In the last decade we noticed a growth on studies regarding energy savings in data centers. The main reasons include political factors such as compliance with global protocols of conscious energy consumption, financial incentives such as tax reduction, and environmentally driven by concerns about sustainability issues such as emission of heat and gases harmful to the ozone layer. Most works aim to reduce the energy consumption of servers and cooling systems. However, network devices comprise also a significant slice of the total Data Center energy consumption, and most studies often neglect that. In this paper, we propose techniques to define flow paths in an SDN-based Data Center network respecting flow bandwidth requirements, while also enabling changing the operation state of network devices to a state of lower energy consumption in order to reduce the total consumption of the network layer. We evaluate the proposed techniques using different ratios of link demand oversubscription in a fat-tree topology with different POD sizes. Results show savings of up to 70% regarding energy consumption in the network layer.
In order to meet the increasing performance demand of applications, the amount of cores in a single chip package has been increasing. However, the heat has been rising at a higher scale, which accelerates the aging process in modern processors. Therefore, wisely balancing the use of resources is important to extend its longevity. Frequency performance stagnates after a certain amount of concurrent threads starts executing. In such cases, the only result is a temperature rise that directly influences the aging process, reducing the processor lifetime. This unbalance between threads can be originated from many factors, which includes the way threads communicate and synchronize. Considering that those characteristics are related to the Parallel Programming Interface (PPI) used to parallelize the application, this work proposes to evaluate three widely used PPIs executing on an embedded multicore. We show that, depending on the characteristic of the application, by only switching from one PPI to another, it is possible to reduce the effects of aging. For that, we have developed a model based on the Arrhenius equation. We show that OpenMP has a lower impact on the processor aging for memory-bound applications: up to 38% and 68% lower than PThreads and MPI, respectively. On the other hand, PThreads presents the lowest impact on the processor aging for CPU-bound applications.
The massive power consumption of data centers has been a recurring concern in current research. In cloud environments, lots of methods are being adopted that aim for energy efficiency. However, although such methods enable the decrease in power consumption, they regularly affect application performance. In this paper, we present a multilevel resource allocation approach towards dynamic network bandwidth at the physical substrate, managing different power-saving states and workload allocation at the cloud infrastructure at the same time employ virtual machine allocation and selection policies at the cloud platform. In order to evaluate our approach, tests were carried out in a simulated environment using scale-out application on a dynamic cloud infrastructure. Results showed that our proposal presents a better balance regarding a more energy-efficient data center with a smaller impact on application performance when compared with other works discussed in the literature.
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