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检索条件"机构=School of Systems and Computing Engineering"

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Comparative Analysis of Cloud computing and the Gig Economy Pricing Strategies

Comparative Analysis of Cloud Computing and the Gig Economy ...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Jothish Kumar, M. Reddy, Gajulapalli Venkata Karthik Kumar Iyapparaja Nowritha, S. School of Computer Science Engineering and Information Systems Vit Vellore Department of Smart Computing Tamil Nadu Vellore India School of Computer Science Engineering and Information Systems Vit Vellore Tamil Nadu Vellore India School of Computer Science Engineering and Information Systems Vit University Department of Smart Computing Tamil Nadu Vellore India

ISBN: (纸本)9798331543617

Cloud computing services and gig economy platforms vary in flexibility, efficiency, and scalability due to their pricing schemes. Cloud computing services provide flexibility and cost control via pay-as-you-go, subscription, and freemium models. Like gig economy platforms, workers and customers pay for task completion, time-based contracts, and premium services. Assessing how successfully different pricing techniques fulfil varied demands, from short-term initiatives to long-term commitments, requires identifying their strengths and weaknesses. The goal is to evaluate these models' capacity to meet shifting demand, save money, and scale. The methodology also assesses cost predictability and company and consumer advantages of each pricing structure. The comparison seeks to improve resource allocation and financial planning by revealing how various pricing systems function in different circumstances. The research helps decision-makers use cloud services or gig economy platforms for different operational purposes by emphasizing their economic consequences. From datasets, simulated pricing model data was examined. The first example includes settings such as Hours Used (5-25), Pay-as-You-Go (10-50 USD), and Subscription-Based (30 USD). The second instance has Job Size (Resources) from 5 to 25, Freemium Model (USD) from 10 to 30, and Usage-Based Pricing (USD) from 10 to 50. © 2024 IEEE.

关键词： Cloud platforms

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A Deep Reinforcement Learning Approach for Cost Optimized Workflow Scheduling in Cloud computing Environments 24

A Deep Reinforcement Learning Approach for Cost Optimized Wo...

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2024 Asia Pacific Conference on computing Technologies, Communications and Networking, CTCNet 2024

作者： Jayanetti, Amanda Halgamuge, Saman Buyya, Rajkumar Lab School of Computing and Information Systems The University of Melbourne Australia School of Electrical Mechanical and Infrastructure Engineering The University of Melbourne Australia

ISBN: (纸本)9798400709609

Cost optimization is a common goal of workflow schedulers operating in cloud computing environments. The use of spot instances is a potential means of achieving this goal, as they are offered by cloud providers at discounted prices compared to their on-demand counterparts in exchange for reduced reliability. This is due to the fact that spot instances are subjected to interruptions when spare computing capacity used for provisioning them is needed back owing to demand variations. Also, the prices of spot instances are not fixed as pricing is dependent on long term supply and demand. The possibility of interruptions and pricing variations associated with spot instances adds a layer of uncertainty to the general problem of workflow scheduling across cloud computing environments. These challenges need to be efficiently addressed for enjoying the cost savings achievable with the use of spot instances without compromising the underlying business requirements. To this end, in this paper we use Deep Reinforcement Learning for developing an autonomous agent capable of scheduling workflows in a cost efficient manner by using an intelligent mix of spot and on-demand instances. The proposed solution is implemented in the open source container native Argo workflow engine that is widely used for executing industrial workflows. The results of the experiments demonstrate that the proposed scheduling method is capable of outperforming the current benchmarks. © 2024 Copyright held by the owner/author(s).

关键词： Deep reinforcement learning

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Adoption of Intelligent Information systems: An Approach to the Colombian Context 25

Adoption of Intelligent Information Systems: An Approach to ...

