Cloud Computing Infrastructure Research Articles

Navigating The Cloud: Enhancing Human-Robot Collaboration In Industry 4.0 Through Digital Twins And Cloud-Based Systems

Industry 4.0 Is Geared Towards Establishing Sophisticated Industrial Ecosystems That Necessitate Secure And Efficient Collaboration Between Humans And Robots, Termed Human-Robot Collaboration (HRC). Within This Framework, HRC Leverages Cutting-Edge Collision Avoidance Technology Capable Of Detecting Objects And Foreseeing Possible Collisions, Thereby Calculating Alternative Pathways To Avert Any Direct Contact. Furthermore, The Concept Of Digital Twins Emerges As An Innovative Solution, Offering A Platform To Simultaneously Evaluate The Consequences Of Various Control Strategies Within A Digitally Simulated Environment. While Cloud Computing Infrastructure Can Furnish The Requisite Computational Prowess, It May Simultaneously Introduce Challenges Such As Increased Latency And Variability In Communication, Known As Jitter, Which Could Potentially Hinder System Performance. Cloud Technologies Are Renowned For Their Inventive Approaches To Ease The Workload For Software Developers And System Operators, Raising Questions About Their Integration With Digital Twin Methodologies And The Resilience Of Robotic Systems Against The Potential Delays Incurred From Cloud-Based Services. This Research Endeavors To Navigate These Complexities By Integrating A Blend Of Both Public And Private Cloud Services, Which Are Distinguished By Their Unique Parallel Processing Capabilities. This Study's Contributions Are Threefold. Firstly, It Introduces A Novel Method Designed To Gauge The Efficacy Of Diverse Strategies By Focusing On Their Associated Latency Impacts. Secondly, It Delineates A Tangible Application Of HRC, Illuminating Its Practical Benefits And Real-World Applicability. Lastly, It Defines A Crucial Performance Metric Intended To Assess The Efficiency Of These Systems Thoroughly. By Delving Into These Aspects, The Research Aims To Conduct A Comprehensive Analysis Of The Strengths And Weaknesses Inherent In Various Technological Methodologies And Their Consequential Effects On The Performance Of HRC Systems Within The Ambit Of Industry 4.0. Through This Detailed Examination, The Study Seeks To Provide Valuable Insights Into Optimizing Human-Robot Collaboration In Industrial Settings, Ensuring Both Security And Efficiency Are Upheld

Strengthening Aviation Cybersecurity with Security Operations Centres

Even though cybersecurity is a top priority for the aviation industry, research indicates that there are still many challenges to address. Modern aviation systems encompass cloud computing, OT, IoT, mobile devices, and traditional IT infrastructure. The network complexity has expanded the attack surface, leading to an increase in security incidents. Due to this complexity, detecting security incidents on time is challenging. Research indicates that it may take up to 196 days to detect an incident and another 56 days to address it, highlighting the urgency of improving security response. In this regard, establishing Security Operations Centres (SOCs) in the aviation sector must be addressed. SOCs can be instrumental in reducing the time it takes to detect and respond to security incidents. They provide visibility into threats, aid investigations, and enhance forensic efforts, enabling proactive threat mitigation. Research has been carried out on SOC implementations for specific domains like IoT, mobile devices, and higher education, neglecting aviation systems. Aviation systems such as Air Traffic Management (ATM) face unique security vulnerabilities, including signal modification, jamming, flooding, data and command injection, GPS spoofing, and blocking attacks, primarily due to their reliance on wireless technology. Most of these wireless technologies do not use encryption or authentication because they were designed to maximise performance. Insufficient funding also negatively affects ATM systems, resulting in the wide use of legacy ATM systems and a shortage of skilled personnel. ATM systems are considered critical infrastructure frequently targeted by well-resourced threat actors, including terrorists and nation-state actors, necessitating higher protection levels. This paper motivates the development of a customised SOC implementation framework for ATM systems to enhance aviation security by increasing visibility into threats and facilitating timely remediation.

