![](bg8.png)
explain that from the fact that the system, during the
latency period, accumulates more waiting jobs before
the complete switching-on of the servers.
9 CONCLUSION
The aim of this study is to develop a model for effi-
cient management of a data center that takes into ac-
count server latency and minimizes energy consump-
tion and Quality of Service (QoS) costs. The model
uses a discrete-time Markov decision process, with
job arrivals and service rates modeled by a discrete
probability distribution estimated from real data. To
account for switching-on latency, it is assumed that
all servers have the same constant latency period of k
units of time. The optimal control policy is computed
using the value iteration algorithm, and is used to im-
plement a Dynamic Power Management strategy that
balances energy consumption and performance. De-
spite the large size of the model (which is an expo-
nential of k), the experimental and theoretical results
demonstrate that increasing buffer size can lead to sig-
nificant energy savings when the latency is higher.
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