Search

There can be many inherent issues in the process of managing cloud infrastructure and the platform of the cloud. The platform of the cloud manages cloud software and legality issues in making contracts. The platform also handles the process of managing cloud software services and legal contract-based segmentation. In this paper, we tackle these issues directly with some feasible solutions. For these constraints, the Averaged One-Dependence Estimators (AODE) classifier and the SELECT Applicable Only to Parallel Server (SELECT-APSL ASA) method are proposed to separate the data related to the place. ASA is made up of the AODE and SELECT Applicable Only to Parallel Server. The AODE classifier is used to separate the data from smart city data based on the hybrid data obfuscation technique. The data from the hybrid data obfuscation technique manages 50% of the raw data, and 50% of hospital data is masked using the proposed transmission. The analysis of energy consumption before the cryptosystem shows the total packet delivered by about 71.66% compared with existing algorithms. The analysis of energy consumption after cryptosystem assumption shows 47.34% consumption, compared to existing state-of-the-art algorithms. The average energy consumption before data obfuscation decreased by 2.47%, and the average energy consumption after data obfuscation was reduced by 9.90%. The analysis of the makespan time before data obfuscation decreased by 33.71%. Compared to existing state-of-the-art algorithms, the study of makespan time after data obfuscation decreased by 1.3%. These impressive results show the strength of our methodology.

Cloud computing security has been a critical issue with its increase in demand. One of the most challenging problems in cloud computing is detecting distributed denial-of-service (DDoS) attacks. The attack detection framework for the DDoS attack is tricky because of its nonlinear nature of interruption activities, atypical system traffic behaviour, and many features in the problem space. As a result, creating defensive solutions against these attacks is critical for mainstream cloud computing adoption. In this novel research, by using performance parameters, perplexed-based classifiers with and without feature selection will be compared with the existing machine learning algorithms such as naïve Bayes and random forest to prove the efficacy of the perplexed-based classification algorithm. Comparing the performance parameters like accuracy, sensitivity, and specificity, the proposed algorithm has an accuracy of 99%, which is higher than the existing algorithms, proving that the proposed algorithm is highly efficient in detecting the DDoS attacks in cloud computing systems. To extend our research in the area of nature-inspired computing, we compared our perplexed Bayes classifier feature selection with nature-inspired feature selection like genetic algorithm (GA) and particle swarm optimization (PSO) and found that our classifier is highly efficient in comparison with GA and PSO and their accuracies are 2% and 8%, respectively, less than those of perplexed Bayes classifier., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2022 Narendra Mishra et al.)

For 5G and future Internet, in this paper, we propose a task allocation method for future Internet application to reduce the total latency in a mobile edge computing (MEC) platform with three types of servers: a dedicated MEC server, a shared MEC server, and a cloud server. For this platform, we first calculate the delay between sending a task and receiving a response for the dedicated MEC server, shared MEC server, and cloud server by considering the processing time and transmission delay. Here, the transmission delay for the shared MEC server is derived using queueing theory. Then, we formulate an optimization problem for task allocation to minimize the total latency for all tasks. By solving this optimization problem, tasks can be allocated to the MEC servers and cloud server appropriately. In addition, we propose a heuristic algorithm to obtain the approximate optimal solution in a shorter time. This heuristic algorithm consists of four algorithms: a main algorithm and three additional algorithms. In this algorithm, tasks are divided into two groups, and task allocation is executed for each group. We compare the performance of our proposed heuristic algorithm with the solution obtained by three other methods and investigate the effectiveness of our algorithm. Numerical examples are used to demonstrate the effectiveness of our proposed heuristic algorithm. From some results, we observe that our proposed heuristic algorithm can perform task allocation in a short time and can effectively reduce the total latency in a short time. We conclude that our proposed heuristic algorithm is effective for task allocation in a MEC platform with multiple types of MEC servers.

One of the most effective approaches to improve resource usage efficiency and degree of resource collecting is to integrate resources. Many studies on the integration of information resources are also available. The search engines are the most well-known. At the same time, this article intends to optimize the integration of British and American literature information resources by employing distributed cloud computing, based on the needs of British and American literature. This research develops a model for the dispersed nature of cloud computing. It optimizes the method by fitting the mathematical model of transmission cost and latency. This article analyzes the weaknesses of the current British and American literature information resource integration and optimizes them for the integration of British and American literature resources. The Random algorithm has the longest delay, according to the results of this paper's experiments (maximum user weighted distance). The algorithms NPA-PDP and BWF have longer delays than the algorithm Opt. The percentage decline varies between 0.17 percent and 1.11 percent for different algorithms. It demonstrates that the algorithm presented in this work can be used to integrate and maximize information resources from English and American literature., Competing Interests: The author does not have any possible conflicts of interest., (Copyright © 2022 Mei Chen.)