Agnostic Energy Consumption Models for Heterogeneous GPUs in Cloud Computing

The adoption of cloud computing has grown significantly among individuals and in organizations. According to this growth, Cloud Service Providers have continuously expanded and updated cloud-computing infrastructures, which have become more heterogeneous. Managing these heterogeneous resources in cloud infrastructures while ensuring Quality of Service (QoS) and minimizing energy consumption is a prominent challenge. Therefore, unifying energy consumption models to deal with heterogeneous cloud environments is essential in order to efficiently manage these resources. This paper deeply analyzes factors affecting power consumption and employs these factors to develop power models. Because of the strong correlation between power consumption and energy consumption, the influencing factors on power consumption, with the addition of other factors, are considered when developing energy consumption models to enhance the treatment in heterogeneous infrastructures in cloud computing. These models have been developed for two Virtual Machines (VMs) containing heterogeneous Graphics Processing Units (GPUs) architectures with different features and capabilities. Experiments evaluate the models through a cloud testbed between the actual and predicted values produced by the models. Deep Neural Network (DNN) power models are validated with shallow neural networks using performance counters as inputs. Then, the results are significantly enhanced by 8% when using hybrid inputs (performance counters, GPU and memory utilization). Moreover, a DNN energy-agnostic model to abstract the complexity of heterogeneous GPU architectures is presented for the two VMs. A comparison between the standard and agnostic energy models containing common inputs is conducted in each VM. Agnostic energy models with common inputs for both VMs show a slight enhancement in accuracy with input reduction.

Cloud media video encoding: review and challenges

AbstractIn recent years, Internet traffic patterns have been changing. Most of the traffic demand by end users is multimedia, in particular, video streaming accounts for over 53%. This demand has led to improved network infrastructures and computing architectures to meet the challenges of delivering these multimedia services while maintaining an adequate quality of experience. Focusing on the preparation and adequacy of multimedia content for broadcasting, Cloud and Edge Computing infrastructures have been and will be crucial to offer high and ultra-high definition multimedia content in live, real-time, or video-on-demand scenarios. For these reasons, this review paper presents a detailed study of research papers related to encoding and transcoding techniques in cloud computing environments. It begins by discussing the evolution of streaming and the importance of the encoding process, with a focus on the latest streaming methods and codecs. Then, it examines the role of cloud systems in multimedia environments and provides details on the cloud infrastructure for media scenarios. After doing a systematic literature review, we have been able to find 49 valid papers that meet the requirements specified in the research questions. Each paper has been analyzed and classified according to several criteria, besides to inspect their relevance. To conclude this review, we have identified and elaborated on several challenges and open research issues associated with the development of video codecs optimized for diverse factors within both cloud and edge architectures. Additionally, we have discussed emerging challenges in designing new cloud/edge architectures aimed at more efficient delivery of media traffic. This involves investigating ways to improve the overall performance, reliability, and resource utilization of architectures that support the transmission of multimedia content over both cloud and edge computing environments ensuring a good quality of experience for the final user.

Efficient Resource Utilization in IoT and Cloud Computing

With the proliferation of IoT devices, there has been exponential growth in data generation, placing substantial demands on both cloud computing (CC) and internet infrastructure. CC, renowned for its scalability and virtual resource provisioning, is of paramount importance in e-commerce applications. However, the dynamic nature of IoT and cloud services introduces unique challenges, notably in the establishment of service-level agreements (SLAs) and the continuous monitoring of compliance. This paper presents a versatile framework for the adaptation of e-commerce applications to IoT and CC environments. It introduces a comprehensive set of metrics designed to support SLAs by enabling periodic resource assessments, ensuring alignment with service-level objectives (SLOs). This policy-driven approach seeks to automate resource management in the era of CC, thereby reducing the dependency on extensive human intervention in e-commerce applications. This paper culminates with a case study that demonstrates the practical utilization of metrics and policies in the management of cloud resources. Furthermore, it provides valuable insights into the resource requisites for deploying e-commerce applications within the realms of the IoT and CC. This holistic approach holds the potential to streamline the monitoring and administration of CC services, ultimately enhancing their efficiency and reliability.

