Cloud Communication Research Articles

Analysis of SR-IOV in Docker containers using RTT measurements

Cloud computing, a central pillar of modern IT infrastructure, faces constant challenges in provisioning and optimizing network performance, specifically regarding low-latency communication. This study investigates the impact of Single Root I/O Virtualization (SR-IOV) as a critical Quality of Service (QoS) enabler in virtualized environments. Data plane innovative technologies for virtual servers, especially SR-IOV technology, emerged as a promising solution adopted in data centers. When combined with Peripheral Component Interconnect (PCI) Passthrough in Docker environments, SR-IOV promises significant network performance gains. Our rigorous experimental methodology demonstrates that integrating SR-IOV reduces Round-Trip Time (RTT) latency by up to 15 times compared to the traditional Linux based Bridge configuration used in Docker, without significant additional costs. This research is particularly relevant for system administrators, data center professionals, and network traffic engineers, providing them valuable information into optimizing communication in cloud computing environments. By addressing this critical gap in knowledge, our study serves as a practical guide for the effective implementation these emerging technologies for network virtualization. In terms of practical applicability, the results raise valuable insights into the performance and implications of implementing SR-IOV and PCI Passthrough in a Docker environment. As a result, more informed decisions are tailored to the specific requirements of different usage scenarios.

A Unification of Fog-Cloud Computing for Data Agitation and Guard Intensification in Industrial Health Care Security

The development of Fog computing is a decentralized environment in which the data processing, storage and applications are processed between the located server in a cloud environment. By increasing the Internet of things (IoT) and remote storage, the communication in cloud become more sophisticated by processing the data safely and securely. Healthcare data is important which contains medical information and processed centrality in the public environment through IoT, due to increasing security breaches they need to protect depends on the security applicants. All over the centralized data computing are accessed by virtual environment through remote protocol doesn’t provide safety in healthcare industry. To resolve this problem, to propose as Unification of fog-cloud computing for data agitation and guard intensification (DA-GI) in Industrial Health Care Security. The Medical Data Health-Care (MDHC) records are stored in Cloud datacenters and the Fog layer based on the guard intensity and the key is provoked for ingress the file. The activity logs are controlled and monitoring from cloud serves sustains the Activity Log, Risk Table and Health Records. To introduce a cryptographic approach based on advanced encryption standards (AES) to protect data and authenticity verification server. The key verification process based on security gateway, Fog cloud server depends on user access rights provided to the user. During the key validation, role of permission to the user s verified and agitate to allow access rights to the verified service access. The proposed system produce high security compared to the other system as well in all security concerns of role, authentication and verification to process data safely.

Development of Learning Management Model of Collaborative Learning and Positive Coaching on Cloud Computing System

This study aims to systematically review the learning management model on cloud computing system using collaborative learning and positive coaching. The author uses the literature review to synthesize research as the main process to study related documents, books, research studies, and articles both domestically and internationally. They are selected by purposive sampling for 69 topics. The documents are analyzed by systematic review. This study is a qualitative study and uses content analysis by using keywords to discover three issues as follows: Cloud computing, Collaborative learning, and Positive coaching. From the results of this study, Cloud computing comprises six components: Cloud learning content, Cloud collaboration, Cloud communication, Cloud creation, Cloud presentation, and Cloud evaluation. The collaborative learning process has five steps as follows: Engagement phase, Exploration phase, Transformation phase, Presentation phase, and Reflection phase. Positive coaching comprises five process as follows: Construct the positive relation, Set a clear and challenging goal, Implement a challenging goal to smooth practice, Set a more challenging goal and practice smoothly, and Evaluate and expand the experience, reward the outcome and discovery.

Health of Things Melanoma Detection System—detection and segmentation of melanoma in dermoscopic images applied to edge computing using deep learning and fine-tuning models

According to the World Health Organization (WHO), melanoma is a type of cancer that affects people globally in different parts of the human body, leading to deaths of thousands of people every year worldwide. Intelligent diagnostic tools through automatic detection in medical images are extremely effective in aiding medical diagnosis. Computer-aided diagnosis (CAD) systems are of utmost importance for image-based pre-diagnosis, and the use of artificial intelligence–based tools for monitoring, detection, and segmentation of the pathological region are increasingly used in integrated smart solutions within smart city systems through cloud data processing with the use of edge computing. This study proposes a new approach capable of integrating into computational monitoring and medical diagnostic assistance systems called Health of Things Melanoma Detection System (HTMDS). The method presents a deep learning–based approach using the YOLOv8 network for melanoma detection in dermatoscopic images. The study proposes a workflow through communication between the mobile device, which extracts captured images from the dermatoscopic device and uploads them to the cloud API, and a new approach using deep learning and different fine-tuning models for melanoma detection and segmentation of the region of interest, along with the cloud communication structure and comparison with methods found in the state of the art, addressing local processing. The new approach achieved satisfactory results with over 98% accuracy for detection and over 99% accuracy for skin cancer segmentation, surpassing various state-of-the-art works in different methods, such as manual, semi-automatic, and automatic approaches. The new approach demonstrates effective results in the performance of different intelligent automatic models with real-time processing, which can be used in affiliated institutions or offices in smart cities for population use and medical diagnosis purposes.

