Cloud Computing Networks Research Articles

End-to-end latency upper bounds and service chain deployment algorithm based on industrial internet network

The diverse service requests in industrial Internet networks require flexible and efficient service chain deployment to ensure the quality of service (QoS). However, current deployment algorithms for service chains are primarily designed to guarantee only low end-to-end latency; they often overlook the amount of service chains that can be accommodated by the network and could lead to severe network load imbalances, significantly reducing service efficiency and causing serious network congestion issues. To address the above issues, we develop a mathematical model of the network topology and service request chains by integrating Network Function Virtualization (NFV) and Software Defined Networking (SDN). Utilizing network calculus theory, we derive the upper bound of end-to-end delay for service chain routing and analyzed the relationship between the upper bound of service chain routing delay and the resource allocation of Virtual Network Function (VNF) nodes. Based on the aforementioned model, we propose a novel service chain deployment algorithm named the Delay-Aware Load-Balanced Routing Algorithm (DLBRA). DLBRA comprehensively considers network traffic load balancing and end-to-end latency of service chains, rationally allocating VNF node resources to complete the determined service chain routing deployment. Experimental results indicate that, compared to the shortest path and load balancing algorithms, DLBRA not only ensures that the end-to-end delay of the service chain meets its QoS requirements, but also effectively reduces network load imbalance, significantly increasing the number of service chain requests that the network can accommodate. Additionally, DLBRA provides tailored deployment guidance for different types of service chains, such as latency-sensitive and data-intensive service chains, ensuring optimal utilization of network resources. This algorithm enhances the efficiency of service chain deployment in industrial internet scenarios and possesses broad application potential in other network environments where delay optimization and load balancing are critical, such as intelligent transportation, cloud computing, and 5G networks.

Advantages of Big Data Analysis and AI Technology in Data Collecting And Processing of Preventive Maintenance

In recent years, the emergence of Cyber-Physical systems (CPS), big data, cloud computing, and industrial wireless networks have promoted the development of Industry 4.0. Intelligent manufacturing systems are the leaders of this change. In smart manufacturing, the emphasis will be placed on using technologies such as big data analytics, cloud computing, edge computing, and artificial intelligence (AI). The advantages of new technologies are significantly reflected in the comparison of preventive maintenance and traditional maintenance based on intelligent technology: preventive maintenance significantly reduces the cost of prevention and, at the same time, dramatically improves the accuracy and timeliness of maintenance, bringing inestimable value to intelligent manufacturing. This paper will analyze new technologies’ innovative advantages and technical prospects through big data analysis and specific application scenarios of AI technology in preventive maintenance. Firstly, in data acquisition, this paper examines the architecture of preventive maintenance systems based on big data. Then, it analyzes the composition of the data pipeline: in the realization of data collection and processing in the field of cloud computing and edge computing, and the aspect of data processing, it discusses the participation of AI technology in data understanding and processing and the unprecedented changes it brings.

Enhancing cybersecurity in cloud computing and WSNs: A hybrid IDS approach

The evolution of cloud computing has revolutionized how users access services, simplifying the development and deployment of applications across various industries. With its pervasive adoption, robust security measures become imperative. Integrating Intrusion Detection Systems (IDSs) into cloud computing and Wireless Sensor Networks (WSNs) addresses these challenges. IDSs serve as attentive protectors, monitoring network traffic and responding to breaches promptly, enhancing security across industries reliant on cloud services. Similarly, IDS integration in WSNs ensures the security of mission-critical operations, despite resource constraints and dynamic topologies, facilitated by cloud computing. This research proposes a hybrid IDS approach, leveraging the NSL-KDD dataset and methodologies like Intrusion Support Scalar Impact Rate (ISSIR), Optimized Support Vector Machine (OSVM), Extended Long-Short-Term Memory (ELSTM), and Multilayer Perceptron Neural Network (MLPNN), enhancing intrusion detection efficacy. ISSIR aids in feature selection, OSVM mitigates localization errors, ELSTM enables precise anomaly detection, and MLPNN provides robust defense mechanisms. Each method is integrated into a collaborative framework to address specific challenges in detecting intrusions with higher accuracy and reduced false positives. The interplay between these methodologies strengthens the overall intrusion detection framework, addressing the dynamic nature of cybersecurity threats. Results demonstrate the superior performance of MLPNN across various metrics, showcasing its effectiveness in accurately predicting outcomes compared to other models. The proposed MLPNN hybrid system achieves an accuracy of 99.9%, surpassing state-of-the-art methods. This study underscores the significance of advancing IDSs in cloud computing and WSNs, offering insights into enhancing security and mitigating vulnerabilities in an interconnected digital landscape.