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25th International Conference on Enterprise Information systems, ICEIS 2023

作者： Abadía, Sofía Avila, Oscar Department of Systems and Computing Engineering School of Engineering Universidad de los Andes Bogota Colombia

ISBN: (纸本)9789897586484

Enterprise Information systems (EIS) are widely used to support operational and tactical processes of companies and have begun, in recent years, to be used at the strategic level to support decision-making processes. To do so, new systems, known as intelligent EIS, integrate data analytics modules to provide the necessary information and reports to make informed decisions. There are certain influencing factors for the adoption of such systems, however, from a first analysis of the academic literature, it was found that research works in the domain are very scarce and even more, there are no research works on the subject in Colombia. Consequently, this article aims at identifying the relevant factors for the adoption of intelligent EIS based on an analysis of the academic literature, and then structuring a focus group activity with 5 experts on the subject to obtain a first approach to the adoption of this kind of systems for the Colombian context. As a preliminary result, we found that in the Colombian industry the most important influencing factors include cost and IT capabilities which differs from main factors identified in the revision of the international scholar literature. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Data Analytics

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Auto Identifying Key Information in Video Lectures to Generate a Navigation Structure

Auto Identifying Key Information in Video Lectures to Genera...

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2024 International Research Conference on Smart computing and systems engineering, SCSE 2024

作者： Dhanushika, Thisaru Weerasinghe, Thushani A. University of Colombo School of Computing Postgraduate Division Colombo07 Sri Lanka University of Colombo School of Computing Department of Information Systems Engineering Colombo07 Sri Lanka

ISBN: (纸本)9798350375688

Online learning platforms are crucial today to ensure uninterrupted teaching and learning. Lecturers upload recorded lectures on these platforms, aiding students having connectivity issues and supporting distance education. However, these lecture recordings are often too lengthy to efficiently navigate to the most attentional content, which includes essential information provided in the lecture. This study proposes a new method to navigate lengthy videos by automatically identifying the most attentional content in video lectures. It would be a better alternative to the existing findings, such as text search in the audio transcript or manual video pausing. Three sub-questions drove the research: what are lecturers' most common techniques to emphasise the most attentional content, how can these techniques be identified in slides, and how can a navigational structure be created to access the most attentional content in video lectures? The research employed a constructive research approach. It started with a questionnaire to uncover lecturers' common techniques, such as text highlighting, underlining, and writing on the slides to denote the most attentional content. The study included a comparative analysis of existing image processing techniques such as Contour Detection, Canny Edge Detection, and Probabilistic Hough Line Transformation, and a machine learning object detection model named EfficientDet DO 512x512 to identify the most attentional content in video frames. The findings revealed that the object detection model was more effective in identifying the most attentional content. Finally, the research employed a strategy involving video frame capturing, slide transition detection, tagging video frames with timestamps, and relevant transcribed words to generate the navigation structure. The findings demonstrated that the navigation structure could effectively locate slides containing the most attentional content with high precision values. Hence, this research can

关键词： Navigation

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A Method for Battery SoH Estimation Based on K-means and LightGBM algorithm 6

A Method for Battery SoH Estimation Based on K-means and Lig...

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6th International Conference on System Reliability and Safety engineering, SRSE 2024

作者： Guo, Fusen Zhang, Zhibo Mo, Huadong Li, Chaojie Swinburne University of Technology School of Science Computing and Engineering Melbourne Australia The University of New South Wales School of Systems and Computing Canberra Australia The University of New South Wales School of Electrical Engineering and Telecommunications Sydney Australia

ISBN: (纸本)9798350356083

The accurate prediction of the State of Health (SOH) of lithium-ion batteries is critical for the health monitoring and cost-effective operation of electric vehicles (EV) and energy storage systems. Current methods for SOH estimation, such as the ohmic resistance and capacity comparison methods, face limitations in real-time applications due to varying operating conditions and extensive computational requirements. To address the above research gaps, this research aims to develop a method that can more accurately classify battery health features and more reliably estimate SOH in a real-time manner. The proposed method utilizes K-means clustering to group similar battery health features, which serve as the foundation for the LightGBM model to make accurate and robust estimations. The experimental results demonstrate that the proposed method significantly outperforms traditional algorithms in terms of accuracy in estimating the SOH. By offering a more precise and reliable tool for battery health monitoring, this research contributes to more effective battery management systems, ultimately enhancing the efficiency and lifespan of lithium-ion batteries. © 2024 IEEE.

