scholarly journals Resource Recommender for Cloud-Edge Engineering

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 224
Author(s):  
Amirmohammad Pasdar ◽  
Young Choon Lee ◽  
Tahereh Hassanzadeh ◽  
Khaled Almi’ani
Keyword(s):  
Resource Allocation ◽  
Data Privacy ◽  
Short Term Memory ◽  
Scheduling Algorithm ◽  
Practical Importance ◽  
Machine Learning Techniques ◽  
Management Service ◽  
Cost Efficient ◽  
Utility Cost ◽  
The Cost

The interaction between artificial intelligence (AI), edge, and cloud is a fast-evolving realm in which pushing computation close to the data sources is increasingly adopted. Captured data may be processed locally (i.e., on the edge) or remotely in the clouds where abundant resources are available. While many emerging applications are processed in situ due primarily to their data intensiveness and short-latency requirement, the capacity of edge resources remains limited. As a result, the collaborative use of edge and cloud resources is of great practical importance. Such collaborative use should take into account data privacy, high latency and high bandwidth consumption, and the cost of cloud usage. In this paper, we address the problem of resource allocation for data processing jobs in the edge-cloud environment to optimize cost efficiency. To this end, we develop Cost Efficient Cloud Bursting Scheduler and Recommender (CECBS-R) as an AI-assisted resource allocation framework. In particular, CECBS-R incorporates machine learning techniques such as multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks. In addition to preserving privacy due to employing edge resources, the edge utility cost plus public cloud billing cycles are adopted for scheduling, and jobs are profiled in the cloud-edge environment to facilitate scheduling through resource recommendations. These recommendations are outputted by the MLP neural network and LSTM for runtime estimation and resource recommendation, respectively. CECBS-R is trained with the scheduling outputs of Facebook and grid workload traces. The experimental results based on unseen workloads show that CECBS-R recommendations achieve a ∼65% cost saving in comparison to an online cost-efficient scheduler (BOS), resource management service (RMS), and an adaptive scheduling algorithm with QoS satisfaction (AsQ).

scholarly journals Gene Teller: an extensible Alexa Skill for gene-relevant databases

2020 ◽  
Author(s):  
Jon D Hill
Keyword(s):  
Short Term Memory ◽  
Consumer Electronics ◽  
Biological Research ◽  
Short Term ◽  
Conversational Interaction ◽  
Amazon Web Services ◽  
Cost Efficient ◽  
The Cost ◽  
Intuitive Manner

Abstract Summary Voice assistants have become increasingly embedded in consumer electronics, as the quality of their interaction improves and the cost of hardware continues to drop. Despite their ubiquity, these assistants remain underutilized as a means of accessing biological research data. Gene Teller is a voice assistant service based on the Alexa Skills Kit and Amazon Lambda functions that enables scientists to query for gene-centric information in an intuitive manner. It includes several features, such as synonym disambiguation and short-term memory, that enable a natural conversational interaction, and is extensible to include new resources. The underlying architecture, based on Simple Storage Service and Amazon Web Services Lambda, is cost efficient and scalable. Availability and implementation A publicly accessible version of Gene Teller is available as an Alexa Skill from the Amazon Marketplace at https://www.amazon.com/dp/B08BRD8SS8. The source code is freely available on GitHub at https://github.com/solinvicta/geneTeller.

scholarly journals Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4093
Author(s):  
Abdullah Lakhan ◽  
Mazin Abed Mohammed ◽  
Ahmed N. Rashid ◽  
Seifedine Kadry ◽  
Thammarat Panityakul ◽  
...  
Keyword(s):  
Current System ◽  
Scheduling Algorithm ◽  
Cost Effective ◽  
Data Consistency ◽  
Smart Contract ◽  
Collect Patient Data ◽  
Healthcare Application ◽  
Cost Efficient ◽  
The Cost ◽  
Earliest Finish Time

