Dr. Muhammad Zakarya, Lecturer of Computer Science, Published a
Research Paper of Impact Factor 2.239 with Title "Modelling Resource
Heterogeneities in Cloud Simulations and Quantifying their Accuracy"
Abstract
Simulations are often used to evaluate the performance of various
scheduling and migration techniques in the context of large computing systems
such as clouds and datacenters. To ensure that simulations match the real
platform as close as possible, plausible assumptions and accurate statistical
models are used in designing simulations; and that could also offer accurate
results. However, it is not always possible that similar numerical results
would also be achievable in a real cloud test-bed. The reason is that a
simulator only abstracts a model and, hence, a system; but does not always
reflect the real world scenarios. Therefore, the solution of any research
problem using numerical simulation (experimentation) is not just to find a
result, but also to ensure the quality and accuracy of the estimated results.
CloudSim is largely used in the cloud research community to evaluate the
performance of various resource allocation and migration policies. However,
resources such as CPU, memory and application heterogeneities are not modelled
yet. Moreover, its accuracy is rarely addressed. In this paper, we: (i)
describe an extension to CloudSim that offers support for resource (CPU) and
application heterogeneities; and (ii) demonstrate several techniques that could
be used to measure the accuracy of results obtained in simulations,
particularly, in the extended CloudSim. Based on our evaluation, we suggest
that the accuracy and precision of the extended version of the CloudSim
simulator may be as high as ∼ 98.63% for certain energy and performance efficient resource
allocation and consolidation with migration policies in heterogeneous
datacenters.