Classification of DDoS attack traffic on SDN network environment using deep learning

Distributed Denial of Service (DDoS) attack is a major threat to the Internet of Things (IoT), Software Defined Networks (SDN), and Cloud Computing Networks. Due to the tremendous applications of IoT networks, the number of DDoS attacks is increasing significantly, and most sophisticated DDoS attacks are generated through IoT botnets. An IoT botnet-based DDoS attack can disrupt the network quickly with a surge of malicious traffic. Especially in an SDN network, it is important to detect the DDoS attack before it occurs to the SDN controller. DDoS attacks on the centralized controller of the SDN can disrupt the whole network. So, identifying DDoS attacks at the earliest is a critical security measure for network experts and practitioners. In this paper, we analyze the DDoS attack on an SDN environment and develop a method to identify the DDoS attack using Deep Learning (DL). The proposed method converts the captured raw network traffic to image data and classifies the malicious data from normal data. The method is evaluated on our test-bed simulated dataset and two other benchmark datasets. The experimental comparison shows that the proposed method performs better on all three datasets, giving more than 99% classification accuracy.

Exploration and optimization of novel replacement and prefetching strategies for inefficiencies of advanced MRAM-based hybrid cache systems

With the emergence of cutting-edge hardware systems such as cloud computing, edge computing, and on-chip neural network accelerators, how to design advanced memory strategies to substitute the traditional ones for maximizing the potential performance of non-volatile memory (NVM) under the existing hardware conditions, has become an urgent research issue for both academia and industrial communities. It is promising and innovative to improve computer systems in the layer of data exchanging with the emerging advanced semiconductor devices. In the paper, to address the inefficiencies of write-intensive, high power consumption, low hit rate and so on, which exist in hybrid magnetic random access memory cache systems, three novel cache replacement strategies and two cache prefetching strategies are put forward. The proposed triple novel replacement strategies, including historical frequency and time judgments, duplicate data-aware deletion, and dynamic relevance factors computing, can be utilized to compensate for the shortcomings of the traditional least recently used replacement strategy, respectively. In the two novel prefetching strategies, region distribution parameters and Listnet ranking network are imported into the caching process, respectively, to achieve optimized hitting performance. The simulation results demonstrate that the proposed replacement strategies can achieve up to 61.76%, 84.91%, 56.49%, and 53.21% optimization of write count, hit rate, dynamic power, and IPC compared to the conventional one. The proposed prefetching strategy can achieve up to 91.27%, 49.25% hit rate and IPC optimization. Meanwhile, the synthetic evaluation of the replacement and prefetching strategies are elaborated in the paper, including multi-core characteristics, information entropy, interplays and the performance constraints between replacement and prefetching mechanism, which would facilitate more credible ideas for future memory inefficiencies management and strategy design.

An In-Memory-Computing Structure with Quantum-Dot Transistor Toward Neural Network Applications: From Analog Circuits to Memory Arrays

The rapid advancements in artificial intelligence (AI) have demonstrated great success in various applications, such as cloud computing, deep learning, and neural networks, among others. However, the majority of these applications rely on fast computation and large storage, which poses significant challenges to the hardware platform. Thus, there is a growing interest in exploring new computation architectures to address these challenges. Compute-in-memory (CIM) has emerged as a promising solution to overcome the challenges posed by traditional computer architecture in terms of data transfer frequency and energy consumption. Non-volatile memory, such as Quantum-dot transistors, has been widely used in CIM to provide high-speed processing, low power consumption, and large storage capacity. Matrix-vector multiplication (MVM) or dot product operation is a primary computational kernel in neural networks. CIM offers an effective way to optimize the performance of the dot product operation by performing it through an intertwining of processing and memory elements. In this paper, we present a novel design and analysis of a Quantum-dot transistor (QDT) based CIM that offers efficient MVM or dot product operation by performing computations inside the memory array itself. Our proposed approach offers energy-efficient and high-speed data processing capabilities that are critical for implementing AI applications on resource-limited platforms such as portable devices.

