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Deadline Sensitive cloud computing Resource Scheduling Method for Scene Rendering 4

Deadline Sensitive Cloud Computing Resource Scheduling Metho...

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4th international conference on Frontiers of Computers and communication engineering, FCCE 2023

作者： Cai, Dunbo Huang, Zhiguo Zhou, Danyuan Qian, Ling Software Technology Co. Ltd Suzhou China China Mobile Communications Group Co. Ltd Beijing China

ISBN: (纸本)9798350333169

Scene rendering is a key process in the area of modern digital animation and digital movie industry. It requires large amount of computing resource, as many digital movies usually contain thousands of scene frames and require many iterations of film editing. To provide flexible compute resources to the industry, cloud computing has become an effective platform for scene rendering. Previous works paid much attention on the load balancing problem of such platforms, which was motivated from service providers perspective. In this paper, we consider a new type of problem scene rendering with deadlines (SRD), which came from the users need. A simple way to meet the deadline is to provide exclusive computing resource for a given job. However, cloud computing providers pursue resource sharing among users. We proposed a method called deadline sensitive cloud resource scheduling (DSCRS) to solve the SRD problem, which supports both the objectives of resource sharing among scene rendering jobs and the satisfaction of job deadlines. Specifically, we model a SRD problem as a constraint satisfaction (CSP) problem, which is then solved by a state of the art constraint programming solver. And we proposed a new CSP encoding method that supports re-planning which is beyond the capability of the well-known flexible job-shop scheduling model. Preliminary results show that our method is effective. © 2023 IEEE.

关键词： cloud computing

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Auto Scaling Infrastructure with Monitoring Tools using Linux Server on cloud 7

Auto Scaling Infrastructure with Monitoring Tools using Linu...

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7th international conference on computing Methodologies and communication, ICCMC 2023

作者： Savitha, S. Sangana, C. Devendran, K. Pravin, L. Rajkumar, M. Nirmal, C. Department of CSE Kongu Engineering College Perundurai India

ISBN: (纸本)9781665464086

cloud computing is the term that has gained widespread usage over these last few years. Due to the rapid increase in the use of information in the digital age of the 21st century, it is increasingly becoming a more attractive option for individuals and organizations to manage all their essential data, projects, and collaborations, rather than relying solely on in-house computers. The user's requirement for hardware and software is reduced via cloud computing. The interface software of cloud computing systems, typically as simple as a web browser, is the only thing the user must operate, and the cloud network handles the rest. To decrease operational costs, both business and government organizations are adopting cloud computing, seeking a flexible and adaptable solution for the supply and delivery of their product services. Microservices and decoupled apps are becoming more popular. These container-based architectures make it easier to build sophisticated SaaS apps quickly, but managing and creating microservices can be a daunting task. Managing and creating microservices that involve a wide range of diverse functions, including handling and storing information, and performing predictive and prescriptive analysis, can be a challenging undertaking. Establishing auto scaling infrastructure on cloud can be challenging due to several reasons, some of which are: understanding the application architecture, setting up monitoring, scaling policies, cost optimization and implementation complexity. Server farms include the tremendous and heterogeneous virtualized frameworks, which are continually extending and broadening after sometime are the essential starting point for registering specialized organizations. These solutions also need to be integrated into existing systems while adhering to Quality of Service (QoS) requirements. The principal objective of this work is to propose an on-premise design to leverage Kubernetes and Docker containers to improve the quality of servic

关键词： Quality of service

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Lip Reading Into Text Using Deep Learning 14

Lip Reading Into Text Using Deep Learning

引用

14th international conference on cloud computing, Data Science and engineering, Confluence 2024

作者： Manglani, Krishna Bahuguna, Utkarsh Gupta, Subhash Chand Amity School of Engineering and Technology Dept. of Computer Science Uttar Pradesh India

