A Queuing Model to Analyze Data Center Performances in a Cloud Computing Environment | Chapter 03 | Advances in Mathematics and Computer Science Vol. 4
In the last decades cloud computing has
been the focus of a lot of research in both academic and industrial fields,
however, implementation-related issues have been developed and have received
more attention than performance analysis which is an important aspect of cloud
computing and it is of crucial interest for both cloud providers and cloud users.
Successful development of cloud computing paradigm necessitates accurate
performance evaluation of cloud data centers. Because of the nature of cloud
centers and the diversity of user requests, an exact modeling of cloud centers
is not practicable; in this work we report an approximate analytical model
based on an approximate Markov chain model for performance evaluation of a
cloud computing center. Due to the nature of the cloud environment, we
considered, based on queuing theory, a MMPP task arrivals, a general service
time for requests as well as large number of physical servers and a finite
capacity. This makes our model more flexible in terms of scalability and
diversity of service time. We used this model in order to evaluate the
performance analysis of cloud server farms and we solved it to obtain accurate
estimation of the complete probability distribution of the request response
time and other important performance indicators such as: the Mean number of
Tasks in the System, the distribution of Waiting Time, the Probability of
Immediate Service, the Blocking Probability and Buffer Size
Author(s) Details
Mohamed Hanini
FST, Hassan 1st University,
Settat, Morocco.
IR2M laboratory, FST, Hassan
1st University, Settat, Morocco.
Fatima Oumellal
FST, Hassan 1st University,
Settat, Morocco.
Abdelkrim Haqiq
FST, Hassan 1st University,
Settat, Morocco.
IR2M laboratory, FST, Hassan
1st University, Settat, Morocco.
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