关键词： K-means clustering

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True Rays Model Integration in OMNeT++ for IIoT Network Planning in Underground Mines 1

True Rays Model Integration in OMNeT++ for IIoT Network Plan...

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1st IEEE Latin American Conference on Antennas and Propagation, LACAP 2024

作者： Suancha, Cristian Montanez, Oscar Medina, Fabian Fernandez, Eduardo Avendano Cespedes, Sandra School UPTC Systems and Computing Engineering Sogamoso Colombia School UPTC Electronic Engineering Sogamoso Colombia Concordia University Computer Science and Software Engineering Montreal Canada

ISBN: (纸本)9798331527587

This work develops a new module in the OMNeT++ simulator incorporating and validating the True Rays propagation model for underground environments. The integration enables the planning and evaluation of Industrial Internet of Things deployments of wireless sensor networks (WSNs) in mining scenarios. The tool can read the mine blueprints and deploy nodes at specified locations. It also defined the environment and network communication parameters for network coverage and performance analysis, allowing an understanding of how the WSN behaves in the underground mine before its deployment. The simulation predicts variables such as received power and path losses, optimizing sensing coverage and connectivity while reducing the need for site surveys. A large number of simulations using blueprints from real mines show that the signal propagation measurements are consistent with the theoretical models proposed in the state of the art. Experimental results were compared with a simulation WSN using LoRa, the maximum range obtained in simulation was 47 m, the measurements under real conditions in the underground only achieved 25 m of reach, but were limited at a threshold of -100 dBm to ensure connectivity and reliability of transmission among the nodes. As a result, the simulations and measurements performed better at the 433 MHz frequency, and the main contribution is a simulation tool simplifying planning IIoT networks in mining tunnels. © 2024 IEEE.

关键词： Sensor nodes

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Impact of Environmental Changes on Optimized Robotics Collective Motion for Multi-objective Coverage Tasks 13th

Impact of Environmental Changes on Optimized Robotics Coll...

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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Ghanem, Reda Ali, Ismail M. Kasmarik, Kathryn Garratt, Matthew School of Systems and Computing University of New South Wales Canberra Australia School of Engineering and Technology University of New South Wales Canberra Australia

ISBN: (纸本)9789819635054

Coverage tasks have garnered significant interest in recent years due to their applications in fields such as environmental monitoring, search and rescue, and robotic exploration. Swarm robotics has emerged as a promising approach to tackle these challenges, leveraging collective behaviors to enhance efficiency. However, the effectiveness of swarm algorithms often hinges on the careful tuning of parameters, which can be influenced by varying environmental features. This study investigates the impact of these features on the performance of optimized collective motion in swarm robotics for solving multi-objective coverage problems. Through a comprehensive sensitivity analysis, we evaluate how changes in parameters-such as arena size, number of robots, obstacle density, and obstacle structure-affect swarm performance in solving coverage problems. The analysis focuses on key performance metrics across multiple objectives: coverage percentage, coverage time, robot connectivity, and collision rates. Our findings provide critical insights into the adaptability of swarm algorithms, highlighting the importance of environmental context in optimizing swarm efficacy across competing objectives. These results contribute to the development of more robust and adaptive swarm-based solutions for multi-objective coverage tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic algorithms

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Hybrid Approach for Electricity Price Forecasting using AlexNet and LSTM 3

Hybrid Approach for Electricity Price Forecasting using Alex...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sambana, Bosubabu Devi, Kotamsetty Geethika Reddy, Bandi Rajeswara Hussain, Galeti Mohammad Siddartha, Gownivalla School of Computing Mohan Babu University Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331527549