With the rapid development of mobile technology, mobile applications have increasing requirements for computational resources, and mobile devices can no longer meet these requirements. Mobile edge computing (MEC) has emerged in this context and has brought innovation into the working mode of traditional cloud computing. By provisioning edge server placement, the computing power of the cloud center is distributed to the edge of the network. The abundant computational resources of edge servers compensate for the lack of mobile devices and shorten the communication delay between servers and users. Constituting a specific form of edge servers, cloudlets have been widely studied within academia and industry in recent years. However, existing studies have mainly focused on computation offloading for general computing tasks under fixed cloudlet placement positions. They ignored the impact on computation offloading results from cloudlet placement positions and data dependencies among mobile application components. In this paper, we study the cloudlet placement problem based on workflow applications (WAs) in wireless metropolitan area networks (WMANs). We devise a cloudlet placement strategy based on a particle swarm optimization algorithm using genetic algorithm operators with the encoding library updating mode (PGEL), which enables the cloudlet to be placed in appropriate positions. The simulation results show that the proposed strategy can obtain a near-optimal cloudlet placement scheme. Compared with other classic algorithms, this algorithm can reduce the execution time of WAs by 15.04-44.99%.

The networking scale and traffic have exploded. At the same time, the rapid development of virtualization and cloud computing technologies not only poses a considerable challenge to the endurance of the network, but also causes more and more problems to the traditional network architecture with IP as the core. Cloud computing is a supercomputing model based on the Internet. With the rapid growth of network access and data traffic, the processing power and computing intensity will also increase, and a single server cannot afford the increase in business. In order to reduce network pressure and improve computing efficiency, load balancing for network computing is particularly important. This paper uses ant colony algorithm to design cloud computing load balance. The ant colony algorithm runs in the controller. According to the real-time network load situation provided by the controller, it calculates the link with the smallest load and provides a dynamic data stream forwarding strategy. The result of the experiments shows that the load-balanced ACO optimized technique can significantly provide an improved computational response. In the ACO algorithm, the average response time is about 30% lower than that in other algorithms. This shows that the use of the ant colony algorithm achieves a good optimization effect., Competing Interests: The author declares no conflicts of interest., (Copyright © 2022 Jing He.)

This paper deals with the challenging problem of scheduling users' tasks, while taking into consideration users' quality of service (QoS) requirements, with the objective of reducing the energy consumption of physical machines. This paper presents a model to analyze the current state of the running tasks according to the results of the QoS prediction assigned by an ARIMA prediction model optimized with Kalman filter. Then, we calculate a scheduling policy with a combined particle swarm optimization (PSO) and gravitational search algorithm (GSA) algorithms according to the QoS status analysis. Experimental results show that the proposed HPSO algorithm reduces resources consumption 16.51% more than the original hybrid algorithm, and the violation of service-level agreement (SLA) is 0.053% less when the optimized prediction model is used.

With the continuous reform and innovation of Internet technology and the continuous development and progress of social economy, Big Data cloud computing technology is more and more widely used in people's work and life. Many parallel algorithms play a very important role in solving large linear equations in various applications. To this end, this article aims to propose and summarize a cloud computing task scheduling model that relies on the solution of large linear equations. The method of this paper is to study the technology of solving large-scale linear equations and propose an M-QoS-OCCSM scheduling model. The function of the experimental method is to solve the problem of efficiently executing N mutually dependent parallel tasks within limited resources, while fully satisfying users' expectations of task completion time, bandwidth rate, reliability, and cost. In this paper, the application experiment of large-scale linear equations in task scheduling is used to study task scheduling algorithms. The results show that when the task load is 10 and 20, the convergence speed of the MPQGA algorithm is 32 seconds and 95 seconds faster than that of the BGA algorithm, respectively., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2022 Jing He.)

Cloud computing is an important milestone in the development of distributed computing as a commercial implementation, and it has good prospects. Infrastructure as a service (IaaS) is an important service mode in cloud computing. It combines massive resources scattered in different spaces into a unified resource pool by means of virtualization technology, facilitating the unified management and use of resources. In IaaS mode, all resources are provided in the form of virtual machines (VM). To achieve efficient resource utilization, reduce users' costs, and save users' computing time, VM allocation must be optimized. This paper proposes a new multiobjective optimization method of dynamic resource allocation for multivirtual machine distribution stability. Combining the current state and future predicted data of each application load, the cost of virtual machine relocation and the stability of new virtual machine placement state are considered comprehensively. A multiobjective optimization genetic algorithm (MOGANS) was designed to solve the problem. The simulation results show that compared with the genetic algorithm (GA-NN) for energy saving and multivirtual machine redistribution overhead, the virtual machine distribution method obtained by MOGANS has a longer stability time. Aiming at this shortage, this paper proposes a multiobjective optimization dynamic resource allocation method (MOGA-C) based on MOEA/D for virtual machine distribution. It is illustrated by experimental simulation that moGA-D can converge faster and obtain similar multiobjective optimization results at the same calculation scale., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Feng Shi and Jingna Lin.)

The fast growth in the amount of connected devices with computing capabilities in the past years has enabled the emergence of a new computing layer at the Edge. Despite being resource-constrained if compared with cloud servers, they offer lower latencies than those achievable by Cloud computing. The combination of both Cloud and Edge computing paradigms can provide a suitable infrastructure for complex applications' quality of service requirements that cannot easily be achieved with either of these paradigms alone. These requirements can be very different for each application, from achieving time sensitivity or assuring data privacy to storing and processing large amounts of data. Therefore, orchestrating these applications in the Cloud-Edge computing raises new challenges that need to be solved in order to fully take advantage of this layered infrastructure. This paper proposes an architecture that enables the dynamic orchestration of applications in the Cloud-Edge continuum. It focuses on the application's quality of service by providing the scheduler with input that is commonly used by modern scheduling algorithms. The architecture uses a distributed scheduling approach that can be customized in a per-application basis, which ensures that it can scale properly even in setups with high number of nodes and complex scheduling algorithms. This architecture has been implemented on top of Kubernetes and evaluated in order to asses its viability to enable more complex scheduling algorithms that take into account the quality of service of applications.