Aerial computing: Enhancing mobile cloud computing with unmanned aerial vehicles as data bridges—A Markov chain based dependability quantification

Aerial Computing, utilizing unmanned aerial vehicles (UAVs), has emerged as a promising solution to enhance mobile cloud computing (MCC) infrastructure for the Internet of Things (IoT). The continuous generation of vast amounts of data by IoT devices requires efficient processing and monitoring for timely decision-making. However, wireless connections between IoT devices and remote servers can be unreliable, resulting in data loss. UAVs, with their increasing processing power and autonomy, can act as bridges between IoT devices and remote servers such as edge or cloud computing. In that context, this paper proposes a continuous time Markov chain (CTMC) models for an aerial computing system to evaluate system dependability metrics including availability and reliability. Sensitivity analysis is conducted to provide extended CTMC models with improved system availability. The proposed advanced model reduces downtime by 62 h compared to the baseline model, showcasing the potential of UAVs in enhancing the availability and reliability of MCC infrastructures. The use of UAVs and MCC in aerial computing is believed to be a win–win solution for cost-effective and energy-saving communication and computation services in various environments.

IoT system architecture for monitoring and analyzing public transport data

The crucial role of public transport in the economy and modern city development is widely acknowledged. However, contemporary public transport systems are confronted with several challenges that require resolution. These challenges include real-time monitoring, data management, passengers flow optimization, and road accident prediction. The Internet of Things (IoT) presents a promising avenue for the development of modern public transport management systems. By leveraging a diverse range of technologies, such as sensors, edge devices, cloud computing, and various communication infrastructures, IoT systems can enable the creation of robust and automated public transport infrastructure. In this research, we propose an architecture for a public transport monitoring and management system. The proposed system can effectively collect, store, and monitor public transport data, thereby facilitating efficient management and timely response to risks that arise in the transport system. Implementation of such a system would enable public transport administrators to monitor and swiftly respond to potential problems such as overloaded passenger flows, emergencies, and technical issues with transport systems.

A Systematic Review of Disaster Management Systems: Approaches, Challenges, and Future Directions

Disaster management is a critical area that requires efficient methods and techniques to address various challenges. This comprehensive assessment offers an in-depth overview of disaster management systems, methods, obstacles, and potential future paths. Specifically, it focuses on flood control, a significant and recurrent category of natural disasters. The analysis begins by exploring various types of natural catastrophes, including earthquakes, wildfires, and floods. It then delves into the different domains that collectively contribute to effective flood management. These domains encompass cutting-edge technologies such as big data analysis and cloud computing, providing scalable and reliable infrastructure for data storage, processing, and analysis. The study investigates the potential of the Internet of Things and sensor networks to gather real-time data from flood-prone areas, enhancing situational awareness and enabling prompt actions. Model-driven engineering is examined for its utility in developing and modeling flood scenarios, aiding in preparation and response planning. This study includes the Google Earth engine (GEE) and examines previous studies involving GEE. Moreover, we discuss remote sensing; remote sensing is undoubtedly a valuable tool for disaster management, and offers geographical data in various situations. We explore the application of Geographical Information System (GIS) and Spatial Data Management for visualizing and analyzing spatial data and facilitating informed decision-making and resource allocation during floods. In the final section, the focus shifts to the utilization of machine learning and data analytics in flood management. These methodologies offer predictive models and data-driven insights, enhancing early warning systems, risk assessment, and mitigation strategies. Through this in-depth analysis, the significance of incorporating these spheres into flood control procedures is highlighted, with the aim of improving disaster management techniques and enhancing resilience in flood-prone regions. The paper addresses existing challenges and provides future research directions, ultimately striving for a clearer and more coherent representation of disaster management techniques.

Community Cloud Computing Infrastructure toSupport Equitable Water Research and Education.

Community Cloud Computing Infrastructure toSupport Equitable Water Research and Education.

