Intelligent Cloud Based Load Balancing System Empowered with Fuzzy Logic

Cloud computing is seeking attention as a new computing paradigm to handle operations more efficiently and cost-effectively. Cloud computing uses dynamic resource provisioning and de-provisioning in a virtualized environment. The load on the cloud data centers is growing day by day due to the rapid growth in cloud computing demand. Elasticity in cloud computing is one of the fundamental properties, and elastic load balancing automatically distributes incoming load to multiple virtual machines. This work is aimed to introduce efficient resource provisioning and de-provisioning for better load balancing. In this article, a model is proposed in which the fuzzy logic approach is used for load balancing to avoid underload and overload of resources. A Simulator in Matlab is used to test the effectiveness and correctness of the proposed model. The simulation results have shown that our proposed intelligent cloud-based load balancing system empowered with fuzzy logic is better than previously published approaches.     

Machine Learning Based Resource Allocation of Cloud Computing in Auction

Resource allocation in auctions is a challenging problem for cloud computing. However, the resource allocation problem is NP-hard and cannot be solved in polynomial time. The existing studies mainly use approximate algorithms such as PTAS or heuristic algorithms to determine a feasible solution; however, these algorithms have the disadvantages of low computational efficiency or low allocate accuracy. In this paper, we use the classification of machine learning to model and analyze the multi-dimensional cloud resource allocation problem and propose two resource allocation prediction algorithms based on linear and logistic regressions. By learning a small-scale training set, the prediction model can guarantee that the social welfare, allocation accuracy, and resource utilization in the feasible solution are very close to those of the optimal allocation solution. The experimental results show that the proposed scheme has good effect on resource allocation in cloud computing.     

Fuzzy Search for Multiple Chinese Keywords in Cloud Environment

With the continuous development of cloud computing and big data technology, the use of cloud storage is more and more extensive, and a large amount of data is outsourced for public cloud servers, and the security problems that follow are gradually emerging. It can not only protect the data privacy of users, but also realize efficient retrieval and use of data, which is an urgent problem for cloud storage. Based on the existing fuzzy search and encrypted data fuzzy search schemes, this paper uses the characteristics of fuzzy sounds and polysemy that are unique to Chinese, and realizes the synonym construction of keywords through Chinese Pinyin and Chinese-English translation, and establishes the fuzzy word and synonym set of keywords. This paper proposes a Chinese multi-keyword fuzzy search scheme in a cloud environment, which realizes the fuzzy search of multiple Chinese keywords and protects the private key by using a pseudo-random function. Finally, the safety analysis and system experiments verify that the scheme has high security, good practicability, and high search success rate.     

Multi-VMs Intrusion Detection for Cloud Security Using Dempster-shafer Theory

Cloud computing provides easy and on-demand access to computing resources in a configurable pool. The flexibility of the cloud environment attracts more and more network services to be deployed on the cloud using groups of virtual machines (VMs), instead of being restricted on a single physical server. When more and more network services are deployed on the cloud, the detection of the intrusion likes Distributed Denial-of-Service (DDoS) attack becomes much more challenging than that on the traditional servers because even a single network service now is possibly provided by groups of VMs across the cloud system. In this paper, we propose a cloud-based intrusion detection system (IDS) which inspects the features of data flow between neighboring VMs, analyzes the probability of being attacked on each pair of VMs and then regards it as independent evidence using Dempster-Shafer theory, and eventually combines the evidence among all pairs of VMs using the method of evidence fusion. Unlike the traditional IDS that focus on analyzing the entire network service externally, our proposed algorithm makes full use of the internal interactions between VMs, and the experiment proved that it can provide more accurate results than the traditional algorithm.     

基于三角模糊数的测试性分配方法

在考虑多因素影响的测试性分配中,需要专家通过两两比较来确定诸因素的相对重要性．然而由于人们的判断具有模糊性和不确定性,故在构造比较判断矩阵时所给出的判断往往不是确定的数值．针对这一问题,本文提出基于三角模糊数的测试性分配方法．该方法用三角模糊数表示专家评判数值范围,考虑人判断的模糊性,将无弹性的硬指标转化成模糊的软指标,建立基于三角模糊数的结构模型,同时结合层次分析（APH）方法进行指标权重的确定,由得到的权重进行测试性指标的分配．最后结合实例对本方法进行应用研究,应用结果验证了此方法的可行性．     

