Security Implications of Edge Computing in Cloud Networks

Security issues in cloud networks and edge computing have become very common. This research focuses on analyzing such issues and developing the best solutions. A detailed literature review has been conducted in this regard. The findings have shown that many challenges are linked to edge computing, such as privacy concerns, security breaches, high costs, low efficiency, etc. Therefore, there is a need to implement proper security measures to overcome these issues. Using emerging trends, like machine learning, encryption, artificial intelligence, real-time monitoring, etc., can help mitigate security issues. They can also develop a secure and safe future in cloud computing. It was concluded that the security implications of edge computing can easily be covered with the help of new technologies and techniques.

Efficient Video Transmission in D2D Assisted Mobile Cloud Networks

The increasing popularity of smart mobile devices and the rise of online services has increased the requirements for efficient dissemination of social video contents. In this paper,we study the problem of distributing video from cloud server to users in partially connected cooperative D2 D network using network coding. In such a scenario, the transmission conflicts occur from simultaneous transmissions of multiple devices, and the scheduling decision should be made not only on the encoded packets but also on the set of transmitting devices. We analyze the lower bound and give an integer linear formulation of the joint optimization problem over the set of transmitting devices and the packet combinations.We also propose a heuristic solution for this setup using a conflict graph and local graph at every device. Simulation results show that our coding scheme significantly reduces the number of transmission slots, which will increase the efficiency of video delivery.

Joint Resource Allocation Using Evolutionary Algorithms in Heterogeneous Mobile Cloud Computing Networks

The problem of joint radio and cloud resources allocation is studied for heterogeneous mobile cloud computing networks. The objective of the proposed joint resource allocation schemes is to maximize the total utility of users as well as satisfy the required quality of service(QoS) such as the end-to-end response latency experienced by each user. We formulate the problem of joint resource allocation as a combinatorial optimization problem. Three evolutionary approaches are considered to solve the problem: genetic algorithm(GA), ant colony optimization with genetic algorithm(ACO-GA), and quantum genetic algorithm(QGA). To decrease the time complexity, we propose a mapping process between the resource allocation matrix and the chromosome of GA, ACO-GA, and QGA, search the available radio and cloud resource pairs based on the resource availability matrixes for ACOGA, and encode the difference value between the allocated resources and the minimum resource requirement for QGA. Extensive simulation results show that our proposed methods greatly outperform the existing algorithms in terms of running time, the accuracy of final results, the total utility, resource utilization and the end-to-end response latency guaranteeing.

Semi-Blind Pilot-Aided Channel Estimation in Uplink Cloud Radio Access Networks

In this paper, a quasi-Newton method fbr semi-blind estimation is derived for channel estimation in uplink cloud radio access networks （C-RANs）. Different from traditional pilot-aided estimation, semiblind estimation utilizes the unknown data symbols in addition to the known pilot symbols to estimate the channel. An initial channel state information （CSI） obtained by least-squared （LS） estimation is needed in semi-blind estimation. BFGS （Brayben, Fletcher, Goldfarb and Shanno） algorithm, which employs data as well as pilot symbols, estimates the CSI though solving the problem provided by maximum-likelihood （ML） principle. In addition, mean-square-error （MSE） used to evaluate the estimation performance can be further minimized with an optimal pilot design. Simulation results show that the semi-blind estimation achieves a significant improvement in terms of MSE performance over the conventional LS estimation by utilizing data symbols instead of increasing the number of pilot symbols, which demonstrates the estimation accuracy and spectral efficiency are both improved by semiblind estimation for C-RANs.

A Novel Stateful PCE-Cloud Based Control Architecture of Optical Networks for Cloud Services

The next-generation optical network is a service oriented network,which could be delivered by utilizing the generalized multiprotocol label switching(GMPLS) based control plane to realize lots of intelligent features such as rapid provisioning,automated protection and restoration(P&R),efficient resource allocation,and support for different quality of service(QoS) requirements.In this paper,we propose a novel stateful PCE-cloud(SPC)based architecture of GMPLS optical networks for cloud services.The cloud computing technologies(e.g.virtualization and parallel computing) are applied to the construction of SPC for improving the reliability and maximizing resource utilization.The functions of SPC and GMPLS based control plane are expanded according to the features of cloud services for different QoS requirements.The architecture and detailed description of the components of SPC are provided.Different potential cooperation relationships between public stateful PCE cloud(PSPC) and region stateful PCE cloud(RSPC) are investigated.Moreover,we present the policy-enabled and constraint-based routing scheme base on the cooperation of PSPC and RSPC.Simulation results for verifying the performance of routing and control plane reliability are analyzed.

