Domain Of Cloud Computing Research Articles

OPTIMIZED CLOUD SECURITY ECC-ENHANCED HOMOMORPHIC PAILLIER RE-ENCRYPTION

In the dynamic domain of cloud computing, ensuring data security is of utmost importance. Conventional encryption techniques, while providing a high level of security, introduce substantial computational burdens, rendering them impractical for environments with limited resources. In response to this predicament, our study introduces a novel lightweight encryption framework that amalgamates Elliptic Curve Cryptography (ECC) with Homomorphic Paillier Re-Encryption, thereby reinforcing the security of data within cloud infrastructures.Our methodology exploits the inherent advantages of ECC, notably its ability to maintain stringent security measures with relatively smaller key dimensions, thereby optimizing efficiency without sacrificing the level of security. The integration of ECC with Homomorphic Paillier Encryption facilitates the execution of secure computations on ciphered data, maintaining user privacy while permitting the cloud to perform meaningful data operations. The re-encryption feature of our scheme ensures the secure mobility and modification of data sans decryption, thus augmenting security measures and operational flexibility.The proposed encryption paradigm has been validated through rigorous theoretical scrutiny and empirical implementation, revealing marked enhancements in both computational efficiency and security measures when juxtaposed with established encryption techniques. The empirical evidence suggests that the streamlined nature of our encryption scheme renders it exceptionally compatible with real-world cloud applications, particularly in scenarios where the optimization of resources is imperative.This work contributes to the field of cloud data security by providing a scalable, efficient, and secure encryption solution, paving the way for more secure and practical cloud computing applications.

Mitigating Cloud Computing Virtualization Performance Problems with an Upgraded Logical Convergence Strategy

<p>In the domain of cloud computing and network resource virtualization, existing fusion techniques for containers and virtual machines suffer from high energy consumption, inflexible scheduling requirements, and suboptimal resource utilization. This study critically examined the current methods, accounted for the contemporary requirements, and developed a novel strategy aimed at maximizing resource utilization while minimizing energy consumption. Comprehensive experiments illustrate the superiority of our approach over state-of-the-art fusion strategies such as Kubernetes+Kubevirt and OpenStack+Kubernetes, demonstrating significant reductions in energy consumption, improved resource utilization, and enhanced system performance.</p> <p> </p>

The Advantages of Artificial Intelligence Application in Computer Technology

Presently, humanity has entered the era of big data, where artificial intelligence (AI) technology has found extensive application across various industries and domains. Notably, in the field of computer network technology, its utilization has significantly elevated the technological prowess of computer science, propelling computer systems towards a gradual trajectory of stability and intelligence. Consequently, it has transformed into an indispensable tool in people's daily lives. This paper commences by expounding on the contemporary concept and characteristics of artificial intelligence technology, followed by a synthesis of the advantages of its implementation in the realm of computer network technology based on relevant literature. Furthermore, practical demonstrations are provided to illustrate the efficacy of artificial intelligence in the domain of cloud computing.

Using cloud computing to increase authentication and security in an IoT-enabled cancer predicative model

Cloud computing, machine learning, the Internet of Things, deep learning, and artificial intelligence are used in a variety of areas, including healthcare, transportation, smart cities, and agriculture, to create beneficial results for a variety of challenges in today’s world. This paper focuses on one of these applications in the cloud computing and IoMT domains. Several sensors were implanted in the human body to gather patient-specific information, such as body measurements temp deviations, and many other factors that contribute to changes in blood cells that develop into malignant cells. The major goal of this project is to create a cancer prediction system that uses the IoT to extract information from blood results in order to determine whether they are normal or abnormal. Furthermore, the findings of cancer patients’ blood tests are encrypted and saved in the cloud for quick access by a doctor or healthcare worker through the Internet to handle patient data in a secure manner. The AES technique is used for encryption and decryption in order to offer authentication and security when dealing with cancer patients. Because all of the required cancer treatment information is stored on the cloud, the main focus is on properly handling healthcare data for patients while they are away from home. Using virtual machines, the work completion time is decreased from 450 to 170 min. Simulations are used to test the proposed model’s performance, and the results show that it outperforms alternative options significantly.

