Computational Intelligence - February 2015 - 33
Efficient Heuristics
for Profit Optimization
of Virtual Cloud Brokers
image licensed by ingram publishing
Sergio Nesmachnow
and Santiago Iturriaga
Centro de Cálculo,
Universidad de la República,
Montevideo, URUGUAY
Bernabé Dorronsoro
SnT, University of Luxembourg,
LUXEMBOURG
Digital Object Identifier 10.1109/MCI.2014.2369893
Date of publication: 14 January 2015
1556-603x/15©2015ieee
Abstract-This article introduces a new kind of broker for cloud computing,
whose business relies on outsourcing virtual machines (VMs) to its customers. More specifically, the broker owns a number of reserved instances of different VMs from several cloud providers and offers them to its customers in
an on-demand basis, at cheaper prices than those of the cloud providers. The
essence of the business resides in the large difference in price between ondemand and reserved VMs. We define the Virtual Machine Planning Problem,
an optimization problem to maximize the profit of the broker. We also propose a number of efficient smart heuristics (seven two-phase list scheduling
heuristics and a reordering local search) to allocate a set of VM requests from
customers into the available pre-booked ones, that maximize the broker
earnings. We perform experimental evaluation to analyze the profit and quality of service metrics for the resulting planning, including a set of 400 problem instances that account for realistic workloads and scenarios using real
data from cloud providers.
february 2015 | ieee Computational intelligenCe magazine
33
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Computational Intelligence - February 2015 - Cover3
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