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A metaheuristic virtual machine placement framework toward power efficiency of sustainable cloud environment

Soft Computing 2023年第7期27卷 3817-3828页

作者： Singh, Ashutosh Kumar Swain, Smruti Rekha Lee, Chung Nan Department of Computer Applications National Institute of Technology Haryana Kurukshetra136119 India Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung804201 Taiwan

The primary aim of Virtual Machine Placement (VMP) is the mapping of Virtual Machines (VMs) to Physical Machines (PMs), such that the PMs may be utilized to their maximum efficiency, where the already active VMs are not to be interrupted. It provides a list of live VM migrations that must be accomplished to get the optimum solution and reduces energy consumption significantly. The inefficient VMP leads to wastage of resources and excessive energy consumption and increases the overall operational cost of the data center. A Metaheuristic Virtual Machine Placement Framework towards the Power Efficiency of Sustainable Cloud Environment (MV-PESC) approach is suggested to address the issues mentioned above. An Extended Flower Pollination Optimization algorithm is suggested, which combines the concept of the Random Fit algorithm and the Flower Pollination Optimization algorithm. The proposed work’s performance is evaluated using actual workload traces of the benchmark Google Cluster Data set. The obtained results are compared with various state-of-the-art and demonstrate a notable reduction in power consumption, the number of active PMs, and execution time up to 64.89%, 35%, and 21.12%, respectively. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Energy utilization

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Vulnerability Classification for Detecting Threats in Cloud Environments Against DDoS Attacks 13

Vulnerability Classification for Detecting Threats in Cloud ...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Jyothi, V. Esther Chowdary, N. Sampreeth Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Applications Vijayawada India P.V.P. Siddhartha Institute of Technology Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350305463

In order to secure data, infrastructure, and services from potential attacks, cloud security is a wide issue that includes any combination of policies, technologies, and procedures. The cloud has substantially different security needs than traditional settings. Because of its dynamic nature and low client infrastructure ownership, the cloud has decreased traditional security architecture. Internet connectivity and distant servers are used in cloud computing to manage data and applications. It provides users with on-demand software, bandwidth, and dynamic virtualized resources through the internet and ensures that its adapters will share in various financial advantages. The utilization of hardware, software licenses, and system maintenance is decreased for the benefit of the customers. The protocol known as SOAP or Simple Object Access Protocol, it enables communication between various web-based services. Extensible Mark-up Language (XML) and Hyper Text Transport Protocol (HTTP) are both used to develop SOAP communications. A cloud web service that uses HTTP and XML might be bringing down by the new type of Distributed Denial of Service (DDoS) attacks. HTTP and XML Denial of Service (DoS) attacks provide a serious security risk to cloud computing. The Hypertext Extensible-Denial of Service (HX-DoS) attack, which causes the cloud service provider's communication channel to get blocked and go down, is made up of purposefully transmitted HTTP and XML messages. Differentiating between suitable and improper communications is necessary to solve the issue of HX-DoS attacks against cloud web services. To decide and place the packets on the side of the victim, the reconstruct and drop technique is utilized. Detection rate, accuracy, and precision were the performance parameters used to evaluated the model's performance. © 2024 IEEE.

关键词： Denial-of-service attack

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Heart Disease Prediction Using Tree Structured Parzen Estimator Optimization 5

Heart Disease Prediction Using Tree Structured Parzen Estima...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Hemalatha, S. Kavitha, T. Sivabalaselvamani, D. Karthika, R.T. Hariprasath, N. Desikan, R. Kongu Engineering College Department of Computer Applications Erode India School of Information Science Presidency University Bengaluru India

ISBN: (纸本)9798331530013

Heart diseases involve diseases of the heart and blood vessels like coronary artery disease, arrhythmias and congenital heart diseases. Electrocardiogram or ECG is a test that assesses the activity, rhythm and electrical conduction of the heart and is paramount in diagnosing heart disorders. It should, however, be noted that even though the ECG is important in heart conditions diagnosis, it cannot by itself be a heart disease predictor, but still crucial in determining the health of the heart. TPE is a type of Bayesian optimization algorithm that is predominantly applied in hyperparameter tuning of predictive models. TPE can efficiently search the hyperparameter space as it creates a probabilistic model of the objective function and at every iteration pushes the most optimal hyperparameters and therefore increases the entire model's performance. This approach acts to augment the effectiveness of the models, particularly in complex prediction tasks. Convolutional Neural Networks (CNNs) are multi-layer perceptron neural networks used for images recognition, hence composed of filter layers, pooling layers, and fully connected layers. Their advantages include the ability to capture spatial features making them powerful in image classification and object detection tasks. The combination of TPE optimization with CNNs results in improved performance because hyperparameter tuning is automated and an optimal setting which maximizes the accuracy all that is required, with values as high as 87.3% accuracy in heart disease prediction. © 2024 IEEE.

