Ecope: Task Aware Workload Elastic Scheduling and Customization for Infrastructure

2018 ◽  
Vol 6 (6) ◽  
pp. 52
Author(s):  
Shifaliya Banu ◽  
M. Prabakar
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Large Data ◽  
Functional Requirement ◽  
The Past ◽  
Energy Proportionality ◽  
Content Processing ◽  
Elastic Scheduling ◽  
Data Centres ◽  
File Processing

In the past several years, the development in non functional requirement such as CPU and memory has been    done. Due to the workload characteristics the energy efficiency of non functional component has made a large coverage. We develop Ecope to attain energy proportionality for different methods of services of virtual machine in data centres’ decrease non functional energy for servers in large data centers. Demonstrate three input methods to illustrate our concept to real world services such as file processing, backend services and content processing. These services are applying on virtual machine in large data centers. In short, our aim is to recognize the preeminent non functional configuration among various workloads.

Algorithmic fairness in credit scoring

2021 ◽  
Vol 37 (3) ◽  
pp. 585-617
Author(s):  
Teresa Bono ◽  
Karen Croxson ◽  
Adam Giles
Keyword(s):  
Machine Learning ◽  
Credit Scoring ◽  
Large Data ◽  
Error Rates ◽  
The Past ◽  
Ensemble Machine Learning ◽  
Hidden Patterns ◽  
Credit Scoring Model ◽  
Distributional Impacts ◽  
Modelling Approach

Abstract The use of machine learning as an input into decision-making is on the rise, owing to its ability to uncover hidden patterns in large data and improve prediction accuracy. Questions have been raised, however, about the potential distributional impacts of these technologies, with one concern being that they may perpetuate or even amplify human biases from the past. Exploiting detailed credit file data for 800,000 UK borrowers, we simulate a switch from a traditional (logit) credit scoring model to ensemble machine-learning methods. We confirm that machine-learning models are more accurate overall. We also find that they do as well as the simpler traditional model on relevant fairness criteria, where these criteria pertain to overall accuracy and error rates for population subgroups defined along protected or sensitive lines (gender, race, health status, and deprivation). We do observe some differences in the way credit-scoring models perform for different subgroups, but these manifest under a traditional modelling approach and switching to machine learning neither exacerbates nor eliminates these issues. The paper discusses some of the mechanical and data factors that may contribute to statistical fairness issues in the context of credit scoring.

BIG DATA PROCESSING IN THE DIGITALIZATION OF ENTERPRISE ACTIVITIES

2021 ◽  
Vol 75 (3) ◽  
pp. 76-82
Author(s):  
G.T. Balakayeva ◽  
◽  
D.K. Darkenbayev ◽  
M. Turdaliyev ◽  
◽  
...  
Keyword(s):  
Social Networks ◽  
Growth Rate ◽  
Big Data ◽  
Data Processing ◽  
New Technologies ◽  
Large Data ◽  
Modern Society ◽  
Information Flows ◽  
Professional Activities ◽  
The Past

The growth rate of these enterprises has increased significantly in the last decade. Research has shown that over the past two decades, the amount of data has increased approximately tenfold every two years - this exceeded Moore's Law, which doubles the power of processors. About thirty thousand gigabytes of data are accumulated every second, and their processing requires an increase in the efficiency of data processing. Uploading videos, photos and letters from users on social networks leads to the accumulation of a large amount of data, including unstructured ones. This leads to the need for enterprises to work with big data of different formats, which must be prepared in a certain way for further work in order to obtain the results of modeling and calculations. In connection with the above, the research carried out in the article on processing and storing large data of an enterprise, developing a model and algorithms, as well as using new technologies is relevant. Undoubtedly, every year the information flows of enterprises will increase and in this regard, it is important to solve the issues of storing and processing large amounts of data. The relevance of the article is due to the growing digitalization, the increasing transition to professional activities online in many areas of modern society. The article provides a detailed analysis and research of these new technologies.

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