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An Efficient Technique for cloud Resource Management Using Machine Learning Model

An Efficient Technique for Cloud Resource Management Using M...

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2023 international conference on Innovative computing, Intelligent communication and Smart Electrical Systems, ICSES 2023

作者： Vyas, Bhuman Saravanakumar, C. Deenadayalan, T. Credit Acceptance Canton48187 United States Government Polytechnic College Department of Computer Engineering Chennai600012 India VIT-Vellore SCORE Vellore India

ISBN: (纸本)9798350319200

Resource management is a vital process in the cloud for satisfying the customer requirement. Resources get the task from the user and perform necessary action. The task needs the data which are collected from various environments. The existing systems are not concentrating on the data but it maps the request to the resources. Resource level management provides the service response to the user. It suffers the data related issues, so it is solved by incorporate the efficient machine learning over the cloud. The proposed model classifies the dataset based on the workload condition namely GPU and CPU level. The model also developed related to the workload. The resources from the cloud are provisioning and allocate to the specific region with necessary parameters. Dynamic load assignment helps to the user for keeping the cost in their acceptable level. Various machine and deep learning models have been analysed related to workload parameters. It achieves high efficient resource monitoring in order to handle faulty resource and make then to active level. © 2023 IEEE.

关键词： cloud computing Dynamic Resource Management Machine Learning Resource Monitoring Service Oriented Architecture

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IOT Based Teeth Pitfall Measurement System

IOT Based Teeth Pitfall Measurement System

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2024 international conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Mahalakshmi, S. Valarmathi, K. Krishnaleela, P. Narayanan, S.Suriya Sivadharsan, K.S. Sridhar, S. P.S.R.Engineering College Electronics and Communication Engineering Sivakasi India

ISBN: (纸本)9798350381887

Oral health monitoring is of paramount importance, and the early detection of teeth - related issues is crucial for preventive care. In this work, an effective embedded system for measuring dental pitfall is designed and put into operation using an ESP model, force sensors, Arduino, and cloud-based updates. The system aims to provide a non-intrusive and accessible solution for users to monitor dental health remotely. The proposed system utilizes Arduino as the embedded platform, integrating force sensors for accurate measurement of teeth pitfall forces during chewing. These force sensors are strategically placed within a custom-designed dental appliance, ensuring real- time data acquisition without discomfort to the user. Arduinos processing capabilities are harnessed to analyze the force data and extract relevant metrics related to teeth pitfall characteristics. To enhance the systems connectivity and enable seamless remote monitoring, an ESP module is employed to establish a secure and efficient communication link with the cloud. The cloud-based infrastructure facilitates real-time data transmission, storage, and analysis. The use of cloud computing ensures scalability, reliability, and accessibility, allowing both users and healthcare providers to access the teeth pitfall measurements anytime, anywhere. The systems architecture enables periodic cloud updates, providing users with insights into their dental health trends over time. Additionally, healthcare professionals can remotely assess the data, allowing for timely interventions and personalized recommendations. The ESP module ensures secure communication, protecting sensitive health information during transmission. The efficiency of the proposed system is demonstrated through extensive testing, validating its accuracy in measuring teeth pitfall forces. The integration of Arduino, force sensors, and cloud-based updates provides a comprehensive and user- friendly solution for continuous dental health monitoring

关键词： Data integration

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Integrating Random Forest and Support Vector Regression Models for Optimized Energy Consumption Evaluation in cloud computing Data Centers 3

Integrating Random Forest and Support Vector Regression Mode...

引用

3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Shaik, Nazeer Kumar, B. Vinoth Bhati, Anita Kumar, Sandeep Singh, Jagendra Sahu, Dinesh Prasad SRIT-Autonomous Department of Computer Science Engineering Anantapur India Ksr Institute for Engineering and Technology Dept. of Electronics and Communication Engineering Tiruchengode India Jecrc University Department of Computer Applications Jaipur India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350342338

This study proposes a novel approach for energy consumption evaluation in cloud computing data centers. The immense energy consumption of these data centers presents significant environmental and economic challenges. Traditional evaluation methods often fail to provide accurate estimations due to the complex dynamics of cloud computing environments. The research introduces an integrated model, combining Random Forest Regression and Support Vector Regression, along with a Gradient Boosting Regressor for iterative training of regression parameters. We measure the model's performance using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), replacing traditional relative deviation measures. The proposed approach aims to provide more accurate and efficient energy consumption evaluations, offering potential benefits in energy management, operational costs reduction, and minimizing environmental impact. © 2023 IEEE.

关键词： cloud computing Energy Consumption Evaluation Gradient Boosting Regressor Random Forest Regression Support Vector Regression

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ALASCA: Function-Driven Advanced Access Control for Big Cold Data 14

ALASCA: Function-Driven Advanced Access Control for Big Cold...