Quality of service aware improved coati optimization algorithm for efficient task scheduling in cloud computing environment

The task scheduling has a potential impact on overall system performance and resource utilization in the domain of Cloud Computing. Cloud computing adopts a cloud service provider (CSP) for facilitating access and delivering services to the shared resources. Task scheduling in clouds can be attained some symmetry form which helps in achieving predominant resource optimization that includes energy efficiency and load balancing. This task scheduling process of cloud computing pertains to the problem of Non-deterministic Polynomial (NP) which can be significantly solved using the techniques of metaheuristic optimization for enhancing the job scheduling effectiveness. In this paper, an Improved Coati Optimization Algorithm-based Task Scheduling (ICOATS) is presented for addressing the issues of lengthier scheduling time, high consumptions of cost and maximized load on Virtual Machine (VM) in cloud computing environment. In this proposed ICOATS, a model for distribution and scheduling of tasks is constructed using the factors of VMs, cost and time. It further included a multi-objective fitness function which targets on minimizing makespan, and at the same time maximizing the rate of resource utilization. It established possible plan for every coati with respect to task scheduling process that aids in determining the best solution (optimal assignment of incoming tasks to VMs). It is proposed with the capability of handing the problem of premature convergence by incorporating an exploitation strategy which improving the local search potential with well-balanced trade-off amid exploration and exploitation. The simulation results of this ICOATS approach under its evaluation with the existing metaheuristic task scheduling approaches confirmed better improvement in reducing makespan, and simultaneously enhances the turnaround efficiency, success rate and availability.

Research Areas in Cyber Security and Forensic and Centre of Excellence for Forensic Laboratory

This research work identifies the national and international related work to identify the cutting-edge areas of research problems in the domain of cloud computing , mobile software and multimedia data for its security and cyber forensics. The explanatory research identifies the statement of problems with its aim, objectives, and the scope of the work to be conducted into the proposed cyber forensic laboratory. The hardware and software requirements for the cybercrime digital forensic laboratory are identified to conduct cybercrime research in the domain of cyber security for the recommendation of security. The Centre of excellence for computer cybercrime digital forensics will be useful for reading, writing, research, and development to give practical and feasible solutions for building digital forensic tools in the cutting domain of computing systems such as Cloud Computing, Web Applications, Mobile communication, and Multimedia Systems. The hardware and software requirements for setting up the forensic laboratory are identified and the budget expected to set the centre of excellence for a cybercrime investigation laboratory is approximately Rs.7,79,83,434.39/- which may vary depending on versions and updates to hardware and software’s.

Hybrid grey wolf and improved particle swarm optimization with adaptive intertial weight-based multi-dimensional learning strategy for load balancing in cloud environments

The popularization of Internet applications and rapid advent of information technologies have invited increased number of developers and companies to focus on the area of Cloud Computing (CC). The most significant issues and challenges in the domain of CC include Load Balancing (LB), scheduling of task executions and managing resource allocation. In particular, LB being the process of distributing computing resources and workloads has attracted maximum attention as it is the predominant issue in CC. In this paper, Hybrid Grey Wolf and Improved Particle Swarm Optimization Algorithm with Adaptive Intertial Weight-based multi-dimensional Learning Strategy (HGWIPSOA)-based LB scheme is proposed for enhancing precision and rapidness in task scheduling and assignment of resources to Virtual Machines (VMs) in cloud environments. In the proposed scheme, initially, Grey Wolf Optimization Algorithm (GWOA) is incorporated into Particle Swarm Optimisation (PSO) for considering the highest fitness particle as alpha wolf search agent, such that the objective of allocating tasks to VMs is attained effectively and efficiently. It then integrates chaos, Adaptive Inertial Weight (AIW) and Dimensional Learning (DL) into PSO specifically to prevent premature convergence and achieve better convergence speed and global search ability depending on the best experience determined by particles for effective LB. The simulation experiments of the proposed HGWIPSOA mechanism confirm better results by offering 21.32 % improved throughput, 19.84 % reduced makespan, 24.98 % minimized degree of imbalance, 18.74 % reduced latency and 27.92 % reduced execution time independent of the number of tasks in the cloud environment on par with benchmarked LB schemes.