Cloud computing has been widely adopted over the years by practitioners and companies with a variety of requirements. With a strong economic appeal, cloud computing makes possible the idea of computing as a utility, in which computing resources can be consumed and paid for with the same convenience as electricity. One of the main characteristics of cloud as a service is elasticity supported by auto-scaling capabilities. The auto-scaling cloud mechanism allows adjusting resources to meet multiple demands dynamically. The elasticity service is best represented in critical web trading and transaction systems that must satisfy a certain service level agreement (SLA), such as maximum response time limits for different types of inbound requests. Nevertheless, existing cloud infrastructures maintained by different cloud enterprises often offer different cloud service costs for equivalent SLAs upon several factors. The factors might be contract types, VM types, auto-scaling configuration parameters, and incoming workload demand. Identifying a combination of parameters that results in SLA compliance directly in the system is often sophisticated, while the manual analysis is prone to errors due to the huge number of possibilities. This paper proposes the modeling of auto-scaling mechanisms in a typical cloud infrastructure using a stochastic Petri net (SPN) and the employment of a well-established adaptive search metaheuristic (GRASP) to discover critical trade-offs between performance and cost in cloud services.The proposed SPN models enable cloud designers to estimate the metrics of cloud services in accordance with each required SLA such as the best configuration, cost, system response time, and throughput.The auto-scaling SPN model was extensively validated with 95% confidence against a real test-bed scenario with 18.000 samples. A case-study of cloud services was used to investigate the viability of this method and to evaluate the adoptability of the proposed auto-scaling model in practice. On the other hand, the proposed optimization algorithm enables the identification of economic system configuration and parameterization to satisfy required SLA and budget constraints. The adoption of the metaheuristic GRASP approach and the modeling of auto-scaling mechanisms in this work can help search for the optimized-quality solution and operational management for cloud services in practice.

Cloud computing has emerged as the most favorable computing platform for researchers and industry. The load balanced task scheduling has emerged as an important and challenging research problem in the Cloud computing. Swarm intelligence-based meta-heuristic algorithms are considered more suitable for Cloud scheduling and load balancing. The optimization procedure of swarm intelligence-based meta-heuristics consists of two major components that are the local and global search. These algorithms find the best position through the local and global search. To achieve an optimized mapping strategy for tasks to the resources, a balance between local and global search plays an effective role. The inertia weight is an important control attribute to effectively adjust the local and global search process. There are many inertia weight strategies; however, the existing approaches still require fine-tuning to achieve optimum scheduling. The selection of a suitable inertia weight strategy is also an important factor. This paper contributed an adaptive Particle Swarm Optimisation (PSO) based task scheduling approach that reduces the task execution time, and increases throughput and Average Resource Utilization Ratio (ARUR). Moreover, an adaptive inertia weight strategy namely Linearly Descending and Adaptive Inertia Weight (LDAIW) is introduced. The proposed scheduling approach provides a better balance between local and global search leading to an optimized task scheduling. The performance of the proposed approach has been evaluated and compared against five renown PSO based inertia weight strategies concerning makespan and throughput. The experiments are then extended and compared the proposed approach against the other four renowned meta-heuristic scheduling approaches. Analysis of the simulated experimentation reveals that the proposed approach attained up to 10%, 12% and 60% improvement for makespan, throughput and ARUR respectively.

Containers have emerged as a more portable and efficient solution than virtual machines for cloud infrastructure providing both a flexible way to build and deploy applications. The quality of service, security, performance, energy consumption, among others, are essential aspects of their deployment, management, and orchestration. Inappropriate resource allocation can lead to resource contention, entailing reduced performance, poor energy efficiency, and other potentially damaging effects. In this paper, we present a set of online job allocation strategies to optimize quality of service, energy savings, and completion time, considering contention for shared on-chip resources. We consider the job allocation as the multilevel dynamic bin-packing problem that provides a lightweight runtime solution that minimizes contention and energy consumption while maximizing utilization. The proposed strategies are based on two and three levels of scheduling policies with container selection, capacity distribution, and contention-aware allocation. The energy model considers joint execution of applications of different types on shared resources generalized by the job concentration paradigm. We provide an experimental analysis of eighty-six scheduling heuristics with scientific workloads of memory and CPU-intensive jobs. The proposed techniques outperform classical solutions in terms of quality of service, energy savings, and completion time by 21.73-43.44%, 44.06-92.11%, and 16.38-24.17%, respectively, leading to a cost-efficient resource allocation for cloud infrastructures., Competing Interests: The authors have declared that no competing interests exist.

