Mystic: Predictive Scheduling for GPU Based Cloud Servers Using Machine Learning

2016 ◽  
Author(s):  
Yash Ukidave ◽  
Xiangyu Li ◽  
David Kaeli
Keyword(s):  
Machine Learning ◽  
Cloud Servers ◽  
Predictive Scheduling

scholarly journals Machine Learning at the Network Edge: A Survey

2022 ◽  
Vol 54 (8) ◽  
pp. 1-37
Author(s):  
M. G. Sarwar Murshed ◽  
Christopher Murphy ◽  
Daqing Hou ◽  
Nazar Khan ◽  
Ganesh Ananthanarayanan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Systems ◽  
Major Research ◽  
Sensors And Actuators ◽  
Computing Systems ◽  
Privacy Concerns ◽  
Iot Devices ◽  
Cloud Servers ◽  
Typical Solution ◽  
Operational Aspects

Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous in recent years. This has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems. However, deploying machine learning models on such end-devices is nearly impossible. A typical solution involves offloading data to external computing systems (such as cloud servers) for further processing but this worsens latency, leads to increased communication costs, and adds to privacy concerns. To address this issue, efforts have been made to place additional computing devices at the edge of the network, i.e., close to the IoT devices where the data is generated. Deploying machine learning systems on such edge computing devices alleviates the above issues by allowing computations to be performed close to the data sources. This survey describes major research efforts where machine learning systems have been deployed at the edge of computer networks, focusing on the operational aspects including compression techniques, tools, frameworks, and hardware used in successful applications of intelligent edge systems.

scholarly journals A Comparative Study on University Admission Predictions Using Machine Learning Techniques

2021 ◽  
pp. 537-548
Author(s):  
Prince Golden ◽  
Kasturi Mojesh ◽  
Lakshmi Madhavi Devarapalli ◽  
Pabbidi Naga Suba Reddy ◽  
Srigiri Rajesh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Education Systems ◽  
University Admissions ◽  
The Troubles ◽  
Learning Techniques ◽  
Cloud Servers

In this era of Cloud Computing and Machine Learning where every kind of work is getting automated through machine learning techniques running off of cloud servers to complete them more efficiently and quickly, what needs to be addressed is how we are changing our education systems and minimizing the troubles related to our education systems with all the advancements in technology. One of the the prominent issues in front of students has always been their graduate admissions and the colleges they should apply to. It has always been difficult to decide as to which university or college should they apply according to their marks obtained during their undergrad as not only it’s a tedious and time consuming thing to apply for number of universities at a single time but also expensive. Thus many machine learning solutions have emerged in the recent years to tackle this problem and provide various predictions, estimations and consultancies so that students can easily make their decisions about applying to the universities with higher chances of admission. In this paper, we review the machine learning techniques which are prevalent and provide accurate predictions regarding university admissions. We compare different regression models and machine learning methodologies such as, Random Forest, Linear Regression, Stacked Ensemble Learning, Support Vector Regression, Decision Trees, KNN(K-Nearest Neighbor) etc, used by other authors in their works and try to reach on a conclusion as to which technique will provide better accuracy.

Decoding search and partnerships across news, journalism, and social media

2020 ◽  
pp. 67-106
Author(s):  
Cate Dowd
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Deep Learning ◽  
Learning Systems ◽  
Third Party ◽  
Viral Video ◽  
News Agency ◽  
Cloud Servers

Semantic news tags processed via cloud servers are in amongst big data and machine learning systems. The latter may have influenced Murdoch’s acquisition of a ‘social media news agency’, and other partnerships, as a mix of new roles across journalism, analytics, and search emerged. Some editing roles in journalism focus on SEO, but Murdoch’s Storyful, which started as a verification business created jobs for cloud operations engineers, viral video editors, and trends editors. Data-mining techniques were a lure for news and social media partnerships circa 2013–2016. In the name of verification, access to big data was matched by social media gaining credibility, evident in Facebook Newswire and other journalism projects. Deep learning methods in search, referrals, and automated tagging have also produced mutual benefits, mostly via third party agreements. However, data sharing for political ends by targeting particular users, and verification projects, have not stopped fake news.

Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems

2020 ◽  
Vol 120 ◽  
pp. 103244 ◽  
Author(s):  
Cristina Morariu ◽  
Octavian Morariu ◽  
Silviu Răileanu ◽  
Theodor Borangiu
Keyword(s):  
Machine Learning ◽  
Resource Allocation ◽  
Manufacturing Systems ◽  
Large Scale ◽  
Predictive Scheduling

scholarly journals Efficient Cognitive Fog Computing for Classification of Network Cyberattacks Using Machine Learning

2020 ◽  
pp. 176-184
Author(s):  
A. V. Deorankar ◽  
Shiwani S. Thakare
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Binary Classification ◽  
Fog Computing ◽  
Machine Learning Techniques ◽  
Machine Learning Classification ◽  
Detection Techniques ◽  
Learning Techniques ◽  
Iot Devices ◽  
Cloud Servers

IoT is the network which connects and communicates with billions of devices through the internet and due to the massive use of IoT devices, the shared data between the devices or over the network is not confidential because of increasing growth of cyberattacks. The network traffic via loT systems is growing widely and introducing new cybersecurity challenges since these loT devices are connected to sensors that are directly connected to large-scale cloud servers. In order to reduce these cyberattacks, the developers need to raise new techniques for detecting infected loT devices. In this work, to control over this cyberattacks, the fog layer is introduced, to maintain the security of data on a cloud. Also the working of fog layer and different anomaly detection techniques to prevent the cyberattacks has been studied. The proposed AD-IoT can significantly detect malicious behavior using anomalies based on machine learning classification before distributing on a cloud layer. This work discusses the role of machine learning techniques for identifying the type of Cyberattacks. There are two ML techniques i.e. RF and MLP evaluated on the USNW-NB15 dataset. The accuracy and false alarm rate of the techniques are assessed, and the results revealed the superiority of the RF compared with MLP. The Accuracy measures by classifiers are 98 and 53 of RF and MLP respectively, which shows a huge difference and prove the RF as most efficient algorithm with binary classification as well as multi- classification.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning
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