ZTE Cloud Radio Solution to Usher in New Era of High-Performance LTE Networks

ZTE showcased its innovative cloud radio solution for 4G network optimization at the Mobile Asia Expo in Shanghai.

Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent

The cloud boundary network environment is characterized by a passive defense strategy,discrete defense actions,and delayed defense feedback in the face of network attacks,ignoring the influence of the external environment on defense decisions,thus resulting in poor defense effectiveness.Therefore,this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent,designs the network structure of the intelligent agent attack and defense game,and depicts the attack and defense game process of cloud boundary network;constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment,and portrays the interaction process between intelligent agent and environment;establishes the reward mechanism based on the attack and defense gain,and encourage intelligent agents to learn more effective defense strategies.the designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics,interaction lag,and control dispersion in the defense decision process of cloud boundary networks,and improve the autonomy and continuity of defense decisions.

A Novel Load Balancing Strategy of Software-Defined Cloud/Fog Networking in the Internet of Vehicles

The Internet of Vehicles(IoV)has been widely researched in recent years,and cloud computing has been one of the key technologies in the IoV.Although cloud computing provides high performance compute,storage and networking services,the IoV still suffers with high processing latency,less mobility support and location awareness.In this paper,we integrate fog computing and software defined networking(SDN) to address those problems.Fog computing extends computing and storing to the edge of the network,which could decrease latency remarkably in addition to enable mobility support and location awareness.Meanwhile,SDN provides flexible centralized control and global knowledge to the network.In order to apply the software defined cloud/fog networking(SDCFN) architecture in the IoV effectively,we propose a novel SDN-based modified constrained optimization particle swarm optimization(MPSO-CO) algorithm which uses the reverse of the flight of mutation particles and linear decrease inertia weight to enhance the performance of constrained optimization particle swarm optimization(PSO-CO).The simulation results indicate that the SDN-based MPSO-CO algorithm could effectively decrease the latency and improve the quality of service(QoS) in the SDCFN architecture.

Visualization of flatness pattern recognition based on T-S cloud inference network

Flatness pattern recognition is the key of the flatness control. The accuracy of the present flatness pattern recognition is limited and the shape defects cannot be reflected intuitively. In order to improve it, a novel method via T-S cloud inference network optimized by genetic algorithm(GA) is proposed. T-S cloud inference network is constructed with T-S fuzzy neural network and the cloud model. So, the rapid of fuzzy logic and the uncertainty of cloud model for processing data are both taken into account. What's more, GA possesses good parallel design structure and global optimization characteristics. Compared with the simulation recognition results of traditional BP Algorithm, GA is more accurate and effective. Moreover, virtual reality technology is introduced into the field of shape control by Lab VIEW, MATLAB mixed programming. And virtual flatness pattern recognition interface is designed.Therefore, the data of engineering analysis and the actual model are combined with each other, and the shape defects could be seen more lively and intuitively.

Energy-Performance Tradeoffs in laaS Cloud with Virtual Machine Scheduling

In the cloud data centers,how to map virtual machines(VMs) on physical machines(PMs) to reduce the energy consumption is becoming one of the major issues,and the existing VM scheduling schemes are mostly to reduce energy consumption by optimizing the utilization of physical servers or network elements.However,the aggressive consolidation of these resources may lead to network performance degradation.In view of this,this paper proposes a two-stage VM scheduling scheme:(1) We propose a static VM placement scheme to minimize the number of activating PMs and network elements to reduce the energy consumption;(2) In the premise of minimizing the migration costs,we propose a dynamic VM migration scheme to minimize the maximum link utilization to improve the network performance.This scheme makes a tradeoff between energy efficiency and network performance.We design a new twostage heuristic algorithm for a solution,and the simulations show that our solution achieves good results.

Pervasive Dependability in Wireless Cloud Networking: a BlueGreen Topological Control Approach

The future Wireless Cloud Networks (WCNs) are required to satisfy both extremely high levels of service resilience and security assurance (i.e., Blue criteria) by overproviding backup network resources and cryptographic protection on wireless communication respectively, as well as minimizing energy consumption (i.e., Green criteria) by switching off unnecessary resources as much as possible. There is a contradiction to satisfy both Blue and Green design criteria simultaneously. In this paper, we propose a new BlueGreen topological control scheme to leverage the wireless link connectivity for WCNs using an adaptive encryption key allocation mechanism, named as Shared Backup Path Keys (SBPK). The BlueGreen SBPK can take into account the network dependable requirements such as service resilience, security assurance and energy efficiency as a whole, so as trading off between them to find an optimal solution. Actually, this challenging problem can be modeled as a global optimization problem, where the network working and backup elements such as nodes, links, encryption keys and their energy consumption are considered as a resource, and their utilization should be minimized. The case studies confirm that there is a trade-off optimal solution between the capacity efficiency and energy efficiency to achieve the dependable WCNs.