The electrical control system of rapier weaving machines is susceptible to various disturbances during operation and is prone to failures. This will seriously affect the production and a fault diagnosis system is needed to reduce this effect. However, the existing popular fault diagnosis systems and methods need to be improved due to the limitations of rapier weaving machine process and electrical characteristics. Based on this, this paper presents an in-depth study of rapier loom fault diagnosis system and proposes a rapier loom fault diagnosis method combining edge expert system and cloud-based rough set and Bayesian network. By analyzing the process and fault characteristics of rapier loom, the electrical faults of rapier loom are classified into common faults and other faults according to the frequency of occurrence. An expert system is built in the field for edge computing based on knowledge fault diagnosis experience to diagnose common loom faults and reduce the computing pressure in the cloud. Collect loom fault data in the cloud, train loom fault diagnosis algorithms to diagnose other faults, and handle other faults diagnosed by the expert system. The effectiveness of loom fault diagnosis is verified by on-site operation and remote monitoring of the loom human-machine interaction system. Technical examples are provided for the research of loom fault diagnosis system., Competing Interests: XY is the chairman and founder of Jiangsu Corey Intelligent Control Automation Technology Co. but does not receive a salary from them. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products associated with this research to declare.

Public key encryption with equality test enables the user to determine whether two ciphertexts contain the same information without decryption. Therefore, it may serve as promising cryptographic technique for cloud-assisted wireless sensor networks (CWSNs) to maintain data privacy. In this paper, an efficient RSA with equality test algorithm is proposed. The presented scheme also handles the attackers based on their authorization ability. Precisely, the proposed scheme is proved to be one-way against chosen-ciphertext attack security and indistinguishable against chosen ciphertext attacks. Moreover, the experimental evaluations depict that the underlying scheme is efficient in terms of encryption, decryption, and equality testing. Thus, this scheme may be used as a practical solution in context of CWSNs, where the users may compare two ciphertexts without decryption., Competing Interests: The authors have declared that no competing interests exist.

In this paper, an efficient normal estimation and filtering method for depth images acquired by Time-of-Flight (ToF) cameras is proposed. The method is based on a common feature pyramid networks (FPN) architecture. The normal estimation method is called ToFNest, and the filtering method ToFClean. Both of these low-level 3D point cloud processing methods start from the 2D depth images, projecting the measured data into the 3D space and computing a task-specific loss function. Despite the simplicity, the methods prove to be efficient in terms of robustness and runtime. In order to validate the methods, extensive evaluations on public and custom datasets were performed. Compared with the state-of-the-art methods, the ToFNest and ToFClean algorithms are faster by an order of magnitude without losing precision on public datasets.

The growing popularity of big data analysis and cloud computing has created new big data management standards. Sometimes, programmers may interact with a number of heterogeneous data stores depending on the information they are responsible for: SQL and NoSQL data stores. Interacting with heterogeneous data models via numerous APIs and query languages imposes challenging tasks on multi-data processing developers. Indeed, complex queries concerning homogenous data structures cannot currently be performed in a declarative manner when found in single data storage applications and therefore require additional development efforts. Many models were presented in order to address complex queries Via multistore applications. Some of these models implemented a complex unified and fast model, while others' efficiency is not good enough to solve this type of complex database queries. This paper provides an automated, fast and easy unified architecture to solve simple and complex SQL and NoSQL queries over heterogeneous data stores (CQNS). This proposed framework can be used in cloud environments or for any big data application to automatically help developers to manage basic and complicated database queries. CQNS consists of three layers: matching selector layer, processing layer, and query execution layer. The matching selector layer is the heart of this architecture in which five of the user queries are examined if they are matched with another five queries stored in a single engine stored in the architecture library. This is achieved through a proposed algorithm that directs the query to the right SQL or NoSQL database engine. Furthermore, CQNS deal with many NoSQL Databases like MongoDB, Cassandra, Riak, CouchDB, and NOE4J databases. This paper presents a spark framework that can handle both SQL and NoSQL Databases. Four scenarios' benchmarks datasets are used to evaluate the proposed CQNS for querying different NoSQL Databases in terms of optimization process performance and query execution time. The results show that, the CQNS achieves best latency and throughput in less time among the compared systems., Competing Interests: The authors have declared that no competing interests exist.

INTRODUCTION: In present days, Automation of everything has become essential. Internet of things (IoT) play an important role among all medical advances of IT. In this paper, feasible solutions are discussed to compare and design better healthcare systems. A thorough investigation and survey of suitable approaches were done to select IoT based systems in hospitals consisting of various high precision sensors. OBJECTIVES: The challenge healthcare system face is to manage the real time patient’s data with high accuracy. Second challenge is at fog devices level to manage the load distribution to all sensors with limited availability of bandwidth. METHODS: This paper summarizes the selection criterions of suitable load balancing algorithms to reduce energy consumption and computational cost of fog devices and increase the network usage that are supposed to be used in IoT based healthcare systems. According to the survey BNBKnapack algorithm has been selected as best suitable approach to analyze the overall performance of fog devices and results are also verify the same. RESULTS: Comparative analysis of Overall performance of fog devices has been proposed with using SJF algorithm and Adaptive BNBKnapsack algorithm. It has been observed by analysing system performance, which is found as best among other load balancing algorithm Adaptive BNBKnapsack is successfully reduce the energy consumption by (99.29%), computational cost by (98.34%) and increase the network usage by (99.95%) of system CONCLUSION: It has been observed by analysing system performance, Adaptive BNBKnapsack Load balancing is successfully able to reduce the computational cost and energy consumption also increase the network usage of the fog network. The performance of the system is found best among other load balancing algorithm. [ABSTRACT FROM AUTHOR]