The Internet of Medical Things (IoMT) is increasingly being used for healthcare purposes. IoMT enables many sensors to collect patient data from various locations and send it to a distributed hospital for further study. IoMT provides patients with a variety of paid programmes to help them keep track of their health problems. However, the current system services are expensive, and offloaded data in the healthcare network are insecure. The research develops a new, cost-effective and stable IoMT framework based on a blockchain-enabled fog cloud. The study aims to reduce the cost of healthcare application services as they are processing in the system. The study devises an IoMT system based on different algorithm techniques, such as Blockchain-Enable Smart-Contract Cost-Efficient Scheduling Algorithm Framework (BECSAF) schemes. Smart-Contract Blockchain schemes ensure data consistency and validation with symmetric cryptography. However, due to the different workflow tasks scheduled on other nodes, the heterogeneous, earliest finish, time-based scheduling deals with execution under their deadlines. Simulation results show that the proposed algorithm schemes outperform all existing baseline approaches in terms of the implementation of applications.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

scholarly journals Comparison of LPWAN Technologies: Cost Structure and Scalability

2021 ◽  
Author(s):  
Mohammad Istiak Hossain ◽  
Jan I. Markendahl
Keyword(s):  
Communication Technology ◽  
Service Providers ◽  
Communication Technologies ◽  
Cost Structure ◽  
Small Scale ◽  
Area Network ◽  
Wide Area Network ◽  
Generic Framework ◽  
Cost Efficient ◽  
The Cost

AbstractSmall-scale commercial rollouts of Cellular-IoT (C-IoT) networks have started globally since last year. However, among the plethora of low power wide area network (LPWAN) technologies, the cost-effectiveness of C-IoT is not certain for IoT service providers, small and greenfield operators. Today, there is no known public framework for the feasibility analysis of IoT communication technologies. Hence, this paper first presents a generic framework to assess the cost structure of cellular and non-cellular LPWAN technologies. Then, we applied the framework in eight deployment scenarios to analyze the prospect of LPWAN technologies like Sigfox, LoRaWAN, NB-IoT, LTE-M, and EC-GSM. We consider the inter-technology interference impact on LoRaWAN and Sigfox scalability. Our results validate that a large rollout with a single technology is not cost-efficient. Also, our analysis suggests the rollout possibility of an IoT communication Technology may not be linear to cost-efficiency.

scholarly journals Cost Analysis of a Novel Method for Ecological Compensation—A Study of the Translocation of Dead Wood

2021 ◽  
Vol 13 (11) ◽  
pp. 6075
Author(s):  
Ola Lindroos ◽  
Malin Söderlind ◽  
Joel Jensen ◽  
Joakim Hjältén
Keyword(s):  
Cost Efficiency ◽  
Dead Wood ◽  
Road Transportation ◽  
Ecological Value ◽  
Ecological Compensation ◽  
Work Time ◽  
Delivery Times ◽  
Novel Method ◽  
Cost Efficient ◽  
The Cost

Translocation of dead wood is a novel method for ecological compensation and restoration that could, potentially, provide a new important tool for biodiversity conservation. With this method, substrates that normally have long delivery times are instantly created in a compensation area, and ideally many of the associated dead wood dwelling organisms are translocated together with the substrates. However, to a large extent, there is a lack of knowledge about the cost efficiency of different methods of ecological compensation. Therefore, the costs for different parts of a translocation process and its dependency on some influencing factors were studied. The observed cost was 465 SEK per translocated log for the actual compensation measure, with an additional 349 SEK/log for work to enable evaluation of the translocation’s ecological results. Based on time studies, models were developed to predict required work time and costs for different transportation distances and load sizes. Those models indicated that short extraction and insertion distances for logs should be prioritized over road transportation distances to minimize costs. They also highlighted a trade-off between costs and time until a given ecological value is reached in the compensation area. The methodology used can contribute to more cost-efficient operations and, by doing so, increase the use of ecological compensation and the benefits from a given input.

scholarly journals Visual multiple cross displacement amplification for the rapid identification of S. agalactiae immediately from vaginal and rectal swabs