ISBN: (纸本)9798350344837

An essential part of computer vision, lip reading, has grown significantly and is now used in autonomous driving, public safety, and hearing-impaired communication. This work provides an in-depth analysis of both conventional and deep learning-based lip reading systems, examining their development over time, difficulties encountered, and critical role in human communication. The suggested LipNet model, which leverages the complementary abilities of Long Short-Term Memory networks (LSTMs) for temporal modelling and Convolutional Neural Networks (CNNs) for spatial feature extraction, is a central focus of this research. When LipNet is tested using the GRID Corpus dataset, it outperforms baseline models in terms of performance. Saliency maps and viseme analysis highlight LipNet's ability to capture phonologically relevant areas in lip motions, providing insight into the network's attention processes. Apart from examining the importance of datasets such as OuluVs2and GRID Corpus, this study presents GhostNet, a lightweight model that tackles the requirement for effective network designs. The testing results highlight LipNet's competitive Character Error Rate (CER) and Word Error Rate (WER) in comparison to baseline models, proving that it is the best method for real-world use. The research ends with a summary of potential future developments in lip reading technology, such as modal integration, realtime applications, personalisation, ethical issues, and the goal of creating a communication environment that is more inclusive. This study highlights the effectiveness of LipNet and paves the way for future developments in the ever-evolving area of lip reading by examining a variety of methodologies. © 2024 IEEE.

关键词： Convolutional neural networks

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Resource Optimization in cloud Environment Using Advanced Metaheuristic Scheduling Algorithm 5

Resource Optimization in Cloud Environment Using Advanced Me...

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5th international conference on Sustainable communication Networks and Application, ICSCNA 2024

作者： Deeplaxmi, Pasnur Vikas, Cholleti Medishetti, Santhosh Kumar Geetanjali College of Engineering and Technology T.S. Hyderabad India Keshav Memorial Institute of Technology T.S. Hyderabad India Nalla Narasimha Reddy Education Society's Group of Institutions T.S. Hyderabad India

ISBN: (纸本)9798331530013

Task Scheduling (TS) especially in cloud environments is quite a challenge because the resources within cloud are dynamic while the tasks present within a cloud have diverse characteristics. This paper aims at presenting a new scheduling technique based on one of the metaheuristic algorithms called Harris Hawks Optimization (HHO), which is derived from the social instinct and predatory hunting behavior of the Harris's hawks. The HHO algorithm is intended to deal with the challenges that appear in the pattern of tasks by adjusting the important factors like makespan, energy and resource utilization. We evaluate the performance of HHO through comprehensive simulations and compare it against traditional scheduling methods, highlighting its efficacy in balancing load and minimizing latency. The results demonstrate that the HHO-based scheduling approach achieves notable improvements in scheduling efficiency, with 15% improvement in energy consumption, a 16% reduction in makespan, and an 18% increase in throughput. The work that is presented in the paper offers a comprehensive approach to effective coordination of tasks in cloud computing systems to propose a satisfactory way to address this challenge in the context of current application-driven compute-intensive applications. © 2024 IEEE.

关键词： cloud platforms

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Smart Agricultural Water Management Scheme in Saudi Arabia Based on IoT and cloud computing Techniques

Smart Agricultural Water Management Scheme in Saudi Arabia B...

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2024 international conference on Smart Systems for Electrical, Electronics, communication and Computer engineering, ICSSEEC 2024

作者： Abbas, Saad Qasim Abdulsada, Zainab.R. Sheel, Shaid Mahmood, Sarmad Nozad Khalid, Raed Ali, Mohsin Al-Fatlawy, Ramy Riad Al- Turath University College Computer Technologies Engineering Baghdad Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah DhiQar Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Kitab University Department of Computer Engineering Technologies Kirkuk Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-Maamoun University College Computer Technology Engineering Baghdad Iraq The Islamic University Department of Computers Techniques Engineering College of Technical Engineering Najaf Iraq

ISBN: (纸本)9798350378177

Irrigating fields, collecting rainwater for use on the farm, and delivering agricultural goods to markets throughout Saudi Arabia all rely heavily on water conservation measures known together as the agriculture water management scheme (A WMS). The dates grown in Saudi Arabia are used both at home and abroad. Management, user operation, and environmentally sound farming are all the provinces of the Ministry of Agriculture. Collectively, these practices are known as agricultural water management. Saudi Arabia's agricultural and energy sectors employ Internet of Things (loT) sensors to gather real-time data on their infrastructures for immediate analysis and response. cloud computing (CC) may assist farmers in predicting the quality of their future harvests. The cloud may hold both long-term predictions and local weather information. Keeping realistic choices open and increasing the efficiency of water reserves are aided by technology. In addition to the problems of excess fertilizers and pesticides, using water is one of the most resource-intensive human endeavours in history. However, if water use continues to rise, the associated cost of water contamination might become unsustainable. The A WMS smart water management system employs electronic and computerized engineering to improve data collecting and analytics in Saudi Arabia. The agricultural loT encompasses specialized machinery, wireless networks, applications, and services. In Saudi Arabia, agricultural fields are monitored through a technology called loT smart farming solutions, which makes use of sensors. The online monitoring of agricultural water management and the management of complicated supply chain ecosystems are two examples of the many areas that need to be integrated to use loT in agriculture in Saudi Arabia. Experiments reveal that compared to other approaches, the A WMS leads to much higher ratios of crop production of 98.2 %, water analysis of 90.2 %, food production of 97.30/0, and economic dev