Binary Multifold Encryption Technique for Complex Cloud Systems

Data security is a major cloud computing issue due to different usertransactions in the system. The evolution of cryptography and cryptographic analysis are regarded domains of current research. deoxyribo nucleic acid (DNA) cryptography makes use of DNA as a sensing platform, which is then manipulated usinga variety of molecular methods. Many security mechanisms including knowledgebased authentication, two-factor authentication, adaptive authentication, multifactorauthentication and single password authentication have been deployed. These cryptographic techniques have been developed to ensure confidentiality, but most ofthem are based on complex mathematical calculations and equations. In the proposed approach, a novel and unique Hybrid helix scuttle-deoxy ribo nucleic acids(HHS-DNA) encryption algorithm has been proposed which is inspired by DNAcryptography and Helix scuttle. The proposed HHS-DNA is a type of multifold binary version of DNA (MF-BDNA). The major role of this paper is to present a multifold HHS-DNA algorithm to encrypt the cloud data assuring more security with lesscomplexity. The experimentation is carried out and it reduces the encryption time,cipher text size, and improves throughput. When compared with previous techniques, there is a 45% improvement in throughput, 37% fast in encryption time,54.67% cipher text size. The relevant experimental results and foregoing analysisshow that this method is of good robustness, stability, and security.

Research on Optimized Allocation of English Teaching Resources Based on Network Cloud Platform

The allocation of resources in English teaching can improve the ability of resource sharing, in order to optimize the allocation of resources, so as to improve the performance of English teaching, and promote the construction of English teaching resources database, a method of optimizing the allocation of English teaching resources is proposed based on network cloud platform. Text semantic key words conceptual decision tree model is constructed for massive English teaching resources allocation, semantic information conversion method is used to compute key semantic features of English Teaching resources, the concept convergence point of English Teaching resource allocation is formed in semantic model. According to the set between the upper and lower relationship, a decision tree model of English Teaching semantic subject words is constructed, semantic conversion and information extraction are realized. English teaching resources optimization allocation simulation is taken in the cloud platform, simulation results show that the scheduling performance of English teaching resources is better, and the adaptive allocation ability of English teaching resources is stronger, and the resource utilization rate is higher.

Computing Power Network:The Architecture of Convergence of Computing and Networking towards 6G Requirement

In 6G era,service forms in which computing power acts as the core will be ubiquitous in the network.At the same time,the collaboration among edge computing,cloud computing and network is needed to support edge computing service with strong demand for computing power,so as to realize the optimization of resource utilization.Based on this,the article discusses the research background,key techniques and main application scenarios of computing power network.Through the demonstration,it can be concluded that the technical solution of computing power network can effectively meet the multi-level deployment and flexible scheduling needs of the future 6G business for computing,storage and network,and adapt to the integration needs of computing power and network in various scenarios,such as user oriented,government enterprise oriented,computing power open and so on.

Beamforming and fronthaul compression design for intelligent reflecting surface aided cloud radio access networks

Owing to the inherent central information processing and resource management ability,the cloud radio access network(C-RAN)is a promising network structure for an intelligent and simplified sixth-generation(6G)wireless network.Nevertheless,to further enhance the capacity and coverage,more radio remote heads(RRHs)as well as high-fidelity and low-latency fronthaul links are required,which may lead to high implementation cost.To address this issue,we propose to exploit the intelligent reflecting surface(IRS)as an alternative way to enhance the C-RAN,which is a low-cost and energy-efficient option.Specifically,we consider the uplink transmission where multi-antenna users communicate with the baseband unit(BBU)pool through multi-antenna RRHs and multiple IRSs are deployed between the users and RRHs.RRHs can conduct either point-to-point(P2P)compression or Wyner-Ziv coding to compress the received signals,which are then forwarded to the BBU pool through fronthaul links.We investigate the joint design and optimization of user transmit beamformers,IRS passive beamformers,and fronthaul compression noise covariance matrices to maximize the uplink sum rate subject to fronthaul capacity constraints under P2P compression and Wyner-Ziv coding.By exploiting the Arimoto-Blahut algorithm and semi-definite relaxation(SDR),we propose a successive convex approximation approach to solve non-convex problems,and two iterative algorithms corresponding to P2P compression and Wyner-Ziv coding are provided.Numerical results verify the performance gain brought about by deploying IRS in C-RAN and the superiority of the proposed joint design.

