چکیده (انگلیسی):

Cloud Computing is a paradigm that delivers services by
providing an access to wide range of shared resources which are hosted in
cloud data centers. One of the recent challenges in this paradigm is to
enhance the energy efficiency in these data centers. In this study, a model that
identifies common patterns for the jobs submitted to the cloud is proposed.
This model is able to predict the type of the job submitted and accordingly,
the set of users’ jobs is classified into four subsets. Each subset contains jobs
that have similar requirements. In addition to the jobs’ common pattern and
requirements, the users’ history is considered in the jobs’ type prediction
model. The goal of job classification is to find a way to propose useful
strategy that helps to improve power efficiency. Based on the process of jobs’
classification, the best fit virtual machine is allocated to each job. Then, the
virtual machines are placed on the physical machines according to a novel
strategy, called Mixed Type Placement strategy. The core idea of the
proposed strategy is to place virtual machines of the jobs of different types in
the same physical machine whenever possible. The placement process is
based on Multi Choice Knapsack Problem which is a generalization of the
classical Knapsack Problem. This is because different types of jobs do not
intensively use the same compute or storage resources in the physical
machine. This strategy minimizes the number of active physical machines
which, in turn, leads to major reduction in the total energy consumption in the
data center. The total execution time and the cost of executing the jobs
submitted are considered in the placement process. To evaluate the
performance of the proposed strategy, the CloudSim simulator is used with a
real workload trace to simulate the cloud computing environment. The results
show that the proposed strategy outperform both Genetic Algorithm and
Round Robin from energy efficiency perspective.