Cloud Computing Paradigm Research Articles

A Block-chain Based Mechanism for Securely Storing Data on Cloud and IOT

The burgeoning paradigm of cloud computing has made a number of tools available to researchers. It is a cutting-edge technology that keeps revolutionizing many sectors in every imaginable method. It is a promising technology that gives users and researchers new perspectives, development, and many amazing factors. The integration of cloud and block-chain technology and internet of things (IOT). The benefits both service providers and customers by enabling the use of resources that can be authenticated while protecting anonymity. This joining method is distinct and environmentally. This cooperative method sheds light on the different paths or facets of computers that can solve issues other than security-related ones. The primary problem at the moment is cloud security. For service providers, it's critical to protect client privacy and avoid data loss. Using client data privacy solutions, the current review study focused on block-chain with combination of IOT and data security. In order to strengthen security and expand the power or utility of cloud systems, a comprehensive examination is also carried out. Block-chain technology has the ability to increase speed and anonymity while providing numerous advantages to cloud-based and IOT based applications.

A Brief Systematics Review and Visualization of Smart Cities based on Fog and Big Data Analysis Technology

The fog computing is based on IoT, and same as clouds are on the edge of a network between a device and the cloud, which can provide very close service to the device in real time. cloud computing frameworks also extend support to emerging application paradigms such as IoT, Fog computing, Edge, and Big Data through service and infrastructure. the ubiquitous deployment of sensors in smart cities requires a high-performance computing paradigm to support big data analysis with smart technologies and communications in IoT, providing location-awareness and latency-sensitive computing near the data sources (i.e., at the edge of the network). Cloud Computing paradigms widely used in enterprises to address the emerging challenges of big data analysis because of its scalable and distributed data management scheme. However, data centers in the Cloud faces great challenges on the burden of exploding amount of big data and the additional requirements of location awareness and low latency at the edge of network necessary for smart cites. the widely distributed sensor networks generate massive volume of data, which leads to a “Big Data “analysis challenge. The machine-to-machine(M2M) communication among massive numbers of sensors will dominate future communication network traffic, namely Internet of Things, instead of traditional Internet of Contents (IoC) in human-to-human and human-to-machine communication. In this review system where we took the research article around 33 from the different database and also taken 1987 datasets form the PubMed database from 1959 to 2024 (02 June 2024). In addition, depending on the different requirement which is partially processed the data. Then the gathered data is stored at the ledger unit through the smart cities based on the fog computing and big data technology. The hashing identity-based data encryption is a public key encryption which uses a digital signature to encrypt the data to prevent the information from the third party.

Multi-objective hybrid optimized task scheduling in cloud computing under big data perspective

The new and rising paradigm of cloud computing offers customers various possibilities of task computation based on their desires and choices. Customers receive services from cloud computing systems as a utility. Customers are enthusiastic about low-cost service availability and task completion times that are kept to be minimum. To achieve client fulfilment, the service provider must schedule the jobs to the right resources if the cloud server gets many user requests. The rapid growth in data volume necessitates petabytes processing of data each day. Unstructured, semi-structured, and structured data are all described in terms of their rapid growth and availability. In order to make correct and timely decisions, it must be processed appropriately. In this research, we present BWUJS (Black Widow Updated Jellyfish Search), a multi-objective hybrid optimization-based task scheduling algorithm. This work considers task generation from the Bigdata perspective. The clustering of tasks is performed via the Map Reduce framework with an Improved K-means clustering model. After task clustering, the task priority estimation is performed. Finally, the scheduling is performed via BWJSU based on certain constraints like priority, makespan, completion time, resource utilization, and degree of imbalance.

Deep Reinforcement Learning techniques for dynamic task offloading in the 5G edge-cloud continuum

The integration of new Internet of Things (IoT) applications and services heavily relies on task offloading to external devices due to the constrained computing and battery resources of IoT devices. Up to now, Cloud Computing (CC) paradigm has been a good approach for tasks where latency is not critical, but it is not useful when latency matters, so Multi-access Edge Computing (MEC) can be of use. In this work, we propose a distributed Deep Reinforcement Learning (DRL) tool to optimize the binary task offloading decision, this is, the independent decision of where to execute each computing task, depending on many factors. The optimization goal in this work is to maximize the Quality-of-Experience (QoE) when performing tasks, which is defined as a metric related to the battery level of the UE, but subject to satisfying tasks’ latency requirements. This distributed DRL approach, specifically an Actor-Critic (AC) algorithm running on each User Equipment (UE), is evaluated through the simulation of two distinct scenarios and outperforms other analyzed baselines in terms of QoE values and/or energy consumption in dynamic environments, also demonstrating that decisions need to be adapted to the environment’s evolution.