关键词： Agriculture cloud computing Integration Internet of Things Water management

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Exploring the empirical studies of cloud computing adoption in anglophone West African countries' academic libraries: A review

Exploring the empirical studies of cloud computing adoption ...

引用

2022 international conference on Mathematical and Statistical Physics, Computational Science, Education, and communication, ICMSCE 2022

作者： Yakubu, Aliyu Shehu Kassim, Azleena Mohd Husin, Mohd Heikal School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Department of Library and Information Science University of Maiduguri Nigeria

ISBN: (纸本)9781510664388

cloud computing offer an important role in almost all online transactional activities that involve a substantial exchange of information. However, studies have revealed that despite the important roles of cloud computing, most libraries in higher educational institutions in some West African Countries have not adopted the technology. Additionally, existing empirical studies on the adoption of cloud computing in English-speaking countries of West African academic libraries are low and vary from one country to another as well as from one geo-political region to the other. This necessitates the need to explore the existing empirical studies which were carried out on the adoption of cloud computing level and its impact in academic libraries in West Africa such as Ghana, Gambia, Sierra Leone, Liberia and Nigeria. Several online academic databases were used as sources of data for this study using relevant keywords. As such, this study reviewed the existing empirical research on cloud computing acceptance within West Africa which revealed varying results concerning cloud computing acceptance in academic libraries. results revealed that the majority of the existing studies of cloud computing in academic libraries were found to be focused in Nigeria, and the findings further revealed that the Southern and Eastern parts of Nigeria have more studies compared to the other parts. Based on this outcome, this study recommends additional empirical study to be conducted on cloud computing adoption among the Anglophone Countries of West African academic libraries, as the factors for technology adoption may likely differ from one area to the other. © 2023 SPIE.

关键词： cloud computing

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KubeEdge for Scalable IoT Applications 7

KubeEdge for Scalable IoT Applications

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Saini, Lalit Mohan Ajmani, Prerna Asha, V. Pithamber, Khobragade Sreevani, N. Viveknanda Institute of Professional Studies Tc Ggsipu Delhi India New Horizon College of Engineering Department of Master of Computer Application Bangalore India Mlr Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Dundigal Hyderabad500043 India

ISBN: (纸本)9798350350067

The rise of IoT (Internet of Things) presents both opportunities and challenges in managing large-scale deployments. Traditional cloud solutions struggle with realtime data processing and scalability for numerous connected devices. KubeEdge, an extension of Kubernetes, addresses these challenges by extending cloud-native orchestration to the edge of the network. This paper explores how KubeEdge enhances IoT applications through efficient edge computing, crucial for reducing latency and optimizing resource utilization. KubeEdge extends Kubernetes' capabilities to manage containerized applications on edge nodes, enabling localized data processing and reducing reliance on centralized cloud infrastructures. This approach minimizes latency and lowers bandwidth costs, making it ideal for real-time, data-intensive IoT applications. This research paper highlights practical use cases and proposes strategic deployment frameworks, emphasizing KubeEdge's benefits in scalability, resource management, and operational efficiency. It also addresses potential challenges in deploying KubeEdge at scale, such as managing resource distribution and integrating with existing infrastructure. This paper provides actionable insights and best practices for Utilizing KubeEdge in various IoT contexts, demonstrating its impact on improving the performance and scalability of IoT systems. © 2024 IEEE.