Distributed Implementation of Computing Language Model in Cloud Computing Network

In this paper, the distributed implementation of Computing Language Model (CLM) in cloud computing network is studied and discussed. With the advent of the era of big data, the application of CLM in natural language processing (NLP), machine translation and other fields is increasingly extensive, and the demand for computing resources is also increasing. As an effective way to manage computing resources, distributed computing can make full use of resources in cloud computing environment and realize efficient execution of computing tasks. In this paper, the application of distributed computing methods such as data parallelism, model parallelism and hybrid parallelism in cloud computing environment is studied and analyzed, and the advantages and disadvantages of different methods in CLM implementation are discussed. The experimental results show that the hybrid parallel method can effectively combine the advantages of data parallelism and model parallelism, and improve the training efficiency and performance of CLM. This study provides important theoretical guidance and technical support for the distributed implementation of CLM in cloud computing networks, and is of great significance for further promoting the development of big data and AI technology.

A Novel Approach of Cloud Computing Network for Authentication and Security Enhancement of IoT Enabled Cancer Forecasting System

Recently, a variety of approaches have been employed to address a broad spectrum of real-world issues; these methodology cover a variety of areas, including healthcare systems. Previous researchers concentrated on health-care monitoring software. It had several shortcomings, such as poor health-care data storage, time, expense, and processing complexity. This paper proposes a unique IoT-enabled and secured clinical monitoring paradigm to address these issues. Initially, implant several sensors to gather information on vital indicators like body temperature fluctuation. Phone numbers, marital status, heart rate deviation, residence, name, age, and blood pressure are among the patient's health information. The IoT medical sensor dataset is used in this investigation. During pre-processing, extra unnecessary attributes are removed, resulting in data size reduction and normalization. A convolutional neural network supporting the classification of cancer sickness that is based on the improved teaching-learning optimization (CNN-ITLO) method. The CNN-ITLO model ascertains whether or not the patient is cancer-prone based on the sensor input. The management of the hospital receives the gathered data after which it is analyzed. Lastly, the homomorphic encryption approach is used to encrypt and store the patient's data on the cloud. The Java platform will be used to carry out the recommended work. According to the experiment results, the suggested method performed better than current cutting-edge practices.

Monitoring and Mitigating Climate-Induced Natural Disasters with Cloud IoT

Innovative solutions are necessary for efficient monitoring and mitigation techniques as climate change increases the frequency and severity of natural catastrophes. In this study, we investigate how Cloud Internet of Things (IoT) might help mitigate climate-related calamities. Through the use of cloud computing and sensor networks, Cloud IoT facilitates the capture, processing, and distribution of data in near-real time. This strategy promotes catastrophe planning, response, and recovery by offering early warnings, predictive insights, and simplified communication. The article emphasises the cumulative influence of Cloud IoT components such as sensor networks, data analytics, decision support systems, and remote control in the context of disaster management. Cloud IoT is useful in real-world scenarios, such as the tracking of floods in Bangladesh and the identification of wildfires in California. These cases show how the technology may prevent injuries and preserve property through early warnings and well-coordinated responses. Despite its potential, it faces obstacles including ensuring the safety of data and dealing with issues related to the necessary infrastructure. In conclusion, including Cloud IoT in disaster management provides a cost-effective, scalable, and efficient solution, greatly contributing to constructing resilient communities and creating a sustainable future in the face of climate-induced natural catastrophes.