Deep Reinforcement Learning for Workload Prediction in Federated Cloud Environments.

The Federated Cloud Computing (FCC) paradigm provides scalability advantages to Cloud Service Providers (CSP) in preserving their Service Level Agreement (SLA) as opposed to single Data Centers (DC). However, existing research has primarily focused on Virtual Machine (VM) placement, with less emphasis on energy efficiency and SLA adherence. In this paper, we propose a novel solution, Federated Cloud Workload Prediction with Deep Q-Learning (FEDQWP). Our solution addresses the complex VM placement problem, energy efficiency, and SLA preservation, making it comprehensive and beneficial for CSPs. By leveraging the capabilities of deep learning, our FEDQWP model extracts underlying patterns and optimizes resource allocation. Real-world workloads are extensively evaluated to demonstrate the efficacy of our approach compared to existing solutions. The results show that our DQL model outperforms other algorithms in terms of CPU utilization, migration time, finished tasks, energy consumption, and SLA violations. Specifically, our QLearning model achieves efficient CPU utilization with a median value of 29.02, completes migrations in an average of 0.31 units, finishes an average of 699 tasks, consumes the least energy with an average of 1.85 kWh, and exhibits the lowest number of SLA violations with an average of 0.03 violations proportionally. These quantitative results highlight the superiority of our proposed method in optimizing performance in FCC environments.

Cloud Computing Paradigm in Academics

Cloud computing has gained significant popularity due to its convenience and accessibility, especially with the surge in remote work arrangements during the global lockdown. Integrating cloud computing with education offers a unique and high-quality experience for educators, administrative staff, parents, guardians, and students. This paper emphasizes the importance of embracing a cloud computing paradigm in educational institutions, backed by extensive research in ed-tech cloud-based concepts. The study examines the intricacies associated with adopting cloud computing, with a focus on the initial decision-making process. Furthermore, the paper conducts a comparative analysis of the three leading cloud computing platforms to identify the most suitable and optimal choice for implementing the suggested model. By leveraging cloud services, educational institutions can enhance their capabilities and provide an enriched learning environment for all stakeholders.

Network-assisted processing of advanced IoT applications: challenges and proof-of-concept application

AbstractRecent advances in the area of the Internet of Things shows that devices are usually resource-constrained. To enable advanced applications on these devices, it is necessary to enhance their performance by leveraging external computing resources available in the network. This work presents a study of computational platforms to increase the performance of these devices based on the Mobile Cloud Computing (MCC) paradigm. The main contribution of this paper is to research the advantages and possibilities of architectures with multiple offloading options. To this end, a review of architectures that use a combination of the computing layers in the available infrastructure to perform this paradigm and outsource processing load is presented. In addition, a proof-of-concept application is introduced to demonstrate its realization along all the network layers. The results of the simulations confirm the high flexibility to offload numerous tasks using different layers and the ability to overcome unfavorable scenarios.

SLA Aware Task-Scheduling Algorithm in Cloud Computing Using Whale Optimization Algorithm

Task scheduling in Cloud Computing paradigm poses new challenges for cloud provider as heterogeneous, diversified tasks arrived on to cloud console. To schedule these type of tasks efficiently on to virtual resources in cloud paradigm, an effective scheduler is needed, which precisely maps tasks to virtual machines by considering priorities of both tasks and VMs. Existing scheduling algorithms failed to map tasks precisely to virtual resources due to high dynamic nature in cloud environment which leads to increase of makespan and SLA violations will be increased. In this paper, authors proposed a task-scheduling mechanism, which considers task priorities and VMs. To model this scheduling paradigm we have chosen whale optimization through which our scheduler will take decisions for scheduling tasks precisely onto virtual resources in cloud environment. Entire simulation was carried out on CloudSim. Initially we have chosen random generated workload to run simulation and after that, we have considered a real-time workload named as BigDataBench and ran our simulation. Finally, we compared our proposed work with classical baseline mechanisms. From simulations we observed that proposed whale scheduler improved makespan for PSO, ACO, GA, and W-schedulers by 20.07%, 17.55%, 19.9%, and 6.35%, respectively, and 17.3%, 17.86%, 17.64%, and 5.93%, respectively, for BigDataBench workloads. SLA violations improved over PSO, ACO, GA, and W-Scheduler by 56.76%, 42.17%, 35.29%, and 24.53%, respectively, and 63.42%, 23.33%, 55.51%, and 40.1%, respectively, for BigDataBench workloads. From extensive simulation results, our proposed scheduler using whale optimization approach minimizes makespan and SLA violations to a great extent.