Real-Time Fire Surveillance with Machine Learning Twilio Integration

Fire threats pose significant risks to people and property, necessitating efficient surveillance systems. A complete fire surveillance system is proposed using machine learning and artificial intelligence to monitor fire incidents in real time. The system is built using React.js for frontend development and MongoDB for backend storage. Node.js is integrated for server-side operations, ensuring data management and user interaction. The system sends alerts via WhatsApp when image analysis identifies a fire concern, leveraging Twilio for seamless messaging. Robo Flow simplifies computer vision model management, while YOLOv8, a cutting-edge object recognition algorithm, enhances detection speed and accuracy. YOLOv8 is widely used in real-time object identification applications like robotics, autonomous cars and surveillance systems. Twilio is a cloud communication platform that allows developers to integrate voice, video, and SMS into their apps, enabling notifications, alarms, and two-way communication. The research utilizes Machine Learning, React.js, MongoDB, YOLOv8, and Twilio to offer efficient real-time fire surveillance.

Wireless 6G Cloud Communication Based Security Analysis Using Machine Learning in Internet of Medical Things (IoMT)

Wireless 6G Cloud Communication Based Security Analysis Using Machine Learning in Internet of Medical Things (IoMT)

Vehicle Collaborative Partial Offloading Strategy in Vehicular Edge Computing

Vehicular Edge Computing (VEC) is a crucial application of Mobile Edge Computing (MEC) in vehicular networks. In VEC networks, the computation tasks of vehicle terminals (VTs) can be offloaded to nearby MEC servers, overcoming the limitations of VTs’ processing power and reducing latency caused by distant cloud communication. However, a mismatch between VTs’ demanding tasks and MEC servers’ limited resources can overload MEC servers, impacting Quality of Service (QoS) for computationally intensive tasks. Additionally, vehicle mobility can disrupt communication with static MEC servers, further affecting VTs’ QoS. To address these challenges, this paper proposes a vehicle collaborative partial computation offloading model. This model allows VTs to offload tasks to two types of service nodes: collaborative vehicles and MEC servers. Factors like a vehicle’s mobility, remaining battery power, and available computational power are also considered when evaluating its suitability for collaborative offloading. Furthermore, we design a deep reinforcement learning-based strategy for collaborative partial computation offloading that minimizes overall task delay while meeting individual latency constraints. Experimental results demonstrate that compared to traditional approaches without vehicle collaboration, this scheme significantly reduces latency and achieves a significant reduction (around 2%) in the failure rate under tighter latency constraints.

Machine learning approach to optimal task scheduling in cloud communication

Cloud communication is a combination of distributed computing and parallel computing. One of the biggest challenges in cloud communications is task scheduling, which is difficult due to the nondeterministic polynomial completeness (NP) of cloud systems. To solve this problem, various approximation techniques based on swarm intelligence have been developed. This study proposes a dual machine learning strategy using kmeans to optimize performance and aid in selecting cloud scheduling technologies. The first technique is called Efficient Kmeans (Ekmeans) and the second technique is called Kmeans HEFT (KmeanH), where HEFT stands for Heterogeneous Earliest End Time. Our main contribution is to reduce processing time and increase speed and efficiency for a given set of tasks. We evaluate the impact of both algorithms on different virtual machines (ranging from 2 to 32) and task sizes (ranging from 50 to 1000).