Application of SDN-IP hybrid network multicast architecture in Commercial Aerospace Data Center

Abstract The increasing amount of massive data generated by commercial spacecraft in orbit puts forward higher and higher requirements for the stability, reliability, and computing power of computer systems for commercial aerospace data center. Data center computer systems are gradually transforming from X86 architecture and IP network model to a platform model with cloud computing and software-defined network (SDN) technology. This article proposes a new network architecture based on a unicast/multicast protocol for data interaction between the SDN and IP network. There are three main contributions of this article. The first is that the architecture proposed in this article aims to reduce end-to-end transmission latency and packet loss. The second is to improve the flexibility of system configuration and precise control when the SND controller state changes. The third is to verify the feasibility of deploying network architecture in a real data center environment.

Survey on Intrusion Tolerant Systems in Emerging Technologies

The wide spread of the Internet leads to the expansion of networks and the increase of data. For this reason, the use of emerging technologies became a necessity to cover the needs and the lapses in traditional systems. However, these systems might reveal security flaws and become vulnerable to attacks. Security is therefore essential to ensure the confidentiality, integrity, and availability of system data and services. As the attacks get more sophisticated, the protection gets more difficult. In fact, mechanisms such as Firewalls, IDSs, and IPSs are still ineffective against unknown attacks. Consequently, defense-in-depth security should include intrusion tolerance. The concern of intrusion tolerance is not how to defend or detect the intrusion, but how to mask or restrain the intrusion when the network has been intruded. Using three separate lines of approaches: Detection-Triggered, Algorithm-Driven, and Recovery Based, this paper will present, analyze, and contrast several works in two different environments: Cloud Computing and Software Defined Networks (SDNs), to proceed towards Intrusion Tolerant Systems (ITSs).

The Application Status and Development Trend of Cloud Computing Technology in Chess

With the rapid advancement of computer technology, renowned chess engines have timely updated and iterated, incorporating state-of-the-art technologies such as cloud computing, machine learning, deep learning, and neural networks. These technologies assist players in studying historical matches and improving their playing abilities and skills. This article focuses on the current status of integrating cloud computing technology with chess. It analyzes the developmental background and performance characteristics of different chess engines, explores the guiding principles and underlying logic of mainstream engines in analyzing chess games, and examines the current application status and future trends of cloud computing technology in chess. Furthermore, the article provides personal perspectives and suggestions regarding existing challenges and future directions of development.

FedAAA-SDN: Federated Authentication, Authorization and Accounting in SDN controllers

Modern technology advancements such as Software Defined Networking (SDN) have been employed in distinct scenarios such as cloud computing and wireless networks, and have made network administration simpler since it gives greater flexibility in centralizing the configurations of a network and its data. Several SDN controllers have started to offer Authorization, Authentication, and Accounting (AAA) services to improve user management and security, including support for OAuth2.0. However, basic authentication schemes using the tuple username and password are not considered secure nowadays (prone to brute force attacks), or the use of basic tokens in OAuth 2.0 is propitious to security vulnerabilities not meeting the security requirements in current and future use cases dealing with authentication. Therefore, the main objective of this work is the design and evaluation of the FedAAA-SDN framework enabling authentication, authorization, and accounting mechanisms in SDN controllers controlling different types of networks. In particular, the FedAAA-SDN framework enables federated authentication and authorization processes for network functions by enabling the conveyance of identity and authentication information across heterogeneous domains/networks, allowing also network operators to enforce authorization policies for network functions in wired and wireless networks. A FedAAA-SDN proof of concept is implemented with OpenDaylight as the SDN controller, OpenID Connect as the authentication and authorization mechanism, and Keycloak as the OpenID Provider. FedAAA-SDN also includes the concept of trust levels and trust policies that rely on the context information of a user’s device and associated connected access networks. The implementation of the FedAAA-SDN framework proof of concept involves modifications in the OpenDaylight AAA filter component to secure all the interactions with the several network applications/subcomponents of the OpenDaylight SDN controller. STRIDE security analysis demonstrates that the FedAAA-SDN framework is able to reduce the number of threats.