VMP-A3C: Virtual machines placement in cloud computing based on asynchronous advantage actor-critic algorithm

Virtualization technology represented through Virtual Machines (VMs) is recognized as a key infrastructure in cloud computing. This technology is developing rapidly and cloud data centers face challenges such as Virtual Machine Placement (VMP) for energy efficiency. VMP is defined as the efficient allocation of VMs to Host Machines (HMs) to achieve various objectives such as reducing energy consumption, load balancing and avoid Service Level Agreement Violations (SLAV). In this paper, VMP is addressed using a Deep Reinforcement Learning (DRL) based strategy to determine the best mapping between VMs and HMs. We present VMP-A3C, an effective strategy to solve VMP using Asynchronous Advantage Actor-Critic (A3C) algorithm as a new DRL approach. VMP-A3C aims at load balancing in HMs without SLAV, where energy consumption is reduced as much as possible. VMP-A3C learns to dynamically consolidate VMs using migration techniques to a minimum number of HMs. We believe that there is scope for improvements in shutting down little-workload HMs through VMs migration. The effectiveness of the proposed algorithm has been evaluated from various aspects such as the deployment rate, energy consumption, SLAV, the number of shutdown HMs and the number of migrated VMs. The main difference in terms of energy consumption and the number of required HMs between VMP-A3C and the best existing state-of-the-art method is 2.54% and 7.14%, respectively.

Cloud Computing with Artificial Intelligence Techniques for Effective Disease Detection

With the current rapid advancement of cloud computing (CC) technology, which enabled the connectivity of many intelligent objects and detectors and created smooth data interchange between systems, there is now a strict need for platforms for data processing, the Internet of Things (IoT), and data management. The field of medicine in CC is receiving a lot of attention from the scientific world, as well as the private and governmental sectors. Thousands of individuals now have a digital system due to these apps where they may regularly obtain helpful medical advice for leading a healthy life. The use of artificial intelligence (AI) in the medical field has several advantages, including the ability to automate processes and analyze large patient databases to offer superior medicine more quickly and effectively. IoT-enabled smart health tools provide both internet solutions and a variety of features. CC infrastructure improves these healthcare solutions by enabling safe storage and accessibility. We suggest a novel Cloud computing and artificial intelligence (CC-AI) premised smart medical solution for surveillance and detecting major illnesses to provide superior solutions to the users. For disease detection, we suggested AI-based whale optimization (WO) and fuzzy neural network (FNN) (WO-FNN). Patients' IoT wearable sensor data is gathered for detection. The accuracy, sensitivity, specificity, and computation time are evaluated and compared with existing techniques.

Cloud Based VPN Using IP Tunneling for Remote Site Interface

Abstract: A strong IP technology that builds a secure and encrypted connection tunnel over the less secure internet is required by the majority of enterprise organizations. Joint location and link connectivity issues must be resolved for distant users and branch offices to have secure access to corporate applications and other resources. This ensures security while data passes over encrypted tunnels. Despite the development of IP-based VPN, JLP and LCP for VPN customer edge devices and provider edge nodes have not yet been completely investigated. The purpose of this work is to apply cutting-edge viewpoints to VPN-IP design that may be used in IP multi-protocol label switching (MPLSVPN) infrastructure for cloud computing. The system architecture includes mathematical formulas. End-to-end delay, throughput, and resource consumption behavior for IP tunneling are seen to behave moderately for low scale workloads. While showcasing the benefits of an MPLS-based IP-based VPN architecture, the difficulties of VPN-IP tunneling are explored. This study suggests cloud-based MPLS-VPN as a workable substitute for traditional VPN-IP tunneling in order to get the best performance and service delivery

Exploring High-Performance Architecture for Data Center Networks

As a critical infrastructure of cloud computing, data center networks (DCNs) directly determine the service performance of data centers, which provide computing services for various applications such as big data processing and artificial intelligence. However, current architectures of data center networks suffer from a long routing path and a low fault tolerance between source and destination servers, which is hard to satisfy the requirements of high-performance data center networks. Based on dual-port servers and Clos network structure, this paper proposed a novel architecture to construct high-performance data center networks. Logically, the proposed architecture is constructed by inserting a dual-port server into each pair of adjacent switches in the fabric of switches, where switches are connected in the form of a ring Clos structure. We describe the structural properties of in terms of network scale, bisection bandwidth, and network diameter. architecture inherits characteristics of its embedded Clos network, which can accommodate a large number of servers with a small average path length. The proposed architecture embraces a high fault tolerance, which adapts to the construction of various data center networks. For example, the average path length between servers is 3.44, and the standardized bisection bandwidth is 0.8 in (32, 5). The result of numerical experiments shows that enjoys a small average path length and a high network fault tolerance, which is essential in the construction of high-performance data center networks.