Retracted: An IoT-Enabled Intelligent and Secure Manufacturing Model Using Blockchain in Hybrid Cloud Communication System

Retracted: An IoT-Enabled Intelligent and Secure Manufacturing Model Using Blockchain in Hybrid Cloud Communication System

Региональное социально-политическое взаимодействие онлайн и офлайн: возможности сравнения и измерения на примере экологической повестки

Regional socio-political interaction is characterized by a combination of the development agenda and local projects implemented in the “home” space. Currently, formats of socio-political interaction intersect everywhere in real and virtual, online and offline interactions. A methodological turn to the study of simultaneous formats of online and offline interactions, in our opinion, allows us to consider the combination of social and political interaction of regional communities in the totality of network techniques of virtual communications. The aim of the study is to consider online and offline formats of socio-political interaction by collecting current issues on the platform of the regional environmental agenda. There is a synchronization of information layers and communication perspectives of the experience of life “here and now”, network and cloud communication. The environmental agenda is highly attractive in discussing and highlighting the socio-political problems of the region. The article uses data obtained by the authors during the Kuzbass-Politics monitoring study aimed at studying the opinions of residents of the cities of Kemerovo Oblast. The results of the surveys of 2017, 2018, 2019, and 2023 were analyzed. Big data collected using data-mining tools was also used. For digital analysis, we used a collected dataset of current data from regional network communities of Kuzbass on VKontakte. The sources of the data were messages (posts) in selected 56 unique communities of Kuzbass; comments to posts with the indication of community id, community name, post id, community territory label (period 01 January 2022 – 18 November 2022). The data obtained show the relevance of environmental issues in offline sociopolitical interaction, and a tendency towards an increase in positive assessments of the environmental situation by citizens over the past four years. Based on the results of the analysis of network data, we can observe the absence of the direct mention of ecology and related issues on the agenda of regional network communities and top socio-political issues. Internet technologies in the context of the study of regional socio-political interaction are understood in three ways: a source of information, a communication medium, and a conductor into virtual public arenas. The results of the study showed the intersection of environmental topics in regional field studies and descriptions of network agendas analyzed using data-mining tools. Consequently, the environmental agenda can serve as a point of assembly of latent regional issues with the subsequent transfer of relevance and mobilization activities to related and sometimes unobvious topics. The regional agenda indicates the main directions for the development of further research: the ability to focus on the use of digital technologies and networking tools and an emphasis on the content of communication and the activities of actors.

Adaptive Resource Allocation Algorithm for 5G Vehicular Cloud Communication

Adaptive Resource Allocation Algorithm for 5G Vehicular Cloud Communication

Research into the world of Cloud-Virtual Reality

Virtual Reality technology has not been able to penetrate the mainstream consumer market due to restrictions on hardware and content. With the advancement of cloud computing technologies, Virtual Reality hardware and technology applications may now be supported strongly. The metaverse, cloud VR gaming, and cloud VR stimulator are just a few of the real-world applications that are now employing cloud computing in Virtual Reality. We can lower the cost of Virtual Reality technology which would begin a new era of many users to apply cloud-based technologies. With the aid of technology, it will be possible to operate from anywhere without having to transport a lot of equipment. The performance, usability, and compatibility of Virtual Reality software and hardware can be improved by cloud computing on the level of hardware and content. The VR dataset is made available for public collaboration through the Internet using a VR-Cloud server. The digital asset that represents the layout of our pedestrian bridge comprises images of all the streets, buildings, trees, and other urban amenities. In the virtual environment of the metropolitan area, where they run and stroll according to predetermined behaviour scenarios, the cars and people are created and entered. By employing cloud communication software to analyse simulations of vehicles and pedestrian crowds and to debate design ideas, users share the VR reality by connecting to the VR Cloud servers from remote devices. This research demonstrates how VR-cloud may be combined with Neuralink and NFT in the virtual world for future enhancement. This paper is a research note on cloud computing and its application using the Virtual Reality (VR) platform for the modern world with futuristic innovations. Keywords: Cloud Computing, Metaverse, Neuralink, Virtual Reality, VR-Cloud.)

Identity and Access Control Techniques for Enhanced Data Communication in Cloud

The preservation of sensitive data and prevention of unauthorized access are critical objectives in cloud computing environments, necessitating the implementation of robust security measures. The present study delves into the amalgamation of the Hierarchical Role Based Access Control Model (HR-ACM), Composikey, a composite key encryption algorithm, and Interactive Tree Based zero knowledge protocol (ITZKP) to construct a robust security framework for cloud security. This study presents an investigation into the viability and efficacy of integrating HRACM, Composikey, and ITZKP as a means of augmenting security measures and reducing potential hazards in cloudbased systems. The execution times comparison of the Composikey, IBPRE, and KPRE models were compared for different filesizes (1 KB to 2 GB) to evaluate the performance of the HRACM method. As roles increase, RBAC a execution time increases significantly. The HRACM Method takes 0.001 to 0.002 seconds to implement across all role counts. The results show that the HRACM Method is more efficient and time-effective than the RBAC Method. The response time measured in microseconds for the existing ZKP and Libra systems for different file sizes, ranging from 1KB to 1 GB. The results reveal that the verifier time for the ITZKP system aligns closely with the existing systems, demonstrating its ability to perform efficient verification processes. These findings collectively demonstrate the potential of the integrated security framework in enhancing cloud security.

