Abstract:
In computing clusters, there are di erent performance metrics, which often appear to be con-
icting while being attempted to be optimized. For having such con
icting cases along with
experiencing existence of heterogeneous environment, it is often di cult for the cluster administrators
to select the right number and right combination of machines. As a remedy to
this situation, in this thesis, we develop a technique through which cluster administrators can
select the right set of machines to enhance cluster performance. In our solution, we integrate
both cooling energy consumption and empirical performance characterization of clusters. To
the best of our knowledge, existing studies do not integrate these two simultaneously in solving
many-objective optimization problem for clusters. We exploit a many-objective optimization
approach based on NSGA-III algorithm to solve our cluster problem. Our technique attempts
to simultaneously optimize many objectives such as computation time, computation energy,
cooling energy, and utilization. Subsequently, we demonstrate through both real experimentation
and simulation that our technique mostly performs better than optimization approaches
existing in the literature. In this study, we integrate cooling energy while evaluating cluster
performance. Cooling energy consumption is one of the most signi cant parts of total energy
consumed by clusters and similar distributed systems. However, little e ort has been spent so
far to integrate the cooling energy in simulators that are used for simulating the distributed systems.
Therefore, we also perform integration of cooling energy consumption in a widely-known
simulator of distributed systems namely SimGrid.