AMB Express ◽  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xueqin Cheng ◽  
Zhiqian Dou ◽  
Jing Yang ◽  
Dexi Liu ◽  
Yulong Gu ◽  
...  
Keyword(s):  
Pathogen Detection ◽  
Clinical Specimen ◽  
Rapid Identification ◽  
Sample Collection ◽  
Special Equipment ◽  
Multiple Cross ◽  
Rectal Swabs ◽  
Cost Efficient ◽  
The Cost ◽  
Positive Results

AbstractStreptococcus agalactiae (S. agalactiae) is an important pathogen that can lead to neonatus and mother infection. The current existing techniques for the identification of S. agalactiae are limited by accuracy, speed and high-cost. Therefore, a new multiple cross displacement amplification (MCDA) assay was developed for test of the target pathogen immediately from vaginal and rectal swabs. MCDA primers screening were conducted targeting S. agalactiae pcsB gene, and one set of MCDA primers with better rapidity and efficiency was selected for establishing the S. agalactiae-MCDA assay. As a result, the MCDA method could be completed at a constant temperature of 61 °C, without the requirement of special equipment. The detection limit is 250 fg (31.5 copies) per reaction, all S. agalactiae strains displayed positive results, but not for non-S. agalactiae strains. The visual MCDA assay detected 16 positive samples from 200 clinical specimen, which were also detected positive by enrichment/qPCR. While the CHROMagar culture detected 6 positive samples. Thus, the MCDA assay is prefer to enrichment/qPCR and culture for detecting S. agalactiae from clinical specimen. Particularly, the whole test of MCDA takes about 63.1 min, including sample collection (3 min), DNA preparation (15 min), MCDA reaction (45 min) and result reporting (6 s). In addition, the cost was very economic, with only US$ 4.9. These results indicated that our S. agalaciae-MCDA assay is a rapid, sensitive and cost-efficient technique for target pathogen detection, and is more suitable than conventional assays for an urgent detection, especially for 'on-site' laboratories and resource-constrained settings.

AOHDL: Archimedes Optimization Based Hybrid Deep Learning Model for Soybean Plant Disease Classification

2021 ◽  
Author(s):  
J. Annrose ◽  
N. Herald Anantha Rufus ◽  
C. R. Edwin Selva Rex ◽  
D. Godwin Immanuel
Keyword(s):  
Deep Learning ◽  
Optimization Algorithm ◽  
Short Term Memory ◽  
Learning Model ◽  
Disease Classification ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Soybean Plant ◽  
Lower Accuracy ◽  
Deep Learning Model

Abstract Bean which is botanically called Phaseolus vulgaris L belongs to the Fabaceae family.During bean disease identification, unnecessary economical losses occur due to the delay of the treatment period, incorrect treatment, and lack of knowledge. The existing deep learning and machine learning techniques met few issues such as high computational complexity, higher cost associated with the training data, more execution time, noise, feature dimensionality, lower accuracy, low speed, etc. To tackle these problems, we have proposed a hybrid deep learning model with an Archimedes optimization algorithm (HDL-AOA) for bean disease classification. In this work, there are five bean classes of which one is a healthy class whereas the remaining four classes indicate different diseases such as Bean halo blight, Pythium diseases, Rhizoctonia root rot, and Anthracnose abnormalities acquired from the Soybean (Large) Data Set.The hybrid deep learning technique is the combination of wavelet packet decomposition (WPD) and long short term memory (LSTM). Initially, the WPD decomposes the input images into four sub-series. For these sub-series, four LSTM networks were developed. During bean disease classification, an Archimedes optimization algorithm (AOA) enhances the classification accuracy for multiple single LSTM networks. MATLAB software implements the HDL-AOA model for bean disease classification. The proposed model accomplishes lower MAPE than other exiting methods. Finally, the proposed HDL-AOA model outperforms excellent classification results using different evaluation measures such as accuracy, specificity, sensitivity, precision, recall, and F-score.

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