The rapid development of mobile Internet has promoted the rapid rise of cloud computing technology. Mobile terminal devices have greatly expanded the service capacity of mobile terminals by migrating complex computing tasks to run in the cloud. However, in the process of data exchange between mobile terminals and cloud computing centers, on the one hand, it consumes the limited power of mobile terminals, and on the other hand, it results in longer communication time, which negatively affects user QoE. Mobile cloud can effectively improve user QoE by shortening the data transmission distance, reducing the power consumption, and shortening the communication time at the same time. In this paper, we utilize the property that genetic algorithm can perform global search seeking the global optimal solution and construct a dynamic task scheduling model by combining the device-cloud link. The task scheduling model based on genetic algorithm and random scheduling algorithm is compared through comparison experiments, which show that the assignment time of the task scheduling model based on genetic algorithm is shortened by 11.82% to 48.51% and the energy consumption is reduced by 22.28% to 47.52% under different load conditions. [ABSTRACT FROM AUTHOR]

After the service provider temporarily selects the required edge nodes based on social and storage capabilities, application execution causes the edge nodes to cache part of the application data. Therefore, offloading part of the application computing tasks to the selected edge nodes can effectively improve application execution performance. However, in cases where the resources of user's IoT devices are insufficient, tasks can be further offloaded to traditional edge servers or even to the cloud to maximize application execution efficiency. In this paper, the entire uninstall utility is modeled as a weighted sum of task completion time and energy consumption. Under the premise of considering users' preferences for completion time and energy consumption, a game-based uninstallation algorithm is proposed. The algorithm performs uninstallation by optimizing the uninstallation decision. Based on user preferences, the total system overhead is relatively small. The subsequent simulation experiments show that the algorithm can reduce system overhead on the basis of satisfying user preferences and has relatively good adaptability. [ABSTRACT FROM AUTHOR]

Most user authentication mechanisms of cloud systems depend on the credentials approach in which a user submits his/her identity through a username and password. Unfortunately, this approach has many security problems because personal data can be stolen or recognized by hackers. This paper aims to present a cloud-based biometric authentication model (CBioAM) for improving and securing cloud services. The research study presents the verification and identification processes of the proposed cloud-based biometric authentication system (CBioAS), where the biometric samples of users are saved in database servers and the authentication process is implemented without loss of the users' information. The paper presents the performance evaluation of the proposed model in terms of three main characteristics including accuracy, sensitivity, and specificity. The research study introduces a novel algorithm called "Bio_Authen_as_a_Service" for implementing and evaluating the proposed model. The proposed system performs the biometric authentication process securely and preserves the privacy of user information. The experimental result was highly promising for securing cloud services using the proposed model. The experiments showed encouraging results with a performance average of 93.94%, an accuracy average of 96.15%, a sensitivity average of 87.69%, and a specificity average of 97.99%. [ABSTRACT FROM AUTHOR]

The ratio at which the number of users is increasing in cloud computing, the same as the number of data destruction, has doubled. Various algorithms have been designed to secure cloud servers to prevent an attacker from attacking, but they cannot protect the data. With the help of cryptography, data are converted into a format that is difficult for an attacker to understand, and these data can be broadcasted in a reliable format. If the attacker obtains access to the cryptographic algorithm, then the attacker can easily decrypt the data, but if the data are encrypted with the help of the key, it is difficult for the attacker to decrypt the data without the key. When the data are encrypted with the same key at different times, there is a possibility to obtain the actual key with the help of the Kasiski test. Different researchers have encrypted the data with the help of the same key or have developed an algorithm by combining different algorithms, which is a problem. This paper developed an efficient algorithm that cannot be broken easily. Even if an attacker obtains access to the algorithm, the attacker cannot identify the keys derived from that text, and the key only works in the text from which it is generated, which is the novelty of this paper. Data are secured in two different layers. In Layer-1, the proposed algorithm implements plaintext and a static key to obtain the encrypted text and a Vigenère key. In Layer-2, the encrypted text and the Vigenère key are implemented on the Vigenère cipher algorithm to obtain the ciphertext. At the end of the paper, a comparison of different papers is made with the proposed paper, and a conclusion is made based on different techniques and results. [ABSTRACT FROM AUTHOR]

As is well known that the global optimization ability of the Fruit fly Optimization Algorithm (FOA)is weak because it is easy to fall into local optimum. In this paper, a Fruit Fly Optimization Algorithm based on Locality Sensitive Hashing-aware (LSHFOA)was proposed. The locality sensitive hashing mechanism to optimize the generation mechanism for swarm population individuals was used, which can improve the individual diversity of the population. Meanwhile, when the fruit fly population falls into the local optimum, the locality sensitive hashing mechanism was adopted to change the population location, which is used for jumping out of local optimal limits. To verify the performance of LSHFOA, it was compared with FOA and its improvement algorithms CFOA, and IFFO with 8 representative benchmark functions. A large number of experimental results showed that LSHFOA has a faster convergence speed and higher precision of optimization for function optimization, especially in high-dimensional multi-peak functions. In addition to the theoretical evaluation, we also evaluate its performance in a real-world scenario. Generally, an edge computing environment, as an extension of cloud computing, can allow the users to access the network in a low-latency manner. In this way, to capture the high-speed convergence advantage, this paper makes the first attempt to tackle a classic research problem in the edge computing environment, i.e., the edge server placement problem. The experimental results show that the new algorithm has an excellent application effect. [ABSTRACT FROM AUTHOR]