面向积雨云检测的模糊支持向量机

为解决SVM在积雨云检测中的难题,本文构造了一种模糊支持向量机(FSVM),首先根据训练样本的分布特性,定义了相邻样本距离类中心的距离变化率,然后通过计算距离变化率来剔除训练集中可能的噪声与野值样本,从而有效克服了传统基于紧密度的FSVM在计算最小超球半径时易受噪声与野值干扰的缺点,使得所计算的隶属度能更好地反映不同样本的差异。实验结果表明,对于FY2D卫星云图,采用从不同通道所提取的光谱特征,本文方法的积雨云检测准确率与传统SVM和基于紧密度的FSVM相比,分别平均提高2%和1%,且具有更强的适应性及噪声鲁棒性。     

Cloud Security Service for Identifying Unauthorized User Behaviour

Recently, an innovative trend like cloud computing has progressed quickly in Information Technology. For a background of distributed networks, the extensive sprawl of internet resources on the Web and the increasing number of service providers helped cloud computing technologies grow into a substantial scaled Information Technology service model. The cloud computing environment extracts the execution details of services and systems from end-users and developers. Additionally, through the system’s virtualization accomplished using resource pooling, cloud computing resources become more accessible. The attempt to design and develop a solution that assures reliable and protected authentication and authorization service in such cloud environments is described in this paper. With the help of multi-agents, we attempt to represent Open-Identity (ID) design to find a solution that would offer trustworthy and secured authentication and authorization services to software services based on the cloud. This research aims to determine how authentication and authorization services were provided in an agreeable and preventive manner. Based on attack-oriented threat model security, the evaluation works. By considering security for both authentication and authorization systems, possible security threats are analyzed by the proposed security systems.     

News Text Topic Clustering Optimized Method Based on TF-IDF Algorithm on Spark

Due to the slow processing speed of text topic clustering in stand-alone architecture under the background of big data, this paper takes news text as the research object and proposes LDA text topic clustering algorithm based on Spark big data platform. Since the TF-IDF (term frequency-inverse document frequency) algorithm under Spark is irreversible to word mapping, the mapped words indexes cannot be traced back to the original words. In this paper, an optimized method is proposed that TF-IDF under Spark to ensure the text words can be restored. Firstly, the text feature is extracted by the TF-IDF algorithm combined CountVectorizer proposed in this paper, and then the features are inputted to the LDA (Latent Dirichlet Allocation) topic model for training. Finally, the text topic clustering is obtained. Experimental results show that for large data samples, the processing speed of LDA topic model clustering has been improved based Spark. At the same time, compared with the LDA topic model based on word frequency input, the model proposed in this paper has a reduction of perplexity.     

A Distributed Intrusion Detection Model via Nondestructive Partitioning and Balanced Allocation for Big Data

There are two key issues in distributed intrusion detection system, that is, maintaining load balance of system and protecting data integrity. To address these issues, this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced allocation. A data allocation strategy based on capacity and workload is introduced to achieve local load balance, and a dynamic load adjustment strategy is adopted to maintain global load balance of cluster. Moreover, data integrity is protected by using session reassemble and session partitioning. The simulation results show that the new model enjoys favorable advantages such as good load balance, higher detection rate and detection efficiency.     

Optimal Resource Allocation and Quality of Service Prediction in Cloud

In the present scenario, cloud computing service provides on-request access to a collection of resources available in remote system that can be shared by numerous clients. Resources are in self-administration; consequently, clients can adjust their usage according to their requirements. Resource usage is estimated and clients can pay according to their utilization. In literature, the existing method describes the usage of various hardware assets. Quality of Service (QoS) needs to be considered for ascertaining the schedule and the access of resources. Adhering with the security arrangement, any additional code is forbidden to ensure the usage of resources complying with QoS. Thus, all monitoring must be done from the hypervisor. To overcome the issues, Robust Resource Allocation and Utilization (RRAU) approach is developed for optimizing the management of its cloud resources. The work hosts a numerous virtual assets which could be expected under the circumstances and it enforces a controlled degree of QoS. The asset assignment calculation is heuristic, which is based on experimental evaluations, RRAU approach with J48 prediction model reduces Job Completion Time (JCT) by 4.75 s, Make Span (MS) 6.25, and Monetary Cost (MC) 4.25 for 15, 25, 35 and 45 resources are compared to the conventional methodologies in cloud environment.     

