4 research outputs found
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
Secure and Privacy-Preserving Automated Machine Learning Operations into End-to-End Integrated IoT-Edge-Artificial Intelligence-Blockchain Monitoring System for Diabetes Mellitus Prediction
Diabetes Mellitus, one of the leading causes of death worldwide, has no cure
to date and can lead to severe health complications, such as retinopathy, limb
amputation, cardiovascular diseases, and neuronal disease, if left untreated.
Consequently, it becomes crucial to take precautionary measures to
avoid/predict the occurrence of diabetes. Machine learning approaches have been
proposed and evaluated in the literature for diabetes prediction. This paper
proposes an IoT-edge-Artificial Intelligence (AI)-blockchain system for
diabetes prediction based on risk factors. The proposed system is underpinned
by the blockchain to obtain a cohesive view of the risk factors data from
patients across different hospitals and to ensure security and privacy of the
user's data. Furthermore, we provide a comparative analysis of different
medical sensors, devices, and methods to measure and collect the risk factors
values in the system. Numerical experiments and comparative analysis were
carried out between our proposed system, using the most accurate random forest
(RF) model, and the two most used state-of-the-art machine learning approaches,
Logistic Regression (LR) and Support Vector Machine (SVM), using three
real-life diabetes datasets. The results show that the proposed system using RF
predicts diabetes with 4.57% more accuracy on average compared to LR and SVM,
with 2.87 times more execution time. Data balancing without feature selection
does not show significant improvement. The performance is improved by 1.14% and
0.02% after feature selection for PIMA Indian and Sylhet datasets respectively,
while it reduces by 0.89% for MIMIC III
ESCOVE: Energy-SLA-Aware Edge–Cloud Computation Offloading in Vehicular Networks
The vehicular network is an emerging technology in the Intelligent Smart Transportation era. The network provides mechanisms for running different applications, such as accident prevention, publishing and consuming services, and traffic flow management. In such scenarios, edge and cloud computing come into the picture to offload computation from vehicles that have limited processing capabilities. Optimizing the energy consumption of the edge and cloud servers becomes crucial. However, existing research efforts focus on either vehicle or edge energy optimization, and do not account for vehicular applications’ quality of services. In this paper, we address this void by proposing a novel offloading algorithm, ESCOVE, which optimizes the energy of the edge–cloud computing platform. The proposed algorithm respects the Service level agreement (SLA) in terms of latency, processing and total execution times. The experimental results show that ESCOVE is a promising approach in energy savings while preserving SLAs compared to the state-of-the-art approach