Transportation of Service Enhancement Based on Virtualization Cloud Desktop

Cloud desktop represents an outstanding product in the domain of cloud computing, which refers to the desktop cloud, desktop virtualization and virtual desktop. Cloud desktop explores the virtualization technology to concentrate computing resources, which delivers traditional computer desktops (operating system interfaces) or applications deployed in the pooled computing resources to polymorphic terminals through the Internet. As a distinctive product of cloud computing, cloud desktop has been a hot topic since its inception. Today, the virtualized resource pool of cloud computing achieves the elastic and dynamic expansion of resources, which brings the desktop system from an independent personal computer to a centralized physical server. Consequently, the great improvement in basic network conditions makes it possible to transmit high-quality desktops over the network. There are two key factors for cloud desktops, one of which is the virtualization technology on the server side and the other one, which is the transmission protocol of cloud desktops. The cloud desktop transmission protocol mainly completes the transmission of graphics, images and audio from the server to the user terminal. The transmission of input information from the user terminal, called DaaS (Desktop-as-a-Service), includes the input information of peripherals such as a mouse, keyboard, printer and so on. The efficiency of the transmission protocol determines the basic delivery capability of the cloud desktop, while the bearer protocol and graphics and image processing methods in the transmission protocol determine the interactive experience of the cloud desktop. Different protocols have their characteristics and applicable space. This paper spies on application and transport layer communication protocols to meet DaaS communication requirements. This paper describes the internal mechanism of various transport protocols applicable to a cloud desktop from the principle level and points out the pros and cons and the current application environment. It can be seen that these methods solve the transmission efficiency of burst traffic, improve user experience and reduce bandwidth consumption, which are the development direction of transmission protocols.

Representative Service Providers and their Selection in Cloud Computing Domain

Representative Service Providers and their Selection in Cloud Computing Domain

Digital Twins for Manufacturing Process and System: A State of the Art Review

As the industry is stepping into industry, 4.0 Digital Twin Technology has been seen as the path for the implementation of virtual manufacturing for the betterment of industry to produce more efficient and reliable products with minimal modification requirements further as per need of the hour for making the product more suitable in the market. Being a rapidly growing technology there is much research work going on it at the current stage in a different form of the industry by utilizing multiple domains of IoT, data acquisition, cloud computing, model and empirical-based design process, etc. This review article will enlighten on the research work done particularly on Digital Twin Technology previously and identify gaps in the research by characterizing the technology on state of concept, terminology, and process associated with it as well as giving a brief idea about how Digital Twin Technology is beneficial to manufacturing.

Mitigation of DDoS Attack in Cloud Computing Domain by Integrating the DCLB Algorithm with Fuzzy Logic

Cloud computing would be an easy method to obtain services, resources and applications from any location on the internet. In the future of data generation, it is an unavoidable conclusion. Despite its many attractive properties, the cloud is vulnerable to a variety of attacks. One such well-known attack that emphasizes the availability of amenities is the Distributed Denial of Service (DDoS). A DDoS assault overwhelms the server with massive quantities of regular or intermittent traffic. It compromises with the cloud servers’ services and makes it harder to reply to legitimate users of the cloud. A monitoring system with correct resource scaling approach should be created to regulate and monitor the DDoS assault. The network is overwhelmed with excessive traffic of significant resource usage requests during the attack, resulting in the denial of needed services to genuine users. In this research, a unique way to the analyze resources used by the cloud users, lowering of the resources consumed is done when the network is overburdened with excessive traffic, and the dynamic cloud load balancing algorithm DCLB (Dynamic Cloud Load Balancing) is used to balance the overhead towards the server. The core premise is to monitor traffic using the fuzzy logic approach, which employs different traffic parameters in conjunction with various built in measured to recognize the DDoS attack traffic in the network. Finally, the proposed method shows a 93% of average detection rate when compared to the existing model. This method is a unique attempt to comprehend the importance of DDoS mitigation techniques as well as good resource management during an attack and analysis of the.

Uncertainty-aware Decisions in Cloud Computing

The rapid growth of the cloud industry has increased challenges in the proper governance of the cloud infrastructure. Many intelligent systems have been developing, considering uncertainties in the cloud. Intelligent approaches with the consideration of uncertainties bring optimal management with higher profitability. Uncertainties of different levels and different types exist in various domains of cloud computing. This survey aims to discuss all types of uncertainties and their effect on different components of cloud computing. The article first presents the concept of uncertainty and its quantification. A vast number of uncertain events influence the cloud, as it is connected with the entire world through the internet. Five major uncertain parameters are identified, which are directly affected by numerous uncertain events and affect the performance of the cloud. Notable events affecting major uncertain parameters are also described. Besides, we present notable uncertainty-aware research works in cloud computing. A hype curve on uncertainty-aware approaches in the cloud is also presented to visualize current conditions and future possibilities. We expect the inauguration of numerous uncertainty-aware intelligent systems in cloud management over time. This article may provide a deeper understanding of managing cloud resources with uncertainties efficiently to future cloud researchers.