Cloud computing is considered a key Internet technology. Cloud providers offer services through the Internet, such as infrastructure, platforms, and software. The scheduling process of cloud providers' tasks concerns allocating clients' tasks to providers' resources. Several mechanisms have been developed for task scheduling in cloud computing. Still, these mechanisms need to be optimized for execution time and makespan. This paper presents a new task-scheduling mechanism based on Discrete Prey–Predator to optimize the task-scheduling process in the cloud environment. The proposed Discrete Prey–Predator mechanism assigns each scheduling solution survival values. The proposed mechanism denotes the prey's maximum surviving value and the predator's minimum surviving value. The proposed Discrete Prey–Predator mechanism aims to minimize the execution time of tasks in cloud computing. This paper makes a significant contribution to the field of cloud task scheduling by introducing a new mechanism based on the Discrete Prey–Predator algorithm. The Discrete Prey–Predator mechanism presents distinct advantages, including optimized task execution, as the mechanism is purpose-built to optimize task execution times in cloud computing, improving overall system efficiency and resource utilization. Moreover, the proposed mechanism introduces a survival-value-based approach, as the mechanism introduces a unique approach for assigning survival values to scheduling solutions, differentiating between the prey's maximum surviving value and the predator's minimum surviving value. This improvement enhances decision-making precision in task allocation. To evaluate the proposed mechanism, simulations using the CloudSim simulator were conducted. The experiment phase considered different scenarios for testing the proposed mechanism in different states. The simulation results revealed that the proposed Discrete Prey–Predator mechanism has shorter execution times than the firefly algorithm. The average of the five execution times of the Discrete Prey–Predator mechanism was 270.97 s, while the average of the five execution times of the firefly algorithm was 315.10 s. [ABSTRACT FROM AUTHOR]

With the application of cloud computing services in more and more fields, it will undertake more computing tasks and storage tasks. The problem of high energy consumption in data centers will become more serious. Virtualization technology is very important in cloud computing, which can improve the utilization rate of resources. At the same time, it has flexibility in resource scheduling and can integrate multiple virtual machines to achieve power efficiency. Using online virtual machine migration technology for energy-saving planning in cloud environment is a hot research topic in academic circles. The scheduling strategy proposed in this paper can reduce the server downtime and the number of server hosts, so as to achieve the maximum use of resources. The main research work of this paper includes the following aspects: Firstly, the energy consumption in the process of virtual machine energy-saving design is modeled, and the relationship between energy consumption and resource usage under different load conditions is analyzed, and the problems are abstracted, e.g., packaging box problem. Secondly, this paper uses genetic algorithm to solve the problem of high energy consumption. Finally, based on the target allocation scheme of virtual machines obtained by the above method, the migration problem of virtual machines is abstracted as the problem of finding the maximum weighted independent set of graphs. And a greedy algorithm is designed to solve this problem. In this paper, CloudSim simulation platform is used to verify the effectiveness of the proposed algorithm. Experiments show that the proposed algorithm can reduce data energy consumption and avoid frequent migration of virtual machines. [ABSTRACT FROM AUTHOR]

Cloud computing has seen a major boom during the past few years. Many people have switched to cloud computing because traditional systems require complex resource distribution and cloud solutions are less expensive. Load balancing (LB) is one of the essential challenges in cloud computing used to balance the workload of cloud services. This research paper presents a performance evaluation of the existing load-balancing algorithms which are particle swarm optimization (PSO), round robin (RR), equally spread current execution (ESCE), and throttled load balancing. This study offers a detailed performance evaluation of various load-balancing algorithms by employing a cloud analyst platform. Efficiency concerning various service broker policy configurations for load-balancing algorithms' virtual machine load balance was also calculated using metrics such as optimized response time (ORT), data center processing time (DCPT), virtual machine costs, data transfer costs, and total cost for different workloads and user bases. Many of the past papers that were mentioned in the literature worked on round robin and equally spread current execution, and throttled load-balancing algorithms were based on efficiency and response time in virtual machines without recognizing the relation between the task and the virtual machines, and the practical significance of the application. A comparison of specific load-balancing algorithms has been investigated. Different service broker policy (SBP) tests have been conducted to illustrate the load-balancing algorithm capabilities. [ABSTRACT FROM AUTHOR]