Exploring Social Credit for Fairness in Cloud Computing Networks

Cloud-based computing networks have taken over the digital landscape. From small non-profits to large multinational corporations, more and more entities have been offloading computing effort to the cloud in order to take advantage of the increased cost-efficiency and scalability of cloud computing. One of the new types of cloud that have emerged is P2P cloud, which disengages from a traditional datacenter setup by allowing users to instead share their own computing hardware into a cloud to reap the benefits of cloud computing's advantages at an even lower cost. However, this new paradigm comes with a slew of challenges: (i) security, when operating with the devices of strangers and (ii) fairness when not operating in a market-based system. This paper aims to address these two issues by proposing an algorithm based on social credits for a P2P cloud system that uses a social network to establish its security measures. We implement our new Social Credit algorithm along with two other task-migration-based load-balancing algorithms adapted for a P2P social cloud in “Cloudsim Plus”, and the relative gains are shown in terms of fairness and other related metrics.

Location Based Power Reduction Cloud Integrated Social Sensor Network

It is great to hear about the advancements in wireless sensor networks and their applications, as well as the integration of cloud computing to enhance data analysis and storage capabilities. Indeed, these technologies have opened up numerous possibilities across various fields, including infrastructure tracking, environmental monitoring, healthcare, and more. The concept of a social sensor cloud, as you mentioned, brings an interesting dimension to this technology landscape by focusing on knowledge-sharing and connecting like-minded individuals or organizations. This could potentially lead to more collaborative and efficient solutions across a wide range of domains. Energy efficiency is a critical consideration in the design and operation of wireless sensor networks and the cloud infrastructure that supports them. The limited battery life of sensors necessitates careful management of energy consumption to ensure optimal functionality and longevity. Sleep scheduling methods are a common technique used to manage energy consumption in these networks. By coordinating when sensors are active and when they are in a low-power sleep mode, energy consumption can be significantly reduced without compromising the network's overall effectiveness. In the context of the Social Sensor Cloud, managing energy efficiency becomes even more crucial due to the shorter battery life of the sensors involved. This is particularly relevant given the growing concerns about environmental sustainability and the need to reduce energy consumption across technological systems. It's clear that your research paper addresses these challenges head-on, by exploring energy-efficient techniques for the Social Sensor Cloud. Sleep scheduling is just one of the many strategies that researchers and engineers are working on to strike a balance between functionality and energy consumption. Other methods might include optimizing data transfer protocols, developing energy-harvesting mechanisms, and enhancing sensor hardware efficiency. As technology continues to evolve, the integration of wireless sensor networks, cloud computing, and social networks will likely pave the way for innovative solutions and transformative applications. Addressing energy efficiency concerns will undoubtedly play a crucial role in ensuring the long-term viability and positive impact of these technologies.

Predicting the future landscape of Dhanbad District: an analysis of land-use change and urban sprawl through cloud computing and neural networks

Predicting the future landscape of Dhanbad District: an analysis of land-use change and urban sprawl through cloud computing and neural networks

Retracted: Design and Implementation of Cloud Computing Network Security Virtual Computing and Defense Technology

Retracted: Design and Implementation of Cloud Computing Network Security Virtual Computing and Defense Technology

Joint Power Control and Task Offloading in Collaborative Edge–Cloud Computing Networks

Mobile edge computing arises as a promising technology to allow mobile devices to offload delay-sensitive and computation-intensive tasks to the nearby edge servers. However, overloaded tasks from mobile devices (MDs) lead to a large latency due to limited computing and channel resources. To mitigate this situation, a collaborative edge-cloud computing network is considered in this paper. Based on power control and task offloading, we formulate a mixed-integer nonlinear programming (MINLP) problem to minimize the weighted sum of the energy consumption and the latency. This NP-hard problem is decomposed into a real variable problem and an integer linear programming problem. By leveraging the proposed extremevalue descent (EVD) method, the optimal power strategy is obtained by solving the real variable problem. The simplex method and branch-and-bound method are adopted to find the optimal offloading strategy. Although these two sub-problems are well solved, the solutions may be unsatisfactory for the original problem. To find a high-quality solution, we use an alternating optimization (AO) method to jointly optimize the decomposed problems. Theoretical analysis demonstrates that the EVD method converges at the rate of O(1/s) and the AO method can converge to a sub-optimal solution if not the optimal solution. Simulation results show that the proposed algorithms significantly outperform other benchmark schemes.