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

作者： Wolf, Karl Pallas, Frank Werner, Sebastian TU Berlin Information Systems Engineering Germany

ISBN: (纸本)9789897587016

Large datasets collected over a long time and only accessed on an infrequent basis – called Big Cold Data herein – play an important role in a broad variety of data-driven applications. In managing such data and the access to it, implementing advanced access control schemes beyond mere role-based yes/no-decisions becomes decisive, given the often sensitive or personal nature of the data as well as the multitude of regulatory requirements and other constraints applying to it. Current, mostly cloud-based technologies for storing and managing Big Cold Data, however, lack that advanced access control functionalities, such as consent-based transformations, while existing approaches for implementing such functionalities do not pay sufficient regard to the particularities of Big Cold Data to offer efficient access on an infrequent basis. We therefore propose an architecture and framework (ALASCA) following the function-as-a-service (FaaS) paradigm for implementing versatile access services on cloud-managed Big Cold Data. Towards that end, we offer a first characterization of Big Cold Data and raise challenges in access control, specifically in performing custom and infrequent transformations on large heterogeneous datasets. We demonstrate the applicability of ALASCA by implementing and evaluating it for AWS and Google cloud. Our preliminary evaluation shows the promise and practical applicability of FaaS-based access control, especially for advanced access control schemes to be applied to Big Cold Data. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Access control

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Big data monitoring and analysis of power network security under cloud platform 8

Big data monitoring and analysis of power network security u...

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8th international conference on Electronic Technology and Information Science, ICETIS 2023

作者： Xu, Sheng-Guo Liu, Jian Guo, Liang Jia, Xue Nanjing Nari information and Communication Technology Co. Ltd Nanjing210003 China

ISBN: (纸本)9781510666535

With the rapid development of cloud computing technology and various applications, enterprises have also built their own cloud *** each information system gradually goes to the cloud and monitoring data increases, the security protection pressure of the cloud platform increases accordingly, resulting in weak data analysis ability and low alarm failure. computing performance has become a key problem that restricts the security monitoring and analysis of power network under cloud platform. Aiming at the performance bottleneck of security monitoring under cloud level, this paper proposes a method to improve the efficiency of big data monitoring and analysis. This method collects the log data of all kinds of security equipment and security system in the network, and uses the real-time processing framework of Flink and ODPS data processing service on the cloud to quickly analyze the big data. Experimental results show that compared with traditional self-built Hadoop platform and real-time computing Storm, this method consumes less resources and has a faster processing speed for the same log volume. © 2023 SPIE.

关键词： Big data

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Cooperative Action Decision based on State Perception Similarity for Deep Reinforcement Learning

Cooperative Action Decision based on State Perception Simila...

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2024 international conference on cloud and Network computing, ICCNC 2024

作者： Chen, Yitian Liu, Bo Zhang, Shigeng Liu, Xuan Central South University School of Computer Science and Engineering Changsha China Hunan University College of Computer Science and Electronic Engineering Changsha China

ISBN: (纸本)9798350367805

In practical applications, implementing reinforcement learning in multi-Agent environments is very important. Most of the existing multi-Agent reinforcement learning algorithms lack communication capabilities, forcing agents to learn solely from their local observations. Some reinforcement learning algorithms that include communication also limit the number of communicating individuals. We propose a multi-Agent reinforcement learning algorithm that incorporates a local communication mechanism. This algorithm does not need to limit the number of individuals with whom the agent communicates. The agent can communicate with any number of agents in each time step, and the agent can choose whether to communicate and with whom to communicate. Besides, this algorithm requires no additional communication content and only uses the information obtained by the agent from the environment. Compared with recent methods, this method makes better use of the environmental information that the agent can obtain and plays a greater role in the team. This makes the actions of multi-Agents more in line with human group actions. To verify the effect of our algorithm, we conducted experiments on several mature open-source multi-Agent reinforcement learning environments. The experimental results show that our algorithm is better than MADDPG and M3DDPG. © 2024 IEEE.

关键词： Contrastive Learning

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Machine Learning-Based Web Service for Robot Programming with cloud Sustenance

Machine Learning-Based Web Service for Robot Programming wit...

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2024 international conference on Sustainable computing and Integrated communication in Changing Landscape of AI, ICSCAI 2024

作者： Devi, T. Sesidhar, D.V.S.R. Manjula, L. Manu, Mehul Ashok, P. Vijaya Bhaskar, K. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of ECE MVSR Engineering College India Chennai India School of Allied Sciences Graphic Era Hill University Graphic Era Deemed to be University Bhimtal Campus Dehradun India Lavale Maharashtra Pune India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (纸本)9798350355338

The utilization of robots is already commonplace in industrial automation. However, there is still ample opportunity for additional automation. An area that offers potential for enhancement is the adaptation of a manufacturing system to novel and unfamiliar items. Presently, this task entails new programming of the robotic device and the recalibration of the image recognition algorithms, should sensors be involved. Small and medium-sized organizations tend to avoid investing in the expertise of specialists due to the required time and effort. Our proposal involves creating a digital replica of a physical manufacturing line, known as a digital twin. This digital twin will imitate the production system and collect labeled data. The data will be used to train a deep learning element. A trained artificial brain network will be used to identify and pinpoint the location of the Noticed items. This enables the production process to efficiently handle all familiar and unfamiliar products with more adaptability. The Artificial neural networks element is situated on a cloud infrastructure and can be reached via an internet service, enabling any staff member to commence the training, irrespective of their programming proficiency. To summarize, our method focuses on both increasing automation in production and utilizing synthesizing data to support artificial learning. © 2024 IEEE.

