Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.

DDoS Attack Detection in Cloud Computing Based on Ensemble Feature Selection and Deep Learning

Intrusion Detection System(IDS)in the cloud Computing(CC)environment has received paramount interest over the last few years.Among the latest approaches,Deep Learning(DL)-based IDS methods allow the discovery of attacks with the highest performance.In the CC environment,Distributed Denial of Service(DDoS)attacks are widespread.The cloud services will be rendered unavailable to legitimate end-users as a consequence of the overwhelming network traffic,resulting in financial losses.Although various researchers have proposed many detection techniques,there are possible obstacles in terms of detection performance due to the use of insignificant traffic features.Therefore,in this paper,a hybrid deep learning mode based on hybridizing Convolutional Neural Network(CNN)with Long-Short-Term Memory(LSTM)is used due to its robustness and efficiency in detecting normal and attack traffic.Besides,the ensemble feature selection,mutualization aggregation between Particle Swarm Optimizer(PSO),Grey Wolf Optimizer(PSO),Krill Hird(KH),andWhale Optimization Algorithm(WOA),is used to select the most important features that would influence the detection performance in detecting DDoS attack in CC.A benchmark dataset proposed by the Canadian Institute of Cybersecurity(CIC),called CICIDS 2017 is used to evaluate the proposed IDS.The results revealed that the proposed IDS outperforms the state-of-the-art IDSs,as it achieved 97.9%,98.3%,97.9%,98.1%,respectively.As a result,the proposed IDS achieves the requirements of getting high security,automatic,efficient,and self-decision detection of DDoS attacks.

A Cloud Computing Perspective for Distributed Routing in Vehicular Environments

Vehicular networks have been envisioned to provide us with numerous interesting services such as dissemination of real-time safety warnings and commercial advertisements via car-to-car communication. However, efficient routing is a research challenge due to the highly dynamic nature of these networks. Nevertheless, the availability of connections imposes additional constraint. Our earlier works in the area of efficient dissemination integrates the advantages of middleware operations with muhicast routing to de- sign a framework for distributed routing in vehicular networks. Cloud computing makes use of pools of physical computing resourc- es to meet the requirements of such highly dynamic networks. The proposed solution in this paper applies the principles of cloud computing to our existing framework. The routing protocol works at the network layer for the formation of clouds in specific geo- graphic regions. Simulation results present the effieiency of the model in terms of serviee discovery, download time and the queu- ing delay at the controller nodes.

The Benefits of Using Google Cloud Computing for Developing Distributed Applications

IT as a dynamic filed changes very rapidly; efficient management of such systems for the most of the companies requires handling tremendous complex situations in terms of hardware and software setup. Hardware and software itself changes quickly with the time and keeping them updated is a difficult problem for the most of the companies; the problem is more emphasized for the companies having large infrastructure of IT facilities such as data centers which are expensive to be maintained. Many applications run on the company premises which require well prepared staff for successfully maintaining them. With the inception of Cloud Computing many companies have transferred their applications and data into cloud computing based platforms in order to have reduced maintaining cost, easier maintenance in terms of hardware and software, reliable and securely accessible services. The benefits of building distributed applications using Google infrastructure are conferred in this paper.

Video-based Person Re-identification Based on Distributed Cloud Computing

Person re-identification has been a hot research issues in the field of computer vision.In recent years,with the maturity of the theory,a large number of excellent methods have been proposed.However,large-scale data sets and huge networks make training a time-consuming process.At the same time,the parameters and their values generated during the training process also take up a lot of computer resources.Therefore,we apply distributed cloud computing method to perform person re-identification task.Using distributed data storage method,pedestrian data sets and parameters are stored in cloud nodes.To speed up operational efficiency and increase fault tolerance,we add data redundancy mechanism to copy and store data blocks to different nodes,and we propose a hash loop optimization algorithm to optimize the data distribution process.Moreover,we assign different layers of the re-identification network to different nodes to complete the training in the way of model parallelism.By comparing and analyzing the accuracy and operation speed of the distributed model on the video-based dataset MARS,the results show that our distributed model has a faster training speed.

