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On achieving intelligent traffic-aware consolidation of virtual machines in a data center using Learning Automata

Jobava, Akaki; Yazidi, Anis; Oommen, John; Begnum, Kyrre
Journal article, Peer reviewed
Accepted version
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Postprint. Embargo 2019-08-22 (786.7Kb)
URI
https://hdl.handle.net/10642/5989
Date
2018-01
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  • TKD - Institutt for informasjonsteknologi [1040]
Original version
Jobava A, Yazidi A, Oommen J, Begnum KM. On achieving intelligent traffic-aware consolidation of virtual machines in a data center using Learning Automata. Journal of Computational Science. 2017   https://doi.org/10.1016/j.jocs.2017.08.005
Abstract
Unlike the computational mechanisms of the past many decades, that involved individual (extremely

powerful) computers or clusters of machines, Cloud Computing (CC) is becoming increasingly pertinent and

popular. Computing resources such as CPU and storage are becoming cheaper, and the servers themselves are

becoming more powerful. This enables clouds to host more Virtual Machines (VMs). A natural consequence

of this is that many modern-day data centers experience very high internal traffic within the data centers themselves. This is, of course, due to the occurrence of servers that belong to the same tenant, communicating

between themselves. The problem is accentuated when the VM deployment tools are not traffic-aware. In

such cases, the VMs with high mutual traffic often end up being far apart in the data center network, forcing

them to communicate over unnecessarily long distances. The consequent traffic bottlenecks negatively affect

both the performance of the application and the network in its entirety, posing non-trivial challenges for the

administrators of these cloud-based data centers.

The problem, and consequently the solution, can, quite naturally, be compartmentalized into two phases which follow each other. In the first, the task is to consolidate VMs into clusters, where those that commu

nicate with each other fall into the same cluster. The second phase assigns these clusters onto the available

server racks. Both of these phases must be executed in a traffic-aware manner. This paper provides efficient

intelligent solutions for both these phases. First of all, the VMs are consolidated with a VM clustering

algorithm, and this is achieved by utilizing the toolbox involving Learning Automata (LA). By mapping the

clustering problem onto the Graph Partitioning (GP) problem, our LA-based solution successfully reduces

the total communication cost by amounts that range between 34% to 85%. Thereafter, the resulting clusters are assigned to the server racks using a cluster placement algorithm that involves a completely different

intelligent strategy, i.e., one that invokes Simulated Annealing (SA). This phase further reduces the total

cost of communication by amounts that range between 89% to 99%. The analysis and results for different

models and topologies demonstrate that the optimization is done in a fast and computationally-efficient way.
Publisher
Elsevier
Series
Journal of Computational Science;Volume 24
Journal
Journal of Computational Science

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