MRTCM: A comprehensive dataset for probabilistic risk assessment of metals and metalloids in traditional Chinese medicine

Traditional Chinese medicine (TCM) is still considered a global complementary or alternative medical system, but exogenous hazardous contaminants remain in TCM even after decocting. Besides, it is time-consuming to conduct a risk assessment of trace elements in TCMs with a non-automatic approach due to the wide variety of TCMs. Here, we present MRTCM, a cloud-computing infrastructure for automating the probabilistic risk assessment of metals and metalloids in TCM. MRTCM includes a consumption database and a pollutant database involving forty million rows of consumption data and fourteen types of TCM potentially toxic elements concentrations. The algorithm of probabilistic risk assessment was also packaged in MRTCM to assess the risks of eight elements with Monte Carlo simulation. The results demonstrated that 96.64% and 99.46% had no non-carcinogenic risk (hazard indices (HI) were < 1.0) for animal and herbal medicines consumers, respectively. After twenty years of exposure, less than 1% of the total carcinogenic risk (CRt) was > 10-4 for TCM consumers, indicating that they are at potential risk for carcinogenicity. Sensitivity analysis revealed that annual consumption and concentration were the main variables affecting the assessment results. Ultimately, a priority management list of TCMs was also generated, indicating that more attention should be paid to the non-carcinogenic risks of As, Mn, and Hg and the carcinogenic risks of As and Cr in Pheretima and Cr in Arcae Conch. In general, MRTCM could significantly enhance the efficiency of risk assessment in TCM and provide reasonable guidance for policymakers to optimize risk management.

A Survey of Virtualization Technologies: Towards a New Taxonomic Proposal

At present, there is a proliferation of virtualization technologies (VTs), which are part of the basic and underlying infrastructure of popular cloud computing. Those interested in VTs are faced with a non-unified volume of information and various approaches to modes of operation, classification structures, and the performance implications of these technologies. This makes it difficult to decide which type of VT is appropriate for a particular context. Therefore, this paper reviews the state of the art on VT taxonomic models. Methodologically, a literature review is carried out to identify VT classification models, recognizing their features and weaknesses. With this in mind, a new taxonomy of virtualization technologies is proposed, which responds to the weaknesses identified in the analyzed schemes. The new VT taxonomy combines the Abstraction Level and Virtual Machine Type approaches, providing the reader with a means to visualize VTs. In doing so, the reader can locate the level of abstraction at which each VT is developed, in addition to the type of machine projected, whether it is a complete system or an execution environment for processes. The proposed taxonomy can be used in the academic environment to facilitate teaching processes or in the business environment to facilitate decision-making when implementing VTs.

Cloud computing: A study of mechanism and cloud cryptography

Cloud Computing is the on-demand delivery of computing services, databases, storage, applications, networking, and many more through the internet. Cloud Computing technology refers to pay-as-you-go pricing. The cloud delivers a hosting environment that is immediate, flexible, scalable, secure, and available which saves enterprise money, time, and resources. As we are going to use the internet services, security is the major factor to be taken into consideration. Cloud cryptography is a way through which we can secure our data on the internet. Cryptography adds several layers of protection to avoid breaching, hacking, or getting affected by malware. So in this survey, we are going to study the mechanism of cloud computing and how the activities on the cloud can be secured over the internet.In comparison to private clouds, which can scale effortlessly and on-demand, the public cloud has a massive infrastructure. IaaS is a method of delivering cloud-computing infrastructures such as servers, storage, networks, and operating systems to customers. Customers can get on-demand access to materials here. Cloud cryptography is a type of encryption that employs a number of algorithms to turn plain text into cipher text. Because symmetric cryptography is substantially faster than asymmetric cryptography, it is ideally suited for bulk data encryption.Using a single key, this technique enables data authentication and permission. The strength of an asymmetric key in assuring the security of information supplied during transmission is significantly greater.