Toward Cloud Computing QoS Architecture:Analysis of Cloud Systems and Cloud Services

Cloud can be defined as a new computing paradigm that provides scalable, on-demand, and virtualized resources for users. In this style of computing, users can access a shared pool of computing resources which are provisioned with minimal management efforts of users. Yet there are some obstacles and concerns about the use of clouds. Guaranteeing quality of service QoS by service providers can be regarded as one of the main concerns for companies tending to use it. Service provisioning in clouds is based on service level agreements representing a contract negotiated between users and providers. According to this contract, if a provider cannot satisfy its agreed application requirements, it should pay penalties as compensation. In this paper, we intend to carry out a comprehensive survey on the models proposed in literature with respect to the implementation principles to address the QoS guarantee issue. © 2014 Chinese Association of Automation.

Intelligent Ironmaking Optimization Service on a Cloud Computing Platform by Digital Twin

The shortage of computation methods and storage devices has largely limited the development of multiobjective optimization in industrial processes.To improve the operational levels of the process industries,we propose a multi-objective optimization framework based on cloud services and a cloud distribution system.Real-time data from manufacturing procedures are first temporarily stored in a local database,and then transferred to the relational database in the cloud.Next,a distribution system with elastic compute power is set up for the optimization framework.Finally,a multi-objective optimization model based on deep learning and an evolutionary algorithm is proposed to optimize several conflicting goals of the blast furnace ironmaking process.With the application of this optimization service in a cloud factory,iron production was found to increase by 83.91 t∙d^(-1),the coke ratio decreased 13.50 kg∙t^(-1),and the silicon content decreased by an average of 0.047%.

An Effective Non-Commutative Encryption Approach with Optimized Genetic Algorithm for Ensuring Data Protection in Cloud Computing

Nowadays,succeeding safe communication and protection-sensitive data from unauthorized access above public networks are the main worries in cloud servers.Hence,to secure both data and keys ensuring secured data storage and access,our proposed work designs a Novel Quantum Key Distribution(QKD)relying upon a non-commutative encryption framework.It makes use of a Novel Quantum Key Distribution approach,which guarantees high level secured data transmission.Along with this,a shared secret is generated using Diffie Hellman(DH)to certify secured key generation at reduced time complexity.Moreover,a non-commutative approach is used,which effectively allows the users to store and access the encrypted data into the cloud server.Also,to prevent data loss or corruption caused by the insiders in the cloud,Optimized Genetic Algorithm(OGA)is utilized,which effectively recovers the data and retrieve it if the missed data without loss.It is then followed with the decryption process as if requested by the user.Thus our proposed framework ensures authentication and paves way for secure data access,with enhanced performance and reduced complexities experienced with the prior works.

Adaptive Butterfly Optimization Algorithm(ABOA)Based Feature Selection and Deep Neural Network(DNN)for Detection of Distributed Denial-of-Service(DDoS)Attacks in Cloud

Cloud computing technology provides flexible,on-demand,and completely controlled computing resources and services are highly desirable.Despite this,with its distributed and dynamic nature and shortcomings in virtualization deployment,the cloud environment is exposed to a wide variety of cyber-attacks and security difficulties.The Intrusion Detection System(IDS)is a specialized security tool that network professionals use for the safety and security of the networks against attacks launched from various sources.DDoS attacks are becoming more frequent and powerful,and their attack pathways are continually changing,which requiring the development of new detection methods.Here the purpose of the study is to improve detection accuracy.Feature Selection(FS)is critical.At the same time,the IDS’s computational problem is limited by focusing on the most relevant elements,and its performance and accuracy increase.In this research work,the suggested Adaptive butterfly optimization algorithm(ABOA)framework is used to assess the effectiveness of a reduced feature subset during the feature selection phase,that was motivated by this motive Candidates.Accurate classification is not compromised by using an ABOA technique.The design of Deep Neural Networks(DNN)has simplified the categorization of network traffic into normal and DDoS threat traffic.DNN’s parameters can be finetuned to detect DDoS attacks better using specially built algorithms.Reduced reconstruction error,no exploding or vanishing gradients,and reduced network are all benefits of the changes outlined in this paper.When it comes to performance criteria like accuracy,precision,recall,and F1-Score are the performance measures that show the suggested architecture outperforms the other existing approaches.Hence the proposed ABOA+DNN is an excellent method for obtaining accurate predictions,with an improved accuracy rate of 99.05%compared to other existing approaches.