Automated Irrigation System Using Improved Fuzzy Neural Network in Wireless Sensor Networks

Irrigation plays a significant role in various agricultural cropping methods deployed in semiarid and arid regions where valuable water applications and managing are considered crucial concerns. Multiple factors such as weather, soil, water, and crop data need to be considered for irrigation maintenance in an efficient besides uniform manner from multifaceted and different information-based systems. A Multi-Agent System (MAS) has been proposed recently based on diverse agent subsystems with definite objectives for attaining global MAS objective and is deployed on Cloud Computing paradigm capable of gathering information from Wireless Sensor Networks (WSNs) positioned in rice, cotton, cassava crops for knowledge discovery and decision making. The radial basis function network has been used for irrigation prediction. However, in recent work, the security of data has not focused on where intruder involvement might corrupt the data at the time of data transferring to the cloud, which would affect the accuracy of decision making. To handle the above mentioned issues, an efficient method for irrigation prediction is used in this work. The factors considered for decision making are soil moisture, temperature, plant height, root depth. The above-mentioned data will be gathered from the sensors that are attached to the crop field. Sensed data will be forwarded to the local server, where data encryption will be performed using Adaptive Elliptic Curve Cryptography (AECC). After the encryption process, the data will be forwarded to the cloud. Then the data stored in the cloud will be decrypted key before being given to the decision-making module. Finally, the uniform distribution-based fuzzy neural network is formulated based on the received data information in the decision-making module. The final decision regarding the level of water required for crop fields would be taken. Based on this outcome, the water volve opening duration and the level of fertilizers required will be considered. Experimental results demonstrate the effectiveness of the proposed model for the United States Geological Survey (USGS) database in terms of precision, accuracy, recall, and packet delivery ratio.

Multi-agent deep reinforcement learning for online request scheduling in edge cooperation networks

Edge computing as a complementary paradigm of cloud computing has gained more attention by providing mobile users with diversified services at the network edge. However, the increasingly complex mobile applications put a heavier load on edge networks. It is challenging to provide concurrency requests with high-quality service processing, especially when the edge networks are dynamically changing. To address the above issues, this paper investigates the online concurrent user requests scheduling optimization problem in edge cooperation networks. We model it as an online multi-stage decision-making problem, where requests are divided into a group of independent and logically related sub-tasks. We proposed a centralized training distributed execution based multi-agent deep reinforcement learning technique to realize the implicit cooperation scheduling decision-making policy learning among edge nodes. At the centralized training stage of the proposed mechanism, a value-decomposition-based policy learning technique is adopted to improve the long-term system performance, while at the distributed execution stage, only local environment status information is needed for each edge node to make the request scheduling decision. Extensive experiments are conducted, and simulation results demonstrate that the proposed mechanism outperforms other request scheduling mechanisms in reducing the long-term average system delay and energy consumption while improving the throughput rate of the system.

Analysis of Time Factor with Resource Provisioning Frameworks in Cloud Environment for Improving Scheduling Performance

Time is critical in the resource provisioning process in the Cloud Computing paradigm when serving cloud resources to cloud users. It's difficult for a cloud provider to serve a large number of users while also reducing long wait times after they've submitted a request. It is possible to improve the time factor by using a systematic resource provisioning process. This paper examines several time-based resource provisioning frameworks in greater detail. Many researchers focused on various time parameters that assist cloud service providers in providing the best resource-serving services to their customers. The primary goal of this paper is to assist future researchers, as well as cloud providers in observing and selecting the best time-based resource provisioning technique also they can emphasize building a new dynamic resource provisioning paradigm in the future with this work’s observations. To validate these observations, a novel Particle Swarm Optimization (PSO) based model is designed in this text, which uses the selected time-based resource provisioning technique, and applies it to real-time cloud scenarios. It was observed that the proposed model was able to showcase better efficiency of scheduling, and optimum cloud utilization when compared with other time-based resource provisioning models for different cloud deployments