A Secure Method for Data Storage and Transmission in Sustainable Cloud Computing

Cloud computing is a technology that provides secure storage space for the customer’s massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encryption and decryption algorithms are being deployed. In cloud computation, data processing, storage, and transmission can be done through laptops and mobile devices. Data Storing in cloud facilities is expanding each day and data is the most significant asset of clients. The important concern with the transmission of information to the cloud is security because there is no perceivability of the client’s data. They have to be dependent on cloud service providers for assurance of the platform’s security. Data security and privacy issues reduce the progression of cloud computing and add complexity. Nowadays; most of the data that is stored on cloud servers is in the form of images and photographs, which is a very confidential form of data that requires secured transmission. In this research work, a public key cryptosystem is being implemented to store, retrieve and transmit information in cloud computation through a modified Rivest-Shamir-Adleman (RSA) algorithm for the encryption and decryption of data. The implementation of a modified RSA algorithm results guaranteed the security of data in the cloud environment. To enhance the user data security level, a neural network is used for user authentication and recognition. Moreover; the proposed technique develops the performance of detection as a loss function of the bounding box. The Faster Region-Based Convolutional Neural Network (Faster R-CNN) gets trained on images to identify authorized users with an accuracy of 99.9% on training.     

Resource Allocation and Power Control Policy for Device-toDevice Communication Using Multi-Agent Reinforcement Learning

Device-to-Device (D2D) communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity. In this paper, we focus on the channel resource allocation and power control to improve the system resource utilization and network throughput. Firstly, we treat each D2D pair as an independent agent. Each agent makes decisions based on the local channel states information observed by itself. The multi-agent Reinforcement Learning (RL) algorithm is proposed for our multi-user system. We assume that the D2D pair do not possess any information on the availability and quality of the resource block to be selected, so the problem is modeled as a stochastic non-cooperative game. Hence, each agent becomes a player and they make decisions together to achieve global optimization. Thereby, the multi-agent Q-learning algorithm based on game theory is established. Secondly, in order to accelerate the convergence rate of multi-agent Q-learning, we consider a power allocation strategy based on Fuzzy Cmeans (FCM) algorithm. The strategy firstly groups the D2D users by FCM, and treats each group as an agent, and then performs multi-agent Q-learning algorithm to determine the power for each group of D2D users. The simulation results show that the Q-learning algorithm based on multi-agent can improve the throughput of the system. In particular, FCM can greatly speed up the convergence of the multi-agent Q-learning algorithm while improving system throughput.     

Enhance Intrusion Detection in Computer Networks Based on Deep Extreme Learning Machine

Networks provide a significant function in everyday life, and cybersecurity therefore developed a critical field of study. The Intrusion detection system (IDS) becoming an essential information protection strategy that tracks the situation of the software and hardware operating on the network. Notwithstanding advancements of growth, current intrusion detection systems also experience dif- ficulties in enhancing detection precision, growing false alarm levels and identifying suspicious activities. In order to address above mentioned issues, several researchers concentrated on designing intrusion detection systems that rely on machine learning approaches. Machine learning models will accurately identify the underlying variations among regular information and irregular information with incredible efficiency. Artificial intelligence, particularly machine learning methods can be used to develop an intelligent intrusion detection framework. There in this article in order to achieve this objective, we propose an intrusion detection system focused on a Deep extreme learning machine (DELM) which first establishes the assessment of safety features that lead to their prominence and then constructs an adaptive intrusion detection system focusing on the important features. In the moment, we researched the viability of our suggested DELMbased intrusion detection system by conducting dataset assessments and evaluating the performance factors to validate the system reliability. The experimental results illustrate that the suggested framework outclasses traditional algorithms. In fact, the suggested framework is not only of interest to scientific research but also of functional importance.     

A Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing

In mobile edge computing (MEC), one of the important challenges is how much resources of which mobile edge server (MES) should be allocated to which user equipment (UE). The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only. This paper presents a novel comprehensive utility function for resource allocation in MEC. The utility function considers the heterogeneous nature of applications that a UE offloads to MES. The proposed utility function considers all important parameters, including CPU, RAM, hard disk space, required time, and distance, to calculate a more realistic utility value for MESs. Moreover, we improve upon some general algorithms, used for resource allocation in MEC and cloud computing, by considering our proposed utility function. We name the improved versions of these resource allocation schemes as comprehensive resource allocation schemes. The UE requests are modeled to represent the amount of resources requested by the UE as well as the time for which the UE has requested these resources. The utility function depends upon the UE requests and the distance between UEs and MES, and serves as a realistic means of comparison between different types of UE requests. Choosing (or selecting) an optimal MES with the optimal amount of resources to be allocated to each UE request is a challenging task. We show that MES resource allocation is sub-optimal if CPU is the only resource considered. By taking into account the other resources, i.e., RAM, disk space, request time, and distance in the utility function, we demonstrate improvement in the resource allocation algorithms in terms of service rate, utility, and MES energy consumption.     