Survey on Three Components of Mobile Cloud Computing: Offloading, Distribution and Privacy

Mobile Cloud Computing (MCC) brings rich computational resource to mobile users, network operators, and cloud computing providers. It can be represented in many ways, and the ultimate goal of MCC is to enable execution of rich mobile application with rich user experience. Mobility is one of the main characteristics of MCC environment where user can be able to continue their work regardless of movement. This literature review paper presents the state-of-the-art survey of MCC. Also, we provide the communication architecture of MCC and taxonomy of mobile cloud in which specifically concentrates on offloading, mobile distribution computing, and privacy. Through an extensive literature review, we found that MCC is a technologically beneficial and expedient paradigm for virtual environments in terms of virtual servers in a distributed environment, multi-tenant architecture and data storing in a cloud. We further identified the drawbacks in offloading, mobile distribution computing, privacy of MCC and how this technology can be used in an effective way.

Residential Electricity Classification Method Based On Cloud Computing Platform and Random Forest

With the rapid development and popularization of new-generation technologies such as cloud computing,big data,and artificial intelligence,the construction of smart grids has become more diversified.Accurate quick reading and classification of the electricity consumption of residential users can provide a more in-depth perception of the actual power consumption of residents,which is essential to ensure the normal operation of the power system,energy management and planning.Based on the distributed architecture of cloud computing,this paper designs an improved random forest residential electricity classification method.It uses the unique out-of-bag error of random forest and combines the Drosophila algorithm to optimize the internal parameters of the random forest,thereby improving the performance of the random forest algorithm.This method uses MapReduce to train an improved random forest model on the cloud computing platform,and then uses the trained model to analyze the residential electricity consumption data set,divides all residents into 5 categories,and verifies the effectiveness of the model through experiments and feasibility.

Trusted Heartbeat Framework for Cloud Computing

In cloud computing environment, as the infrastructure not owned by users, it is desirable that its security and integrity must be protected and verified time to time. In Hadoop based scalable computing setup, malfunctioning nodes generate wrong output during the run time. To detect such nodes, we create collaborative network between worker node (i.e. data node of Hadoop) and Master node (i.e. name node of Hadoop) with the help of trusted heartbeat framework (THF). We propose procedures to register node and to alter status of node based on reputation provided by other co-worker nodes.

基于Hadoop的海量工程数据处理技术研究

随着信息技术的快速发展,工程领域中产生的数据量也在爆炸性增长。这些海量数据的有效处理和分析对工程决策、优化和创新至关重要。作为一种开源的分布式存储和计算框架,Hadoop以其高可靠性、高扩展性和高效性,成为处理大规模数据的首选技术。文中探讨了基于Hadoop的海量工程数据处理技术,分析了其在工程数据处理中的应用优势和面临的挑战,并提出了相应的优化策略,揭示了Hadoop在处理工程数据中的潜力和局限,为工程领域的数据处理提供了新的视角和方法。

Interpurchase Time of Customer Behavior in Cloud Computing

Cloud computing is an increasingly popular paradigm for accessing computing resources. For marketing application, this paper proposes a dynamic model of customer interpurchase time with geometric distribution. This model considers that there is a change point in interpurchase time and two types of probability density functions are demonstrated （time decreasing before changing; time increasing after changing）. With the description of change point, Bernoulli and Poisson distributions also are discussed in the model construction.