Cloud computing and the increasing popularity of 5G have greatly increased the application of images on Internet of Things (IoT) devices. The storage of images on an untrusted cloud has high security and privacy risks. Several lightweight cryptosystems have been proposed in the literature as appropriate for resource-constrained IoT devices. These existing lightweight cryptosystems are, however, not only at the risk of compromising the integrity and security of the data but also, due to the use of substitution boxes (S-boxes), require more memory space for their implementation. In this paper, a secure lightweight cryptography algorithm, that eliminates the use of an S-box, has been proposed. An algorithm termed Enc, that accepts a block of size n divides the block into L n R bits of equal length and outputs the encrypted block as follows: E = L ⨂ R ⨁ R , where ⨂ and ⨁ are exclusive-or and concatenation operators, respectively, was created. A hash result, h a s R = S H A 256 P ⨁ K , was obtained, where SHA256, P, and K are the Secure Hash Algorithm (SHA−256), the encryption key, and plain image, respectively. A seed, S, generated from e n c h a s h = E n c h a s h e n c , K , where hashenc is the first n bits of hasR, was used to generate a random image, Rim. An intermediate image, i n t i m a g e = R i m ⨂ P , and cipher image, C = E n c i n t i m a g e , K , were obtained. The proposed scheme was evaluated for encryption quality, decryption quality, system sensitivity, and statistical analyses using various security metrics. The results of the evaluation showed that the proposed scheme has excellent encryption and decryption qualities that are very sensitive to changes in both key and plain images, and resistance to various statistical attacks alongside other security attacks. Based on the result of the security evaluation of the proposed cryptosystem termed Hash XOR Permutation (HXP), the study concluded that the security of the cryptography algorithm can still be maintained without the use of a substitution box. [ABSTRACT FROM AUTHOR]

To address the challenge of distinguishing the health status of bearings, in this paper, a health index (HI) is developed through utilization of the multiple target time-varying black widow optimization–bidirectional gating recurrent unit (MTBWO-BiGRU) model and the Bray–Curtis distance. This index offers a visual representation of the health status of bearings, enabling more intuitive monitoring and prediction. The first step involves utilizing L1 regularization to extract effective features as degradation elements from the current bearing vibration data. Additionally, the characteristics of the initial time window of the vibration data serve as the health features. Next, the HI of the bearing is constructed by computing the Bray–Curtis distance between the bearing's degradation characteristics and health features. The cloud monitoring platform constantly tracks the health of the bearing and employs the MTBWO-BiGRU model to anticipate the forthcoming state of health. The platform generates an immediate alert when the HI of the bearing overtakes the alteration rate threshold and foresees the condition of the bearing. We compare the MTBWO-BiGRU model with the bidirectional long short-term memory (BiLSTM) and BiGRU models. The results indicate an accuracy level of 92.57%, which is evidently higher than that obtained when using the other two models. Moreover, the MTBWO-BiGRU model is lighter, demonstrating the practicality of the proposed approach. [ABSTRACT FROM AUTHOR]

The collaboration among different organizations is considered one of the main benefits of moving applications and services to a cloud computing environment. Unfortunately, this collaboration raises many challenges such as the access of sensitive resources by unauthorized people. Usually, Role-Based Access-Control (RBAC) model is deployed in large organizations. This paper addresses the scalability problem of the online stored rules. This problem affects the performance of the access control system due to increasing number of shared resources and/or number of collaborating organizations in the same cloud environment. Therefore, this paper proposes replacing the cross-domain RBAC rules with Role-To-Role (RTR) mapping rules among all organizations. The RTR mapping rules are generated using a newly proposed Role-Mapping algorithm. A comparative study is performed to evaluate the proposed algorithm's performance with concerning the Rule-Store size and the authorization response time. According to the results, it is found that the proposed algorithm reduces the number of stored rules which minimizes the Rule-Store size and reduces the authorization response time. Additionally, this paper proposes applying a concurrent approach on the RTR mapping model using the proposed Role-Mapping algorithm to achieve more savings in the authorization response time. Therefore, it will be suitable in highly-collaborative cloud environments. [ABSTRACT FROM AUTHOR]

There is a trend to deploy neural network on edge devices in recent years. While the mainstream of research often concerns with single edge device processing and edge-cloud two-layer neural network collaborative computing, in this paper, we propose partitioning multi-layer edge network for neural network collaborative computing. With the proposed method, sub-models of neural network are deployed on multi-layer edge devices along the communication path from end users to cloud. Firstly, we propose an optimal path selection method to form a neural network collaborative computing path with lowest communication overhead. Secondly, we establish a time-delay optimization mathematical model to evaluate the effects of different partitioning solutions. To find the optimal partition solution, an ordered elitist genetic algorithm (OEGA) is proposed. The experimental results show that, compared with traditional cloud computing, single-device edge computing and edge-cloud collaborative computing, the proposed multi-layer edge network collaborative computing has a smaller runtime delay with limited bandwidth resources, and because of the pipeline computing characteristics, the proposed method has a better response speed when processing large number of requests. Meanwhile, the OEGA algorithm has better performance than conventional methods, and the optimized partitioning method outperforms other methods like random and evenly partition. [ABSTRACT FROM AUTHOR]

The physical cloud servers consists of n number of machines for serving the requirements posted via Virtual Machines by the users. The allocation of requested Virtual Machines to appropriate Physical Servers is one among the challenging task exists in Cloud Computing. During the deployment phase several objectives needs to be considered which includes the total power consumption, the resource wastage, network propagation delay and so on. Allocating VM's to PM's with such fashion will lead the service providers financially. In this paper a swarm-based algorithm is modified for solving VM placement in terms of multi-objective perspective. Minimizing total power consumption and minimizing the resource wastage are the objectives considered in this paper. The results show the significance of the proposed model in terms of objective space and with the performance indicators such as ONGV and Spacing ( S p ). [ABSTRACT FROM AUTHOR]