The recent development of advanced machine learning methods for hybrid models has greatly addressed the need for the correct prediction of electrical prices. This method combines AlexNet and LSTM algorithms, which are used to introduce a new model with higher accuracy in price forecasting. Despite RNN and ANN being effective, they often fail to deal with forex time sequence data. The traditional methods do not accurately forecast the prices. These traditional methods only focus on demand and price which leads to insufficient analysis of data. To address this issue, using the hybrid approach, which focuses on external variables that also effect the predicted prices. Nevertheless, due to AlexNet's excellent feature extraction and LSTM's learning sequential patterns, the prediction accuracy is vastly increased. The model is built on the past data, which has been supplied with the most significant elements like demand, temperature, sunlight, and rain. For example, the model applies methods, such as minimum-maximum scaling and a time window, to predict the electricity prices of the future. The results show that this hybrid model is good than the standalone ones in terms of accuracy. Although we got our accuracy rating of 97.08, it shows higher accompaniments than remaining models RNN and ANN with accuracies of 96.64 and 96.63 respectively. © 2025 IEEE.

关键词： Prediction models

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Edge Intelligence for Wildlife Conservation: Real-Time Hornbill Call Classification Using TinyML 2nd

Edge Intelligence for Wildlife Conservation: Real-Time Hornb...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Hing, Kong Ka Behjati, Mehran Saleh, Vala Meng, Yap Kian Majeed, Anwar P. P. Abdul Zheng, Yufan Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya47500 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya47500 Malaysia Department of Electrical Engineering Shiraz University of Technology Shiraz Iran Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Hornbills, an iconic species of Malaysia's biodiversity, face threats from habitat loss, poaching, and environmental changes, necessitating accurate and real-time population monitoring that is traditionally challenging and resource intensive. The emergence of Tiny Machine Learning (TinyML) offers a chance to transform wildlife monitoring by enabling efficient, real-time data analysis directly on edge devices. Addressing the challenge of wildlife conservation, this research paper explores the pivotal role of machine learning, specifically TinyML, in the classification and monitoring of hornbill calls in Malaysia. Leveraging audio data from the Xeno-canto database, the study aims to develop a speech recognition system capable of identifying and classifying hornbill vocalizations. The proposed methodology involves preprocessing the audio data, extracting features using Mel-Frequency Energy (MFE), and deploying the model on an Arduino Nano 33 BLE, which is adept at edge computing. The research encompasses foundational work, including a comprehensive introduction, literature review, and methodology. The model is trained using Edge Impulse and validated through real-world tests, achieving high accuracy in hornbill species identification. The project underscores the potential of TinyML for environmental monitoring and its broader application in ecological conservation efforts, contributing to both the field of TinyML and wildlife conservation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Environmental monitoring

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Reinforcement Learning for Joint Resource and Power Allocation in D2D Communications 63

Reinforcement Learning for Joint Resource and Power Allocati...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Saeed, Ifrah Cullen, Andrew C. Zaidi, Zainab R. Erfani, Sarah Alpcan, Tansu The University of Melbourne Department of Electrical and Electronic Engineering Australia The University of Melbourne School of Computing and Information Systems Australia

ISBN: (纸本)9798350316339

Device-to-Device (D2D) communication is critical in many public safety scenarios where access to network infrastructure is not guaranteed. In such out-of-coverage situations, random resource allocation is a straightforward way for direct communication by each D2D pair. However, such a resource allocation plan often results in severe interference and low throughput, which can degrade crucial communications performance. In contrast, the problem of centralised joint resource block selection and power control optimisation to maximise the total data rate of all D2D pairs, known as the sum-rate, is non-convex and NP-hard but can effectively reduce interference. To address the inability to solve this non-convex centralised problem setting due to limited information in resource-constrained out-of-coverage scenarios, we propose two distributed reinforcement learning schemes. Our methods allow D2D pairs to autonomously make decisions on their joint resource block and power allocation to minimise their mutual interference and maximise their sum-rate, while maintaining the quality of service constraints. To evaluate these methods in out-of-coverage scenarios, we conduct extensive performance evaluations using the ns-3 network simulator designed for public safety LTE-D2D. We demonstrate that our algorithms, in the absence of any centralised coordination and neighbouring information, autonomously reach time-averaged sum-rates that are within 98.2 % and 98.6 % of the rates achieved by the centralised optimisation solution. © 2024 IEEE.

关键词： Reinforcement learning

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