Software-defined networks for resource allocation in cloud computing: A survey

Cloud computing has a shared set of resources, including physical servers, networks, storage, and user applications. Resource allocation is a critical issue for cloud computing, especially in Infrastructure-as-a-Service (IaaS). The decision-making process in the cloud computing network is non-trivial as it is handled by switches and routers. Moreover, the network concept drifts resulting from changing user demands are among the problems affecting cloud computing. The cloud data center needs agile and elastic network control functions with control of computing resources to ensure proper virtual machine (VM) operations, traffic performance, and energy conservation. Software-Defined Network (SDN) proffers new opportunities to blueprint resource management to handle cloud services allocation while dynamically updating traffic requirements of running VMs. The inclusion of an SDN for managing the infrastructure in a cloud data center better empowers cloud computing, making it easier to allocate resources. In this survey, we discuss and survey resource allocation in cloud computing based on SDN. It is noted that various related studies did not contain all the required requirements. This study is intended to enhance resource allocation mechanisms that involve both cloud computing and SDN domains. Consequently, we analyze resource allocation mechanisms utilized by various researchers; we categorize and evaluate them based on the measured parameters and the problems presented. This survey also contributes to a better understanding of the core of current research that will allow researchers to obtain further information about the possible cloud computing strategies relevant to IaaS resource allocation.

A Task Scheduling Approach for Cloud Environments Employing Evolutionary Algorithms

One of the key challenges in the domain of cloud computing is task scheduling and estimation of cloud workloads for time critical applications pertaining to constrained cloud resources. While effective task scheduling is necessary for balancing the load, workload forecasting is necessary to plan in advance the requirements of cloud platforms based on previous data so as to effectively utilize cloud resources. Often it is challenging to gather sufficient information about the tasks and hence allocating the tasks to virtual machines (VMs) in the most optimal way is non-trivial. In this paper, a hybrid task scheduling approach is proposed based on evolutionary algorithms. The first approach is the amalgamation of bat and particle swarm optimization (PSO) techniques. The scheduling approach also combines the processing time preemption (PTP) approach to schedule the source intensive tasks which allows to reduce the response time of the proposed system. The second approach is a machine learning based approach employing gradient descent with momentum (GDM). The evaluation of the proposed system has been done based on the response time and mean square error of the system.

Big Data Analytics and Artificial intelligence in business and Marketing: Cloud security and encryption influencing business

Security is one of the most important aspects of any business. However, it is often the most neglected aspect as most businesses focus their resources on development and marketing. Security is the paramount concern of the user in the domain of cloud computing. Cloud based services offer a certain level of security from various attacks but a lot of the responsibility lies in the hands of the user. Furthermore, the current protocols and technologies employed in cloud services have a lot of potential for exploits that are ignored due to lower priority. In this paper the aspects of cloud security that are most important from a business owners’ standpoint are explored. Current security techniques have been discussed and state of art optimized framework has been proposed along with discussion about the complexity and usability of said framework.

An efficient secure data deduplication method using radix trie with bloom filter (SDD-RT-BF) in cloud environment

Recent advancements in the domain of cloud computing (CC) and big data technologies leads to an exponential increase in cloud data, huge replica data utilized the available memory space and maximum computation brought a major issue to the restricted cloud storage space. This paper develops an effective radix trie (RT) with Bloom Filter (BF) based secure data deduplication model, abbreviated as SDD-RT-BD. The proposed SDD-RT-BF model involves three major stages namely, authorized deduplication, proof of ownership and role key update. Initially, a convergent encryption approach is applied for preventing the leakage of data and employed role re-encryption process for attaining authorized deduplication resourcefully. Specifically, management centre handles the authorized request, and establish a RT structure to map the relationship among roles and keys. Besides, BF is applied for the implementation of data updating and enhance the retrieval of ownership verifying efficiently. The inclusion of RT along with BF for secure data deduplication shows the novelty of the paper. A detailed simulation experiments takes place for demonstrating the security and effectiveness of the presented model. The experimental outcome pointed out that the SDD-RT-BF model possesses many beneficial features namely Client-side deduplication, Tag consistency preservation, Update of outsourced data and Fault tolerance. The experimental results denoted that under the file size of 8 MB, the SDD-RT-BF model offers maximum deduplication rate of 25.40% whereas the SS, SSIMI and SDM models attains minimum deduplication rate of 24.60%, 23.60% and 22.30% respectively.

Privacy and data protection in mobile cloud computing: A systematic mapping study.