A Guide to Data Collection for Computation and Monitoring of Node Energy Consumption

The digital transition that drives the new industrial revolution is largely driven by the application of intelligence and data. This boost leads to an increase in energy consumption, much of it associated with computing in data centers. This fact clashes with the growing need to save and improve energy efficiency and requires a more optimized use of resources. The deployment of new services in edge and cloud computing, virtualization, and software-defined networks requires a better understanding of consumption patterns aimed at more efficient and sustainable models and a reduction in carbon footprints. These patterns are suitable to be exploited by machine, deep, and reinforced learning techniques in pursuit of energy consumption optimization, which can ideally improve the energy efficiency of data centers and big computing servers providing these kinds of services. For the application of these techniques, it is essential to investigate data collection processes to create initial information points. Datasets also need to be created to analyze how to diagnose systems and sort out new ways of optimization. This work describes a data collection methodology used to create datasets that collect consumption data from a real-world work environment dedicated to data centers, server farms, or similar architectures. Specifically, it covers the entire process of energy stimuli generation, data extraction, and data preprocessing. The evaluation and reproduction of this method is offered to the scientific community through an online repository created for this work, which hosts all the code available for its download.

SOUTH AFRICA’S RESPONSE TO THE DIGITAL ECONOMY’S DIRECT TAX CHALLENGES – PART 1

International tax rules were developed more than a century ago. At their core is the principle that profits should be taxed where economic activities physically take place and where value is created. Advances in technology and the progression of the fourth industrial revolution have changed how businesses around the world operate and have given rise to the “digital economy”. Businesses no longer need to be “physically” present in a jurisdiction but can operate digitally or virtually anywhere in the world. New business models such as e-commerce, payment services, app stores, online advertising, cloud computing and participative network platforms have emerged. The digital economy and these new business models pose various challenges to the effectiveness of rules on the current jurisdiction to tax; businesses are able to derive significant economic benefits from a country without a “taxable nexus” to such country – for example, without the creation of a fixed place of business, permanent establishment or establishing a place of effective management. The digital economy is global in nature and, therefore, policy actions dealing with the global economy need a global approach. The Organisation for Economic Co-operation and Development (OECD) has taken a leading role in developing new direct-tax rules that will address the tax challenges posed by the digital economy and has agreed to develop a two-pillar solution that can be consented to internationally and implemented by countries. Pillar one proposes new rules on tax nexus and profit allocation for large multinational enterprises (MNEs) that meet certain revenue and profitability thresholds. The rules do not require MNEs to be physically present in a jurisdiction. Pillar two proposes mechanisms to ensure large MNEs pay a minimum level of tax (currently set at 15 per cent) regardless of where their headquarters are or the jurisdictions in which they operate. Some countries (such as the United Kingdom (UK), United States of America (USA), India, and Nigeria) have opted to take unilateral measures as they wait for a global solution. These unilateral measures are often uncoordinated and give rise to some undesirable consequences, such as double taxation. South Africa has decided to wait for global consensus and is currently not taxing the digital economy through its direct-tax rules. Although the OECD solutions are helpful proposals on taxing the digital economy and are a step in the right direction, it is submitted that they are not completely suited for South Africa as a developing African country; they do not consider some of South Africa’s unique circumstances, such as the prevalence of corruption, semi-skilled tax administration and limited resources. South Africa should not merely adopt the rules blindly but should adapt them to suit its needs as a developing country. South Africa needs to protect its tax base while embracing the digital economy; perhaps, while it waits for a global solution, it could strengthen its source rules as recommended by the Davis Tax Committee. This article is divided into two parts. Part 1 evaluates the suitability of South Africa adopting the OECD global solutions to the direct-tax challenges posed by the digital economy in a developing African country; Part 2 evaluates whether South Africa’s response to these challenges is the best option by considering the approach and consequences of select developed and developing jurisdictions (that is, USA, UK, Nigeria and India) adopting unilateral measures while waiting for an OECD global solution.