10,274 research outputs found
Cloud WorkBench - Infrastructure-as-Code Based Cloud Benchmarking
To optimally deploy their applications, users of Infrastructure-as-a-Service
clouds are required to evaluate the costs and performance of different
combinations of cloud configurations to find out which combination provides the
best service level for their specific application. Unfortunately, benchmarking
cloud services is cumbersome and error-prone. In this paper, we propose an
architecture and concrete implementation of a cloud benchmarking Web service,
which fosters the definition of reusable and representative benchmarks. In
distinction to existing work, our system is based on the notion of
Infrastructure-as-Code, which is a state of the art concept to define IT
infrastructure in a reproducible, well-defined, and testable way. We
demonstrate our system based on an illustrative case study, in which we measure
and compare the disk IO speeds of different instance and storage types in
Amazon EC2
Computing server power modeling in a data center: survey,taxonomy and performance evaluation
Data centers are large scale, energy-hungry infrastructure serving the
increasing computational demands as the world is becoming more connected in
smart cities. The emergence of advanced technologies such as cloud-based
services, internet of things (IoT) and big data analytics has augmented the
growth of global data centers, leading to high energy consumption. This upsurge
in energy consumption of the data centers not only incurs the issue of surging
high cost (operational and maintenance) but also has an adverse effect on the
environment. Dynamic power management in a data center environment requires the
cognizance of the correlation between the system and hardware level performance
counters and the power consumption. Power consumption modeling exhibits this
correlation and is crucial in designing energy-efficient optimization
strategies based on resource utilization. Several works in power modeling are
proposed and used in the literature. However, these power models have been
evaluated using different benchmarking applications, power measurement
techniques and error calculation formula on different machines. In this work,
we present a taxonomy and evaluation of 24 software-based power models using a
unified environment, benchmarking applications, power measurement technique and
error formula, with the aim of achieving an objective comparison. We use
different servers architectures to assess the impact of heterogeneity on the
models' comparison. The performance analysis of these models is elaborated in
the paper
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