关键词： Multilayer neural networks

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The Analysis of Srgb Color Space Based Density for Brain Tumor Segmentation 7th

The Analysis of Srgb Color Space Based Density for Brain Tum...

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7th International Symposium on Intelligent Informatics, ISI 2022

作者： Gangadharappa, S. Naveena, C. Aradhya, V. N. Manjunath Department of Computer Science and Engineering SJBIT Karnataka Bengaluru India Department of Computer Applications JSS Science and Technology University Mysuru Mysore India

ISBN: (纸本)9789811980930

Medical image processing is one of the significant fields to identify the diseases as earlier to diagnose them appropriately. The brain tumor segmentation process is sub branch of a medical image processing field. The computer vision and machine learning techniques provide an effective channel for the medical practitioners for diagnosing the diseases in an effective method. This research article implements the Srgb-based density analysis for isolating the brain tumor space in MRI images. Intensity values of a given input are normalized using Srgb color space and Gaussian filter to distinguish the tumor region from the background. The adaptive threshold technique helps identify the possible tumor space in brain MRI samples. The actual brain tumor space is extracted by performing the region properties such as area and density function. Finally, the accurate tumor space is detected by applying morphological functions with eliminating possible false positives. Performance metrics including recall, precision, and F-measure are used to assess the effectiveness of the proposed approach. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Tumors

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e-Health: A Modern Approach to Healthcare Delivery 14

e-Health: A Modern Approach to Healthcare Delivery

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Maithani, Ankit Pradhan, Rahul Dit University Dehradun Department of Computer Science & Engineering Dehradun India Gla University Mathura Department of Computer Engineering & Applications Mathura India

ISBN: (纸本)9798350335095

e-Health had various attributes other than doctors, patients and medicine. Some of the components are health records, prescriptions, decision support system, online consultation videos, transcripts and telemedicine. Information technology is now integral and one of the core components of eHealth. Prescriptions generated from computer and Artificial intelligent expert systems, include diagnosis, basis of diagnosis, medicine, their side-effects and drug combination and their dosages. © 2023 IEEE.

关键词： Diagnosis

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A Novel Underwater Packet Scheduling based on Modified Priority Backpressure and Peak Age of Information approach

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2023年第5期7卷 55-62页

作者： Mary, A. Caroline Kumar, A. V. Senthil Chennamma, H.R. Department of Computer Science Hindusthan College of Arts and Science Coimbatore India Department of MCA Hindusthan College of Arts and Science Coimbatore India Department of Computer Applications Sri Jayachamarajendra College of Engineering India

Objective: To develop an effective underwater packet scheduling algorithm considering the freshness of information. Underwater Communication has gained interest in recent days and Energy consumption, Freshness of information is considered to be an important metric. applications include defense-based applications, environmental monitoring, pollution detection, and ocean study. This paper works on the Age of Information-based concepts to ensure that information’s freshness is sustained. Peak AoI is used as an important metric to assign priority to the packets resulting in less packet delay and loss. Congestion is reduced as it works on the principle of backlog. And normalized class-based AoI helps in giving importance according to the importance of packets. As the value of information reduces over time, the proposed technique helps to maintain freshness. By varying the number of nodes and speed of nodes simulation results are shown, and delay, throughput, packet delivery ratio, and energy consumed are calculated. Energy consumed is almost reduced to two third and as Information freshness is given importance, packet loss and delay are reduced. © 2023, International Journal of Systematic Innovation. All rights reserved.

关键词： Backpressure Peak AoI Priority Underwater

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Network Media Platform to Build a Sustainable Development of Ecological Environment Using Blockchain Technology 3

Network Media Platform to Build a Sustainable Development of...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Sundar, G. Patchaiammal, P. Swetha, V.P. Rengarajan, Manikandan Satheesh, Nimmati Sasikala, P. Sindhi College Department of Computer Applications Chennai600077 India Panimalar Engineering College Department of MBA Chennai India RandD Institute of Science and Technology Vel Tech Rangarajan Dr. Sagunthala Avadi Chennai6000627 India PSNA College of Engineering and Technology Department of Computer Applications Dindigul India Department of Computer Science Bangalore India

ISBN: (纸本)9798350396157

Social technology has grown rapidly and effectively as a result of new information technology. The importance of the big data approach, which was among the most prominent current occurrences, has improved as a result of the amount of information generated from social media. For the installation of renewable energies, the Energy Network seems to have become a central feature. Because there are so many different sustainable energy sources and how they interact with the grid, the system is extremely complex and presents a number of intrinsic challenges, such as the coordination and monitoring of distributed sustainable energy generation, which can be extremely difficult to handle. This study presents a blockchain-based approach for managing digital information in a network as a solution to these problems. Blockchain is a distributed ledger that implements agreement, inevitability, and transparency for online data using encryption schemes, hash chains, and consensus mechanisms. The suggested approach can employ blockchain's features to achieve efficient network content creation management, copyright management, system administration, and user instructional techniques. Our suggested method can consistently provide the network media platform with crucial assistance, enabling it to pursue long-term, ecologically sustainable expansion. © 2024 IEEE.