As a result of a shift in the world of technology, the combination of ubiquitous mobile networks and cloud computing produced the mobile cloud computing (MCC) domain. As a consequence of a major concern of cloud users, privacy and data protection are getting substantial attention in the field. Currently, a considerable number of papers have been published on MCC with a growing interest in privacy and data protection. Along with this advance in MCC, however, no specific investigation highlights the results of the existing studies in privacy and data protection. In addition, there are no particular exploration highlights trends and open issues in the domain. Accordingly, the objective of this paper is to highlight the results of existing primary studies published in privacy and data protection in MCC to identify current trends and open issues. In this investigation, a systematic mapping study was conducted with a set of six research questions. A total of 1711 studies published from 2009 to 2019 were obtained. Following a filtering process, a collection of 74 primary studies were selected. As a result, the present data privacy threats, attacks, and solutions were identified. Also, the ongoing trends of data privacy exercise were observed. Moreover, the most utilized measures, research type, and contribution type facets were emphasized. Additionally, the current open research issues in privacy and data protection in MCC were highlighted. Furthermore, the results demonstrate the current state-of-the-art of privacy and data protection in MCC, and the conclusion will help to identify research trends and open issues in MCC for researchers and offer useful information in MCC for practitioners.

A framework for verifying deadlock and nondeterminism in UML activity diagrams based on CSP

• A framework to check deadlock and nondeterminism based on formal methods. • Verification capabilities for activity diagrams at the modeling level. • Formal aspects are completely hidden from the user. • No need to understand complex mathematical concepts or rigorous notations. • Full traceability from the formal tools to the UML activity diagram models. Deadlock and nondeterminism may become increasingly hard to detect in concurrent and distributed systems. UML activity diagrams are flowcharts that model sequential and concurrent behavior. Although the UML community widely adopts such diagrams, there is no standard approach to verify the presence of deadlock and nondeterministic behavior in activity diagrams. Nondeterminism is usually neglected in the literature even though it may be considered a very relevant property. This work proposes a framework for the automatic verification of deadlock and nondeterminism in UML activity diagrams. It introduces a compositional CSP semantics for activity diagrams that is used to automatically generate CSP specifications from UML models. These specifications are the input for the automatic verification of deadlock and nondeterminism using the FDR refinement checker. We propose a plugin for the Astah modeling environment that mechanizes the translation process, and that calls FDR in the background to perform the verification of properties. The tool keeps the traceability between a diagram and its CSP specification. It parses the FDR results to highlight the diagram paths that lead to a deadlock or a nondeterministic behavior. This framework adds verification capabilities to the UML modeling tool and keeps the formal semantics transparent to the users. Therefore, the user does not need to understand or manipulate formal notations during modeling. We present the results of a case study that applies the proposed framework for the verification of models in the domain of cloud computing. We discuss future applications due to the potential of our approach.

Power consumption prediction in cloud data center using machine learning

The flourishing development of the cloud computing paradigm provides several services in the industrial business world. Power consumption by cloud data centers is one of the crucial issues for service providers in the domain of cloud computing. Pursuant to the rapid technology enhancements in cloud environments and data centers augmentations, power utilization in data centers is expected to grow unabated. A diverse set of numerous connected devices, engaged with the ubiquitous cloud, results in unprecedented power utilization by the data centers, accompanied by increased carbon footprints. Nearly a million physical machines (PM) are running all over the data centers, along with (5 – 6) million virtual machines (VM). In the next five years, the power needs of this domain are expected to spiral up to 5% of global power production. The virtual machine power consumption reduction impacts the diminishing of the PM’s power, however further changing in power consumption of data center year by year, to aid the cloud vendors using prediction methods. The sudden fluctuation in power utilization will cause power outage in the cloud data centers. This paper aims to forecast the VM power consumption with the help of regressive predictive analysis, one of the Machine Learning (ML) techniques. The potency of this approach to make better predictions of future value, using Multi-layer Perceptron (MLP) regressor which provides 91% of accuracy during the prediction process.

An approach toward design and development of an energy-aware VM selection policy with improved SLA violation in the domain of green cloud computing

With the rapid demand for service-oriented computing in association with the growth of cloud computing technologies, large-scale virtualized data centers have been established throughout the globe. These huge data centers consume power at a large scale that results in a high operational cost. The massive carbon footprint from the energy generators is another great issue to deal global warming. It is essential to lower the rate of carbon emission and energy consumption as much as possible. The live-migration-enabled dynamic virtual machine consolidation results in high energy saving. But it also incurs the violation of service level agreement (SLA). Excessive migration may lead to performance degradation and SLA violation. The process of VM selection for migration plays a vital role in the domain of energy-aware cloud computing. Using VM selection policies, VMs are selected for migration. A new power-aware VM selection policy has been proposed in this research that helps in VM selection for migration. The proposed power-aware VM selection policy has been further evaluated using trace-based simulation environment.