Stochastic Gradient Deep Multilayer Neural Network based Linear Congruential Generative Cryptosystem for Secured Data Communication in Cloud

Cloud computing is a kind of distributed computing that use a vast network of interconnected resources accessible over the internet. Security is a crucial concern in cloud computing due to the fact that users save their data on the cloud for convenient access from any location and at any time. Consequently, many users are worried about safeguarding their sensitive data in an unsafe location. Therefore, cloud computing architecture requires an innovative cryptographic method that ensures the secrecy, authenticity, integrity, and non-repudiation of data transfer in the cloud. A new technique called SEMcrypt, which stands for Stochastic Gradient DEep Multilayer Neural Network based Linear Congruential Generative Cryptography, has been developed to enhance secure data transmission. SEMcrypt ensures higher data confidentiality and reduces the time required for communication between the cloud user (i.e., patient) and the server. The SEMcrypt approach has two distinct processes: categorization and secure data transport. Initially, the data is gathered from the patients and is used as input for the stochastic gradient regularised deep multilayer neural network. The deep neural network consists of one input layer, two hidden layers, and one output layer. At first, the information is gathered from the patients and sent to the input layer. Next, the patient data that has been gathered is examined in hidden layer 1 using the generalised Tikhonov regularisation function. The patient data that has been analysed is sent to hidden layer 2. The hyperbolic tangent activation function is used at that layer to classify the patient data. Subsequently, the categorised data undergoes encryption via the use of Linear Congruential Generative Goldwasser-Micali encryption, ensuring safe transfer of the data. Subsequently, the encrypted data is sent to the cloud server. The patient data is encrypted on the server side using the Linear Congruential Generative Goldwasser-Micali decryption technique to prevent unauthorised access or assaults. Consequently, the authorised recipient receives the unaltered information, which is then kept in the database for further analysis. Secured data transmission is achieved by ensuring better levels of data confidentiality and reducing the time required for the process. The experimental assessment focuses on criteria such as the time it takes to generate keys, the level of data confidentiality and integrity, the computing time, and the accuracy of categorization. The empirical findings demonstrate that our suggested SEMcrypt approach delivers efficient performance outcomes by attaining superior levels of data confidentiality and integrity within a minimal timeframe.

Smart Manufacturing Implementation of a Continuous Downstream Precipitation and Filtration Process for Antibody Purification

Abstract Recently, continuous bioprocessing has gained momentum in biomanufacturing and can alleviate many of the hurdles faced in batch or semi-batch operations. Moreover, the parallel development of smart manufacturing (SM) allows the rapid small-scale prototyping and large-scale implementation of continuous bioprocesses. With this background, this paper presents the laboratory-scale implementation of a continuous precipitation-filtration process that can ultimately be used for therapeutic protein capture purification. The experimental setup includes four static mixers, four peristaltic pumps, one hollow fiber dewatering filtration module, and multiple pressure sensors and weigh scales. The system also includes an in-line advanced microscopic particle imaging probe that provides real-time images and derived metrics of the precipitate particle morphologies and a fiber optic 880 nm optical absorbance probe. A polyclonal human serum antibody mixture (hIgG) (10 g/L) was used as a stand-in for a monoclonal antibody therapeutic along with 7 % w/v polyethylene glycol (PEG, volume exclusion agent) and 10 mM zinc chloride (cross-linking agent) as the precipitants to demonstrate the principles of operation and control of a precipitation-based process using SM technology. An integrated input/output (I/O) system was used to acquire pressure, flow rate, and weigh scale data and also to communicate with the pumps to change flow rates in real-time. Edge computers communicate with the I/O system and the imaging probe and host the software layer. The software layer enables real-time data acquisition, data-driven and first-principles model predictions, closed-loop control of precipitate particle morphology using pump flow rate of PEG, and cloud communications with the Clean Energy Smart Manufacturing Innovation Institute Smart Manufacturing Innovation Platform. The paper presents the initial results obtained with this integrated system, demonstrating the potential of SM strategies to enhance the production of life-saving biopharmaceutical products.

