approach is that the convergence time and make span are larger than the benchmarks for small VM numbers and small problem dimensions. For large problems, the proposed approach is clearly better. But how could the proposed approach be improved for small problem dimensions and small VM numbers?

The proposed approach gives a fine-grained allocation of tasks to VMs and PMs. How does this fine-grained allocation affect latency? Recently, there has been a push to reduce latency so as to enable ultra-low latency services, see e.g., Chowdhury, et al. "UNiS: A user-space non-intrusive workflow-aware virtual network function scheduler." 2018 14th International Conference on Network and Service Management (CNSM). IEEE, 2018.Xiang, et al. "Reducing latency in virtual machines: Enabling tactile internet for human-machine co-working." IEEE Journal on Selected Areas in Communications 37.5 (2019): 1098-1116. Zhang, et al. "Virtualized network coding functions on the Internet." 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2017. Zhang,  et al. "HyperVDP: High-performance virtualization of the programmable data plane." IEEE Journal on Selected Areas in Communications 37.3 (2019): 556-569. Would such latency reducing schemes be compatible
with the proposed traffic load scheduling mechanism?



Reviewer #3: Overall the paper is hard to read and hard to follow from the idea flow point of view. There is no clear problem definition as well as no clear "original contribution" discussion. Please bear in mind that the "average" reader does not necessarily have to be a cloud or a LB specialist since the "publication venue" is not a niche conference.

The general impression is that 3 previous research results (step1, 2 and 3) are put together and somehow a journal paper should be born. In the current form the paper "does not fly" since it has no head and no tail.

The paper states in the Introduction that "In this paper we propose a three step approach to handle the problem." but the problem is not clearly defined before making this statement.

When the authors claim e.g. "optimal mapping VM onto PM" they have to provide the proof and also discuss it thoroughly.

All acronyms should be explained/defined when they first appear within the paper and not only at the end of the paper.

Starting with the first sentence in the Abstract (which by the way makes no sense), the paper is in need of a thorough review from the language point of view. Below are some examples of sentences that should be rephrased (by all means the list is not complete):
- "Data  stored  in  IBR  are  retrieved  by  the proposed approach where needed and strengthen our approach to make the appropriate decision"
- "A cloud consists of a number of Data Centers (DCs)around the globe, which cooperate on the internet basis. "
- "Due to the nature of LB problem in clouds, and the involvement of multi targets in LB, it could be deduced that optimization approaches in practice outperform other  gradient-based  [19]. "
- "Obviously, there would not be feasible solution that minimizes all the objective functions simultaneously, thereof,  we  focus  on  Pareto  optimal  solution ..."


The reference list should be alphabetically ordered. Also please review the reference information for correct editing (see e.g. [55])
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