Systematic Cloud-Based Optimization: Twin-Fold Moth Flame Algorithm for VM Deployment and Load-Balancing

Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and applications.Nevertheless,existing commercial cloud computing models demonstrate an appropriate design by concentrating computational assets,such as preservation and server infrastructure,in a limited number of large-scale worldwide data facilities.Optimizing the deployment of virtual machines(VMs)is crucial in this scenario to ensure system dependability,performance,and minimal latency.A significant barrier in the present scenario is the load distribution,particularly when striving for improved energy consumption in a hypothetical grid computing framework.This design employs load-balancing techniques to allocate different user workloads across several virtual machines.To address this challenge,we propose using the twin-fold moth flame technique,which serves as a very effective optimization technique.Developers intentionally designed the twin-fold moth flame method to consider various restrictions,including energy efficiency,lifespan analysis,and resource expenditures.It provides a thorough approach to evaluating total costs in the cloud computing environment.When assessing the efficacy of our suggested strategy,the study will analyze significant metrics such as energy efficiency,lifespan analysis,and resource expenditures.This investigation aims to enhance cloud computing techniques by developing a new optimization algorithm that considers multiple factors for effective virtual machine placement and load balancing.The proposed work demonstrates notable improvements of 12.15%,10.68%,8.70%,13.29%,18.46%,and 33.39%for 40 count data of nodes using the artificial bee colony-bat algorithm,ant colony optimization,crow search algorithm,krill herd,whale optimization genetic algorithm,and improved Lévy-based whale optimization algorithm,respectively.

Cloud Computing Technology and Its Applications

Virtualization and distributed parallel architecture are typical cloud computing technologies. In the area of virtuatization technology, this article discusses physical resource pooling, resource pool management and use, cluster fault location and maintenance, resource pool grouping, and construction and application of heterogeneous virtualization platforms. In the area of distributed technology, distributed file system and KeyNalue storage engine are discussed. A solution is proposed for the host bottleneck problem, and a standard storage interface is proposed for the distributed file system. A directory-based storage scheme for Key/Value storage engine is also proposed.

QKD in Cloud-Fog Computing for Personal Health Record

Cloud computing is a rapid growing technology which delivers computing services such as servers,storage,database,networking,software and analytics.It has brought a new way to securely store and share information and data with multiple users.When authorized person access these clouds,the released data should not compromise any individual’s privacy and identity should not be revealed.Fog Computing is the extension of cloud with decentralized structure which stores the data in locations somewhere between the data source and cloud.The goal of fog computing is to provide high security,improve performance and network efficiency.We use quantum key distribution to produce and distribute key which change its quantum state and key,when key is known by mediator and it has ability to detect presence of mediator trying to gain lore of the key.In this paper,we introduced sugar-salt encryption which overcomes brute-force attack in effect delivers phony data in return to every incorrect guess of the password or key.

Resource Load Prediction of Internet of Vehicles Mobile Cloud Computing

Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.

The Objective Function Value Optimization of Cloud Computing Resources Security Allocation of Artificial Firefly Algorithm

Based on the current cloud computing resources security distribution model’s problem that the optimization effect is not high and the convergence is not good, this paper puts forward a cloud computing resources security distribution model based on improved artificial firefly algorithm. First of all, according to characteristics of the artificial fireflies swarm algorithm and the complex method, it incorporates the ideas of complex method into the artificial firefly algorithm, uses the complex method to guide the search of artificial fireflies in population, and then introduces local search operator in the firefly mobile mechanism, in order to improve the searching efficiency and convergence precision of algorithm. Simulation results show that, the cloud computing resources security distribution model based on improved artificial firefly algorithm proposed in this paper has good convergence effect and optimum efficiency.