Enhancing Cloud Communication Security: A Blockchain-Powered Framework with Attribute-Aware Encryption

The global production of information continuously increases in quantity and variety. However, the tools and technologies developed to handle such large volumes of data have not adequately met the security and privacy requirements. Existing cloud security systems, often managed by a trusted third party, are susceptible to various security risks. To address these challenges and ensure the protection of personal information, blockchain technology emerges as a crucial solution with substantial potential. This research uses the blockchain-powered attribute-aware encryption method to establish a real-time secure communication approach over the cloud. By employing attribute-based encryption technology, data owners can implement fine-grained search permissions for data users. The proposed solution incorporates accessible encryption technology to enable secure access to encrypted data and facilitate keyword searches on the blockchain. This study provides a functional comparison of recently developed attribute-based encryption algorithms. The access control strategy comprises two access tree types and a linear secret-sharing system, serving as the main components. The elliptic curve’s base field was set to 512b, and the bilinear pairing parameter type used was Type-A. This approach involves storing keywords on a remote server and encrypting them using attribute-based encryption. Furthermore, the encrypted data blockchain and the corresponding ciphertext are stored in the blockchain. Numerical experiments were conducted to evaluate the system’s key generation, trapdoor building, and keyword retrieval capabilities.

Communications with guaranteed bandwidth and low latency using frequency-referenced multiplexing

Emerging cloud applications such as virtual reality and connected car fleets demand guaranteed connections, as well as low and stable latency, to edge data centres. Currently, user–cloud communications rely on time-scheduled data frames through tree-topology fibre networks, which are incapable of providing guaranteed connections with low or stable latency and cannot be scaled to a larger number of users. Here we show that a frequency-referenced multiplexing method can provide guaranteed bandwidth and low latency for time-critical applications. We use clock and optical frequency synchronization, enabled by frequency comb and signal processing techniques, to provide each user with dedicated optical bandwidth, creating scalable user–cloud upstream communications. As a proof of concept, we demonstrate a frequency-division multiplexing system servicing up to 64 users with an aggregate bandwidth of 160 GHz, exhibiting a data rate of up to 4.3 Gbps per user (240.0 Gbps aggregated capacity considering a 200 GHz wavelength band) with a high receiver sensitivity of –35 dBm.

Digital Architecture for Monitoring and Operational Analytics of Multi-Vector Microgrids Utilizing Cloud Computing, Advanced Virtualization Techniques, and Data Analytics Methods

Microgrids are considered a viable solution for achieving net-zero targets and increasing renewable energy integration. However, there is a lack of conceptual work focusing on practical data analytics deployment schemes and case-specific insights. This paper presents a scalable and flexible physical and digital architecture for extracting data-driven insights from microgrids, with a real-world microgrid utilized as a test-bed. The proposed architecture includes edge monitoring and intelligence, data-processing mechanisms, and edge–cloud communication. Cloud-hosted data analytics have been developed in AWS, considering market arrangements between the microgrid and the utility. The analysis involves time-series data processing, followed by the exploration of statistical relationships utilizing cloud-hosted tools. Insights from one year of operation highlight the potential for significant operational cost reduction through the real-time optimization and control of microgrid assets. By addressing the real-world applicability, end-to-end architectures, and extraction of case-specific insights, this work contributes to advancing microgrid design, operation, and adoption.

Secured Transfer and Storage Image Data for Cloud Communications

In cloud computing, resources are used to communicate instead of local servers or individual devices. However, sharing resources among several users is a difficult issue in cloud communication. Cryptography and steganography techniques are used for cloud storage to address data security challenges. This paper presents a novel method for securely encrypting image data for transmission and link exchange with a cloud storage service. There are two phases to accomplish the encryption process, the first phase encrypts the image file by XORing it with a random key that is generated by a new hybrid of the chaotic map. The second phase converts the encrypted image format to audio format to add another layer of security and improve secure image data transfer. The random key is generated using a hybrid chaotic map and has the benefit of having more than 10256 key spaces and the necessary level of security. Based on a statistical analysis of the encryption, the quality of the image is evaluated using several criteria, and the results demonstrate the algorithm's ability to accomplish resist encryption

An Ant-colony Based Model for Load Balancing in Fog Environments

Delay-sensitive applications are becoming more and more in demand as a result of the development of information systems and the expansion of communication in cloud computing technologies. Some of these requests will be overlooked in cloud environments due to the communication delay between the processing center and the client’s request. The ‘fog-based computing paradigm’, a novel processing model, can be added to cloud computing to help with the aforementioned issues. The performance of computing systems is always influenced by latency. In this paper, we focus on balancing the load on the fog nodes to lower the latency. It is also a crucial component of fog computing devices. It necessitates the use of load-balancing algorithms to select the optimal hosts, resulting in an even distribution of the load on the available resources.We provide a load-balancing approach based on the Ant-colony optimization algorithm’s latency rate for responding to tasks. A random data set evaluation of this model reveals shorter response times than those of earlier strategies suggested in this field.