CloudIDS: Cloud Intrusion Detection Model Inspired by Dendritic Cell Mechanism

Cloud Computing Security is a new era of computer technology and opens a new research area and creates a lot of opportunity of exploration. One of the new implementation in Cloud is Intrusion Detection System (IDS).There are problems with existing IDS approach in Cloud environment. Implementing traditional IDS need a lot of self-maintenance and did not scale with the customer security requirements. In addition, maintenance of traditional IDS in Cloud Computing system requires expertise and consumes more time where not each Cloud user has. A decentralized traditional IDS approach where being deployed in current Cloud Computing infrastructure will make the IDS management become complicated. Each user's IDS will not be the same in term of type and configurations and each user may have outdated signatures. Inter VM's communication also become a big concern when we implementing Cloud Computing system where communication between Clouds are not monitored and controlled by the traditional IDS. A specific IDS model for Cloud computing is required to solve these problems. In this paper, we develop a prototype of Cloud IDS inspired by Dendritic Cell mechanism. Experiment result proved that Cloud IDS was able to detect any attempt to attack the Cloud environment. The experiments show that the Cloud IDS model based on Dendritic Cell algorithm able to identify and detect novel threat that targeting Cloud environment.

A Survey of Biological Data in a Big Data Perspective.

The amount of available data is continuously growing. This phenomenon promotes a new concept, named big data. The highlight technologies related to big data are cloud computing (infrastructure) and Not Only SQL (NoSQL; data storage). In addition, for data analysis, machine learning algorithms such as decision trees, support vector machines, artificial neural networks, and clustering techniques present promising results. In a biological context, big data has many applications due to the large number of biological databases available. Some limitations of biological big data are related to the inherent features of these data, such as high degrees of complexity and heterogeneity, since biological systems provide information from an atomic level to interactions between organisms or their environment. Such characteristics make most bioinformatic-based applications difficult to build, configure, and maintain. Although the rise of big data is relatively recent, it has contributed to a better understanding of the underlying mechanisms of life. The main goal of this article is to provide a concise and reliable survey of the application of big data-related technologies in biology. As such, some fundamental concepts of information technology, including storage resources, analysis, and data sharing, are described along with their relation to biological data.

Genetically-modified Multi-objective Particle Swarm Optimization approach for high-performance computing workflow scheduling

Nowadays, scientific research, industry, and many other fields are greedy regarding computing resources. Therefore, Cloud Computing infrastructures are now attracting pervasive interest thanks to their excellent hallmarks such as scalability, high performance, reliability, and the pay-per-use strategy. The execution of these high-performant applications on such kind of computing environments in respect of optimizing many conflicting objectives brings us to a challenging issue commonly known as the multi-objective workflows scheduling on large scale distributed systems. Having this in mind, we outline in the present paper our proposed approach called Genetically-modified Multi-objective Particle Swarm Optimization (GMPSO) for scheduling application workflows on hybrid Clouds in the context of high-performance computing in an attempt to optimize Makespan and Cost. The GMPSO consists of incorporating genetic operations into the Multi-objective Particle Swarm Optimization to enhance the resulting solutions. To achieve this, we have designed a novel solution encoding that represents the task ordering, the task mapping and the resource provisioning processes of the workflow scheduling problem in hybrid Clouds. In addition, a set of particular adaptive evolutionary operators have been designed. Conducted simulations lead to significant results compared with a set of well-performed algorithms such NSGA-II, OMOPSO and SMPSO, especially, for the most-demanding workload of workflows.

Towards the Development of a Cloud Computing Intrusion Detection Framework Using an Ensemble Hybrid Feature Selection Approach

Attacks on cloud computing (CC) services and infrastructure have raised concerns about the efficacy of data protection mechanisms in this environment. The framework developed in this study (CCAID: cloud computing, attack, and intrusion detection) aims to improve the performance of intrusion detection systems (IDS) operating in CC environments. It deploys a proposed new hybrid ensemble feature selection (FS) method. The ensemble includes FS algorithms of three different types (filter, wrapper, and embedded algorithms). The selected features used to train the ML (machine learning) model of the intrusion detection component comprised a binary detection engine for the identification of malicious/attack packets and a multiclassification detection engine for the identification of the type of attack. Both detection engines deploy ensemble classifiers. Experiments were carried out using the NSL KDD dataset. The binary model achieved a classification accuracy of 99.55% with a very low false alarm rate of 0.45%. The classification accuracy of the multiclassification model was also high (98.92%). These results compare very favourably with the results reported in the literature and indicate the feasibility of the framework implementation.