A Cloud Detection Method for Landsat 8 Satellite Remote Sensing Images Based on Improved CDNet Model

Cloud detection in remote sensing images is a crucial task in various applications,such as meteorological disaster prediction and earth resource exploration,which require accurate cloud identi¯cation.This work proposes a cloud detection model based on the Cloud Detection neural Network(CDNet),incorporating a fusion mechanism of channel and spatial attention.Depthwise separable convolution is adopted to achieve a lightweight network model and enhance the e±ciency of network training and detection.In addition,the Convolutional Block Attention Module(CBAM)is integrated into the network to train the cloud detection model with attention features in channel and spatial dimensions.Experiments were conducted on Landsat 8 imagery to validate the proposed improved CDNet.Averaged over all testing images,the overall accuracy(OA),mean Pixel Accuracy(mPA),Kappa coe±cient and Mean Intersection over Union(MIoU)of improved CDNet were 96.38%,81.18%,96.05%,and 84.69%,respectively.Those results were better than the original CDNet and DeeplabV3+.Experiment results show that the improved CDNet is e®ective and robust for cloud detection in remote sensing images.

A survey of cloud network fault diagnostic systems and tool

Recently, cloud computing has become a vital part that supports people’s normal lives and production.However, accompanied by the increasing complexity of the cloud network, failures constantly keep coming up and cause huge economic losses. Thus, to guarantee the cloud network performance and prevent execrable effects caused by failures, cloud network diagnostics has become of great interest for cloud service providers. Due to the characteristics of cloud network(e.g., virtualization and multi-tenancy), transplanting traditional network diagnostic tools to the cloud network face several difficulties. Additionally, many existing tools cannot solve problems in the cloud network. In this paper, we summarize and classify the state-of-the-art technologies of cloud diagnostics which can be used in the production cloud network according to their features. Moreover, we analyze the differences between cloud network diagnostics and traditional network diagnostics based on the characteristics of the cloud network. Considering the operation requirements of the cloud network, we propose the points that should be cared about when designing a cloud network diagnostic tool. Also, we discuss the challenges that cloud network diagnostics will face in future development.

Integration of IoT and DRAGON-lab in cloud environment

Distributed research ＆ academic gigabits open network lab （DRAGON-lab） is the only test-bed for research purpose related to next generation interact （NGI） which based on the confederation network using three layers cloud structure. As an essential part of NGI, the research related to the Internet of things （loT） devices should be applied on the DRAGON-lab platform. This paper proposes an approach to converging the IoT devices to confederation network by integrating each layer of cloud structure between DRAGON-lab and IoT. This research activity extends the use of DRAGON-lab, makes the IoT and wireless sensor network （WSN） devices working well in the confederation network. It was also a foundational research for three layers cloud structure, which could be reused for other related research. Finally, it finishes the preparation work for IoT internet protocol version 6 （IPv6） devices research in DRAGON-Jab, which is essential for the NGI.

Bayes-Based ARP Attack Detection Algorithm for Cloud Centers

To address the issue of internal network security, Software-Defined Network（SDN） technology has been introduced to large-scale cloud centers because it not only improves network performance but also deals with network attacks. To prevent man-in-the-middle and denial of service attacks caused by an address resolution protocol bug in an SDN-based cloud center, this study proposed a Bayes-based algorithm to calculate the probability of a host being an attacker and further presented a detection model based on the algorithm. Experiments were conducted to validate this method.

An Algorithm to Recognize the Target Object Contour Based on 2D Point Clouds by Laser-CCD-Scanning

For a vision measurement system consisted of laser-CCD scanning sensors, an algorithm is proposed to extract and recognize the target object contour. Firstly, the two-dimensional（2D） point cloud that is output by the integrated laser sensor is transformed into a binary image. Secondly, the potential target object contours are segmented and extracted based on the connected domain labeling and adaptive corner detection. Then, the target object contour is recognized by improved Hu invariant moments and BP neural network classifier. Finally, we extract the point data of the target object contour through the reverse transformation from a binary image to a 2D point cloud. The experimental results show that the average recognition rate is 98.5% and the average recognition time is 0.18 s per frame. This algorithm realizes the real-time tracking of the target object in the complex background and the condition of multi-moving objects.