Personality Detection Using Context Based Emotions in Cognitive Agents

Detection of personality using emotions is a research domain in artificial intelligence. At present, some agents can keep the human’s profile for interaction and adapts themselves according to their preferences. However, the effective method for interaction is to detect the person’s personality by understanding the emotions and context of the subject. The idea behind adding personality in cognitive agents begins an attempt to maximize adaptability on the basis of behavior. In our daily life, humans socially interact with each other by analyzing the emotions and context of interaction from audio or visual input. This paper presents a conceptual personality model in cognitive agents that can determine personality and behavior based on some text input, using the context subjectivity of the given data and emotions obtained from a particular situation/context. The proposed work consists of Jumbo Chatbot, which can chat with humans. In this social interaction, the chatbot predicts human personality by understanding the emotions and context of interactive humans. Currently, the Jumbo chatbot is using the BFI technique to interact with a human. The accuracy of proposed work varies and improve through getting more experiences of interaction.     

HVAC系统的模糊预测函数控制器设计

针对暖通空调HVAC系统中由于存在高度非线性、时变特征以及扰动和不确定性等因素而难以控制的特点，提出基于Takagi-Sugeno(T-S)模糊模型的预测函数控制器设计方法。该方法通过最小二乘辨识算法建立系统的模糊T-S模型，然后基于模糊全局线性化预测模型，采用预测函数控制算法设计系统控制律。仿真实验结果表明该算法是一种跟踪性能好、鲁棒性强的有效控制方法。与常规的PID控制器相比，该方法具有超调量小、调整时间短等优良的动态性能。     

基于模糊销售价格的供应链协调机制研究

研究由一个制造商和一个零售商组成的两级供应链系统,面对产品销售价格浮动时的供应链协调问题.将产品销售价格看作三角模糊数,分析其在分散决策和集中决策下的基准点、浮动范围以及各方的模糊收益估计值.最后对两种决策下各参数值进行比较.     

A Fuzzy Mathematics Based Fault Auto-diagnosis System for Vacuum Resin Shot Dosing Equipment

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Predictive Control Algorithm for Urban Rail Train Brake Control System Based on T-S Fuzzy Model

Urban rail transit has the advantages of large traffic capacity, high punctuality and zero congestion, and it plays an increasingly important role in modern urban life. Braking system is an important system of urban rail train, which directly affects the performance and safety of train operation and impacts passenger comfort. The braking performance of urban rail trains is directly related to the improvement of train speed and transportation capacity. Also, urban rail transit has the characteristics of high speed, short station distance, frequent starting, and frequent braking. This makes the braking control system constitute a time-varying, time-delaying and nonlinear control system, especially the braking force changes directly disturb the parking accuracy and comfort. To solve these issues, a predictive control algorithm based on T-S fuzzy model was proposed and applied to the train braking control system. Compared with the traditional PID control algorithm and self-adaptive fuzzy PID control algorithm, the braking capacity of urban rail train was improved by 8%. The algorithm can achieve fast and accurate synchronous braking, thereby overcoming the dynamic influence of the uncertainty, hysteresis and time-varying factors of the controlled object. Finally, the desired control objectives can be achieved, the system will have superior robustness, stability and comfort.     

运动背景中结合特征位移矢量场模糊分割与 OTSU法的运动检测

运动背景中的运动检测难度较大,背景运动补偿后差分以及分割光流场可实现动目标和背景的分离,差分前需进行鲁棒的背景估计,且差分后易出现空洞,而光流估计在噪声以及目标运动速度较大时并不准确,尤其在光照变化时,两种方法均易失效。本文提出一种特征点位移矢量场模糊分割与图像自适应阈值化相结合的运动检测方法,实现在无任何关于运动目标或者运动背景先验信息条件下的动目标检测。通过改进的 SIFT匹配方法生成鲁棒的特征位移矢量场,采用模糊 C均值聚类算法对 SIFT位移矢量场进行无监督分类,实现动目标与背景特征的自适应分离。 OTSU法和形态学操作实现图像的自适应分割,用以修正特征点凸包,最终分割出动目标区域。与鲁棒的背景运动补偿后差分以及光流估计的对比实验表明,在目标运动速度较大、光照变化以及噪声情况下,本文方法均能够检测出运动目标,且在光照变化下的优势明显。