Taxonomy of Load Balancing Practices in the Cloud Computing Paradigm

Rapid growth in communication technology allows users location-independent access to IT infrastructure at pay-per-use via cloud computing. This has paved a new paradigm in information processing for the consumers. Due to Cloud's inherent characteristics, most service providers shift to the Cloud and its data centers. To retain Cloud's services' reliability, it's essential to carry out the minimum latency tasks and cost-effectively. Various techniques to improve performance and use of assets are focused on load control, task management, resource management, service quality, and workload management. Data load balancing helps data centers to avoid overload/underload of virtual machines, a difficulty in the world of cloud computing. This study reports a state-of-the-art analysis of current load balancing approaches', problems, and complexities to design more successful algorithms.

Flow Assignment and Processing on a Distributed Edge Computing Platform

The evolution of telecommunication networks toward the fifth generation of mobile services (5G), along with the increasing presence of cloud-native applications, and the development of Cloud and Mobile Edge Computing (MEC) paradigms, have opened up new opportunities for the monitoring and management of logistics and transportation. We address the case of distributed streaming platforms with multiple message brokers to develop an optimisation model for the real-time assignment and load balancing of event streaming generated data traffic among Edge Computing facilities. The performance indicator function to be optimised is derived by adopting queuing models with different granularity (packet- and flow-level) that are suitably combined. A specific use case concerning a logistics application is considered and numerical results are provided to show the effectiveness of the optimisation procedure, also in comparison to a “static” assignment proportional to the processing speed of the brokers.

A Systematic Survey of Simulation Tools for Cloud and Mobile Cloud Computing Paradigm

Cloud computing (CC) provides fast and on-demand access to virtually unlimited computing resources. Mobile cloud computing (MCC) is a new technology that brings CC and mobile devices together. MCC allows mobile devices access to cloud services. Describe the basic architectures for various MCC applications, as well as a brief comparison of cloud and MCC. As CC becomes more common, researchers in this field may need to conduct real-world experiments in their studies. Configuring and running these experiments in real-world cloud environments is costly. As a consequence, modeling and simulation approaches are suitable solutions that can be used to simulate cloud computing environments. Several simulation tools tailored to CC have been developed. The most powerful simulation methods in this field of study are described in this article. Among them are CloudSim, CloudSim Plus, CloudAnalyst, iFogSim, and CloudReports.

SecEDMO: Enabling Efficient Data Mining with Strong Privacy Protection in Cloud Computing

Frequent itemsets mining and association rules mining are among the top used algorithms in the area of data mining. Secure outsourcing of data mining tasks to the third-party cloud is an effective option for data owners. However, due to the untrust cloud and the distrust between data owners, the traditional algorithms which only work over plaintext should be re-considered to take security and privacy concerns into account. For example, each data owner may not be willing to disclose their own private data to others during the cooperative data mining process. The previous solutions are either not sufficiently secure or not efficient. Therefore, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sec</u> ure and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> fficient <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> ata <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> ining <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</u> utsourcing (SecEDMO) scheme for secure outsourcing of frequent itemsets mining and association rules mining over the joint database (i.e., database aggregated from multiple data owners) in the paradigm of cloud computing. Based on our customized lightweight symmetric homomorphic encryption algorithm and a secure comparison algorithm, SecEDMO can ensure strong privacy protection and low data mining latency simultaneously. Moreover, the well-designed virtual transaction insertion algorithm can hide the information of the original database while still preserving the cloud’s ability to perform data mining over the obfuscated data. By evaluation of a numerical experiment and theoretical comparisons, the correctness, security, and efficiency of SecEDMO are confirmed.