关键词： Robot programming

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RL with Balanced Reward and Masking Mechanism for Multi-NUMA Virtual Machine Scheduling 19

RL with Balanced Reward and Masking Mechanism for Multi-NUMA...

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19th international conference on Ubiquitous Information Management and communication, IMCOM 2025

作者： Gao, Yucen Cheng, Yunlong Ping, Chan Tin Gao, Xiaofeng Chen, Guihai MoE Key Lab of Artificial Intelligence Department of Computer Science and Engineering Shanghai China Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798331507817

With the rapid development of data center and cloud computing, the importance of resource management is increasing in recent years. In this paper, we focus on the virtual machine scheduling problem in resource management, that is, how to allocate the limited virtual machine resources when a request arrives. Existing works usually model the virtual machine scheduling problem as a bin-packing problem, but they do not consider the multi-NUMA architecture servers that have been widely used in recent years. Compared with the traditional symmetric multiprocessing architecture, the multi-NUMA architecture has the advantages of faster access time and higher energy efficiency, which is more suitable for dealing with today’s scenarios with a large number of computing requirements. In this paper, we first formalize the multi-NUMA virtual machine scheduling problem as a combinatorial optimization problem and explain why it is suitable to be solved by reinforcement learning. A reinforcement learning model called BM-DQN is then proposed, which designs a balanced reward function suitable for the multi-NUMA architecture and adopts a masking mechanism to screen out the optional servers in each step of reinforcement learning, so as to improve the solution performance. Experimental results on the open-source real-world virtual machine scheduling dataset of Huawei cloud show that BM-DQN is superior to the existing methods in terms of the number of completed requests. © 2025 IEEE.

关键词： Resource allocation

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BiOIoV: Biometric-based Secure Data Dissemination for IoV Ecosystem 13

BiOIoV: Biometric-based Secure Data Dissemination for IoV Ec...

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13th international conference on cloud computing, Data Science and engineering, Confluence 2023

作者： Bodkhe, Umesh Tanwar, Sudeep Nirma University Institute of Technology Department of Computer Science and Engineering Gujarat Ahmedabad382481 India

ISBN: (纸本)9781665462631

Internet of vehicles (IoV) allows vehicles to disseminate various messages to neighbouring vehicles and these messages include traffic data, location sharing, driving assistance, road safety, and accident statistics. Additionally, an insecure channel is used for the exchange of these messages, which causes several problems, such as security attacks, data distribution, dynamic topology, vehicle mobility, user privacy, high computational and transmission overhead, throughput, or authentication process when sending these messages across an IoV network with limited resources. Considering the aforementioned issues, in this paper, we propose a biometric-based secure and lightweight message dissemination scheme (i.e., BiOIoV ) for the IoV ecosystem to mitigate various security attacks. We assess BiOIoV security and privacy defenses against various security threats in formal security analysis using Scyther and ProVerif 2.03. We also presented security proofs of BiOIoV. Then, we evaluated the performance of the proposed scheme with cutting-edge approaches. Results show the efficacy of the proposed scheme compared to state-of-the-art approaches in terms of communication overhead, computation cost, and energy consumption. © 2023 IEEE.

关键词： Information dissemination

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A Weighted k-Medoids Clustering Algorithm Based on Granular computing 10

A Weighted k-Medoids Clustering Algorithm Based on Granular ...

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10th IEEE international conference on Cyber Security and cloud computing and 9th IEEE international conference on Edge computing and Scalable cloud, CScloud-EdgeCom 2023

作者： Sun, Shao-Jie Chen, Lin-Shu Mei, Ben-Xia Li, Tao Ye, Xue-Qi Shi, Min Hunan University of Science and Technology School of Computer Science and Engineering XiangTan China Haivaidi Medical School Department of Ophthalmology Boston United States

ISBN: (纸本)9798350312461

Because of the problems that the fast k-Medoids clustering algorithm does not consider the weight of each attribute and the initial clustering center may be in the same cluster, this paper proposes a weighted $\boldsymbol{k}$-Medoids clustering algorithm based on granular computing. Firstly, the hierarchical structure in the fuzzy quotient space theory is introduced to define the decision attribute of the sample under each granularity, and the computing method of sample attribute weight is defined by the attributes of the sample set itself and the definition of attribute importance in the rough set model. Secondly, the sample similarity function is defined by the attribute weight coefficient, and the attribute weight is integrated into the similarity of the fast k-Medoids clustering algorithm to quantitatively define the importance of each sample's attribute. Finally, from the prospective view of granular computing, the samples are clustered according to the above similarity function, and the original clustering centers are initialized by K cluster centers with long distance. The experimental results on machine learning datasets UCI show that the proposed weighted k-Medoids clustering algorithm based on granular computing greatly improves the accuracy of clustering. © 2023 IEEE.

关键词： Clustering algorithms

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