关键词： Resource allocation

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cloud-network resource management based on IDN and ONAP 4

Cloud-network resource management based on IDN and ONAP

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4th international conference on Computer Science and communication Technology, ICCSCT 2023

作者： Li, Jun Wang, Jianxiu Li, Xiangning China Telecom Beijing100024 China

ISBN: (纸本)9781510671232

With the rapid development of 5G, networks are moving towards ubiquity, sharing, and intelligence. Traditional networks suffer from the problem of not being able to schedule routes accurately. The advent of intent-driven network (IDN) has made the network more flexible and efficient. IDN could identify and integrate UE intents in natural language and translate them into cloud network scheduling policy to manage cloud-network resources on the ONAP (Open Network Automation Platform), which is the platform for managing and automating network and edge computing services. To satisfy a lot of different demands, ONAP and IDN scheduling are necessary. MEC cloudifies the third-part App and saves the calculated resources in the local UE (user equipment). This paper introduces the containers and slices management, components of ONAP and IDN, introduces the MEC (mobile edge computing), and designs the procedure to manage the cloud-network user intent instances. © 2023 SPIE.

关键词： Mobile edge computing

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Optimizing Service Placement in Edge-to-cloud AR/VR Systems Using a Multi-Objective Genetic Algorithm 14

Optimizing Service Placement in Edge-to-Cloud AR/VR Systems ...

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14th international conference on cloud computing and Services Science, CLOSER 2024

作者： Herabad, Mohammadsadeq G. Taheri, Javid Ahmed, Bestoun S. Curescu, Calin Department of Mathematics and Computer Science Karlstad University Karlstad Sweden School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast United Kingdom Department of Computer Science Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Ericsson AB Stockholm Sweden

ISBN: (纸本)9789897587016

Augmented Reality (AR) and Virtual Reality (VR) systems involve computationally intensive image processing algorithms that can burden end-devices with limited resources, leading to poor performance in providing low latency services. Edge-to-cloud computing overcomes the limitations of end-devices by offloading their computations to nearby edge devices or remote cloud servers. Although this proves to be sufficient for many applications, optimal placement of latency sensitive AR/VR services in edge-to-cloud infrastructures (to provide desirable service response times and reliability) remain a formidable challenging. To address this challenge, this paper develops a Multi-Objective Genetic Algorithm (MOGA) to optimize the placement of AR/VR-based services in multi-tier edge-to-cloud environments. The primary objective of the proposed MOGA is to minimize the response time of all running services, while maximizing the reliability of the underlying system from both software and hardware perspectives. To evaluate its performance, we mathematically modeled all components and developed a tailor-made simulator to assess its effectiveness on various scales. MOGA was compared with several heuristics to prove that intuitive solutions, which are usually assumed sufficient, are not efficient enough for the stated problem. The experimental results indicated that MOGA can significantly reduce the response time of deployed services by an average of 67% on different scales, compared to other heuristic methods. MOGA also ensures reliability of the 97% infrastructure (hardware) and 95% services (software). © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Augmented reality

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A Three-Tier Architecture Framework with Blockchain Integration to Improve the Security in Healthcare Applications 14

A Three-Tier Architecture Framework with Blockchain Integrat...

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14th international conference on cloud computing, Data Science and engineering, Confluence 2024

作者： Sivashankari, T. Pallikonda Rajasekaran, M. Ramaraj, Kottaimalai Arunprasath, T. Reddy, Muthangi Kantha Thilagaraj, M. Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Biomedical Engineering Tamilnadu Krishnankoil India Sri Vishnu Engineering College Department of Computer Science and Engineering AndhraPradesh Bhimavaram India Mvj College of Engineering Department of Industrial Internet of Things Bengaluru India

ISBN: (纸本)9798350344837

The utilization of online diagnosis as a prevalent data service in contemporary healthcare systems involves the real-time collection of physical data through Internet of Things (IoT) devices, such as body sensors and software. This data informs instantaneous decision-making in the diagnostic process. However, the sensitive nature of the collected user data and diagnosis results poses a significant risk of privacy breaches or hacking incidents, potentially leading to misinformation or even fatal outcomes. The involvement of third-party cloud services exacerbates concerns about data protection. Compounding these issues, resource-constrained IoT devices often lack standard security measures. It is imperative to consider parameters such as accuracy, adaptability, low computation time, and the implementation of appropriate anonymity procedures. Addressing these challenges requires the development of a robust algorithm that prioritizes secure access to health records, with a particular emphasis on blockchain technology. To mitigate these shortcomings and ensure the secure storage of patient health data, this study advocates for the creation of a resilient three-tier architecture leveraging blockchain technology. This approach aims to enhance security measures, maintain data accuracy, and facilitate efficient access to health records while safeguarding patient confidentiality. © 2024 IEEE.

关键词： Internet of things

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