Використання хмарних технологій для вирішення питань інтеграції інформації у багаторівневих системах управління

Introduction. Currently, the paradigm of cloud computing has become widespread in many areas of human life. Particular attention is paid to the use of cloud technologies in emergency response management systems. In Ukraine, such systems are multi-level, mainly with state administration. The use of cloud technologies also opens up new opportunities for the processes of information integration in these systems. Therefore, the problem of building cloud computing models for data integration in multilevel emergency response management systems is quite relevant. Purpose of the article. The purpose of the article is to build a model for integrating data coming to different levels of a 3-level emergency response management model using cloud computing technology. Three main levels of government carried out by public authorities – strategic (state), operational (regional) and tactical (direct executors) are involved in responding to crisis and emergency situations and are the object of research work in terms of data integration using cloud computing. Results. The analysis of a 3-level emergency response management system was carried out for the purpose of transformation, integration and storage in cloud structures of data coming from different sources. A study was made of the process of converting primary data to a state that allows them to be used as input in analytical systems. A hybrid cloud infrastructure for processing and storing data at various levels of management is proposed. It is proposed to use a role-oriented user interface for accessing a catalog of services that are publicly available in a hybrid cloud. The advantages of using cloud technologies in creating a joint information environment and situational awareness for a 3-level emergency response management model are shown. Conclusions. As a result of the research, it can be concluded that the use of cloud technologies in multi-level emergency response management systems can provide an opportunity to solve the problem of processing and storing a large amount of incoming data and provide an advantage in the processes of creating a joint information environment and awareness of the situation. The results of the work can be useful in building a cloud infrastructure for multi-level control systems.

The Paradigm of Cloud Computing From The Lens of Technolegal Challenges

The Paradigm of Cloud Computing From The Lens of Technolegal Challenges

Optimized Tuned Deep Learning Model for Chronic Kidney Disease Classification

In recent times, Internet of Things (IoT) and Cloud Computing (CC) paradigms are commonly employed in different healthcare applications. IoT gadgets generate huge volumes of patient data in healthcare domain, which can be examined on cloud over the available storage and computation resources in mobile gadgets. Chronic Kidney Disease (CKD) is one of the deadliest diseases that has high mortality rate across the globe. The current research work presents a novel IoT and cloud-based CKD diagnosis model called Flower Pollination Algorithm (FPA)-based Deep Neural Network (DNN) model abbreviated as FPA-DNN. The steps involved in the presented FPA-DNN model are data collection, preprocessing, Feature Selection (FS), and classification. Primarily, the IoT gadgets are utilized in the collection of a patient’s health information. The proposed FPA-DNN model deploys Oppositional Crow Search (OCS) algorithm for FS, which selects the optimal subset of features from the preprocessed data. The application of FPA helps in tuning the DNN parameters for better classification performance. The simulation analysis of the proposed FPA-DNN model was performed against the benchmark CKD dataset. The results were examined under different aspects. The simulation outcomes established the superior performance of FPA-DNN technique by achieving the highest sensitivity of 98.80%, specificity of 98.66%, accuracy of 98.75%, F-score of 99%, and kappa of 97.33%.

Cryptography based Techniques of Encryption for Security of Data in Cloud Computing Paradigm

Cryptography based Techniques of Encryption for Security of Data in Cloud Computing Paradigm

Cloud-Based Zero Trust Access Control Policy: An Approach to Support Work-From-Home Driven by COVID-19 Pandemic.

The ubiquitous cloud computing services provide a new paradigm to the work-from-home environment adopted by the enterprise in the unprecedented crisis of the COVID-19 outbreak. However, the change in work culture would also increase the chances of the cybersecurity attack, MAC spoofing attack, and DDoS/DoS attack due to the divergent incoming traffic from the untrusted network for accessing the enterprise’s resources. Networks are usually unable to detect spoofing if the intruder already forges the host’s MAC address. However, the techniques used in the existing researches mistakenly classify the malicious host as the legitimate one. This paper proposes a novel access control policy based on a zero-trust network by explicitly restricting the incoming network traffic to substantiate MAC spoofing attacks in the software-defined network (SDN) paradigm of cloud computing. The multiplicative increase and additive decrease algorithm helps to detect the advanced MAC spoofing attack before penetrating the SDN-based cloud resources. Based on the proposed approach, a dynamic threshold is assigned to the incoming port number. The self-learning feature of the threshold stamping helps to rectify a legitimate user’s traffic before classifying it to the attacker. Finally, the mathematical and experimental results exhibit high accuracy and detection rate than the existing methodologies. The novelty of this approach strengthens the security of the SDN paradigm of cloud resources by redefining conventional access control policy.