关键词： Smart contract

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A Hybrid Deep Learning Model with Consensus-Based Feature Selection for DDoS Attacks Detection in SDN 4

A Hybrid Deep Learning Model with Consensus-Based Feature Se...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Kachavimath, Amit V. Narayan, D.G. Department of Master of Computer Applications KLE Technological University Hubballi580031 India School of Computer Science and Engineering KLE Technological University Hubballi580031 India

Software-Defined Networking (SDN) increases network flexibility by decoupling network control from hardware. However, this also makes networks more vulnerable to Distributed Denial of Service (DDoS) attacks, that can severely disrupt operations. Existing detection methods typically focus on specific DDoS attacks, highlighting the necessity for more comprehensive detection strategies. Our proposed methodology presents a resilient technique for detecting DDoS attacks by employing a hybrid deep learning approach. Utilizing the InSDN dataset tailored for SDN environments, we employ an advanced feature selection process based on a consensus approach to identify the best eight features, enhancing detection accuracy and efficiency. Cross entropy is utilized at the control plane to detect anomalous activity by computing the entropy between source IP (Internet Protocol) and destination IP data to identify DDoS attacks. Our proposed model integrates Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) networks, achieving a detection accuracy of 99.9%. The CNN provides efficient spatial feature extraction, while LSTM captures temporal dependencies, enhancing the model's capability to detect complex attack patterns. We also implement a comprehensive evaluation framework, including metrics such as model loss, model accuracy, and confusion matrix. © 2025 The Authors. Published by Elsevier B.V.

关键词： Feature Selection

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Efficiency beyond Limits: LSTM and k-NN Synergy for Forecasting Energy Needs in Data Centers

Efficiency beyond Limits: LSTM and k-NN Synergy for Forecast...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Mogal, Amit K. Sonaje, Vaibhav P. Kumar, C.H.N. Santhosh Devi, V. Chithra Gottumukkala, Prasanthi Satheesh Kumar, D. Sandip University School of Computer Science and Engineering Department of Computer Science & Application Maharashtra Nashik India Sandip University School of Computer Science and Engineering Department of Computer Science & Applications Maharashtra Nashik India Anurag Engineering College Department of CSE Telangana Kodada India PSNA College of Engineering and Technology Tamilnadu Dindigul India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Hyderabad India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350361537

The objective of this study is to examine the effectiveness of a hybrid methodology that combines Long Short-Term Memory (LSTM) and k-Nearest Neighbors (k-NN) models in the context of energy prediction within data centres. The objective of this study is to improve the accuracy of predictions and the efficiency of operations by utilising the combined capabilities of these two machine learning approaches. The proposed LSTM-kNN model has superior performance compared to individual LSTM and k-NN models, as evidenced by a thorough assessment on sample data. It achieves a Mean Absolute Error (MAE) of 9.45, Root Mean Square Error (RMSE) of 12.97, and a coefficient of determination (R2) value of 0.89. The importance of model synergy in enhancing the accuracy of energy forecasts is shown by comparative analysis. The results of this study provide a significant contribution to the advancement of energy management methods in data centres. These findings provide vital insights for stakeholders, enabling them to optimise resource allocation, capacity planning, and sustainability initiatives. Proactive energy optimisation measures and cost-effective operational decisions are examples of practical applications. Potential avenues for future study encompass the exploration of supplementary machine learning techniques and the examination of the influence exerted by external factors on patterns of energy usage. In summary, this work highlights the significance of incorporating machine learning methods to tackle intricate issues in energy prediction and control in data centre settings. © 2024 IEEE.

关键词： k-nearest neighbors

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Optimization of Speech Emotion Recognition Through Advanced Ensemble Learning Techniques 9

Optimization of Speech Emotion Recognition Through Advanced ...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Prathyakshini Prathwini Keerthana Department of Information Science and Engineering Nitte Karkala India Department of Master of Computer Applications Nitte Karkala India Nitte Karkala India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377972

Recognition of emotion in speech (RES) is generating significant interest due to its promise to improve human-computer interaction through precise identification of emotions from speech signals. This work investigates how well deep learning methods advance RES accuracy. The study makes it possible to analyze a variety of speech patterns, emotional expressions, and demographic information all of which are crucial for developing trustworthy RES models. According to the study, CNNs demonstrated their ability to capture spatial characteristics from voice spectrogram representations with an amazing accuracy rate of 93%. RNNs performed exceptionally well at modeling temporal dependencies and capturing the sequential nature of speech, with an accuracy rate of 90%. Both models performed well, but some emotions such 'angry' and 'surprise' were more accurately detected than others, including 'disgust' and 'happy,' suggesting room for improvement. © 2024 IEEE.

关键词： Adversarial machine learning

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