With the rapid development of image video and tourism economy, tourism economic data are gradually becoming big data. Therefore, how to schedule between data has become a hot topic. This paper first summarizes the research results on image video, cloud computing, tourism economy, and data scheduling algorithms. Secondly, the origin, structure, development, and service types of cloud computing are expounded in detail. And in order to solve the problem of tourism economic data scheduling, this paper regards the completion time and cross-node transmission delay as the constraints of tourism economic data scheduling. The constraint model of data scheduling is established, the fitness function is improved on the basis of an artificial immune algorithm combined with the constraint model, and the directional recombination of excellent antibodies is carried out by using the advantages of gene recombination so as to obtain the optimal solution to the problem more appropriately. When the resource node scale is 100, the response time of EDSA is 107.92 seconds. [ABSTRACT FROM AUTHOR]

Researchers around the globe have contributed for many years to the research field of fault-tolerant control; the importance of this field is ever increasing as a consequence of the rising complexity of technical systems, the enlarging importance of electronics and software as well as the widening share of interconnected and cloud solutions. This field was supplemented in recent years by fault-tolerant design. Two main goals of fault-tolerant design can be distinguished. The first main goal is the improvement of the controllability and diagnosability of technical systems through intelligent design. The second goal is the enhancement of the fault-tolerance of technical systems by means of inherently fault-tolerant design characteristics. Inherently fault-tolerant design characteristics are, for instance, redundancy or over-actuation. This paper describes algorithms, methods and tools of fault-tolerant design and an application of the concept to an automated guided vehicle (AGV). This application took place on different levels ranging from conscious requirements management to redundant elements, which were consciously chosen, on the most concrete level of a technical system, i.e., the product geometry. The main scientific contribution of the paper is a methodical framework for fault-tolerant design, as well as certain algorithms and methods within this framework. The underlying motivation is to support engineers in design and control trough product development process transparency and appropriate algorithms and methods. [ABSTRACT FROM AUTHOR]

The distributed architecture of cloud computing necessitates robust defense mechanisms to secure network-accessible resources against a diverse and dynamic threat landscape. A Network Intrusion Detection System (NIDS) is pivotal in this context, with its efficacy in cloud environments hinging on its adaptability to evolving threat vectors while mitigating false positives. In this paper, we present a novel NIDS algorithm, anchored in the Transformer model and finely tailored for cloud environments. Our algorithm melds the fundamental aspects of network intrusion detection with the sophisticated attention mechanism inherent to the Transformer model, facilitating a more insightful examination of the relationships between input features and diverse intrusion types, thereby bolstering detection accuracy. We provide a detailed design of our approach and have conducted a thorough comparative evaluation. Our experimental results demonstrate that the accuracy of our model is over 93%, which is comparable to that of the CNN-LSTM model, underscoring the effectiveness and viability of our Transformer-based intrusion detection algorithm in bolstering cloud security. [ABSTRACT FROM AUTHOR]

In application scenarios such as cloud storage, a symmetric algorithm can be used to encrypt massive data. However, using the symmetric operation and keys in encryption and decryption, it is very difficult to realize an efficient access control system. The asymmetric encryption algorithm ciphertext-policy attribute-based encryption (CP-ABE), as a versatile one-to-many encryption algorithm, is considered an ideal access control tool to solve the user's distrust of the service provider of cloud storage. However, in a traditional CP-ABE system, the malicious users may deliberately leak their attribute keys to others or build a decryption black box (DB) to provide illegal decryption services for benefits, while no one can determine their identities. To deal with the problems, especially the problem of tracing the DB builder, CP-ABE schemes with black-box traceability have been proposed in the past few years. But in this paper, we point out that for now, the tracing algorithms in all existing schemes actually work on an impractical assumption. That is, whenever a DB receives a decryption request, it always performs the decryption algorithm honestlycorrectly. We argue that if a DB finds the decryption request comes from the tracing algorithm, it may intentionally output incorrect decryption results to counter the tracing. Thus, we present the structural defect of the tracing algorithm applied to all known traceable CP-ABE schemes. We describe the construction of an alert decryption black box (ADB) that is capable of distinguishing a tracing ciphertext from a normal one. We also show how an ADB frustrates the tracing algorithm, and furthermore, malicious users can even apply an ADB to frame innocent users. [ABSTRACT FROM AUTHOR]

Cloud computing plays an essential role in various fields. However, the existing cloud services face a severe challenge, which is how to share the data among a large scale of devices securely. In this paper, we introduce a cloud-based privacy-preserving data sharing scheme, derived from identity-based matchmaking encryption. In our scheme, the access policies are designed by both the sender and receiver simultaneously, to support bilateral access control. To improve efficiency, we delegate the match algorithm to the cloud server, reducing the computation cost and communication overhead on end devices without revealing the users' privacy. Through formal security analysis, we show that our scheme holds security, authenticity, and privacy. Finally, we evaluate our scheme by conducting extensive experiments, indicating that our scheme is more efficient than the other data-sharing schemes in ME-based services in a real-world dataset. [ABSTRACT FROM AUTHOR]

In the network economy domain, urban competitiveness refers to the comparison between cities in terms of competition and development. It is the ability to gain competitive advantage under different factors. The evaluation of urban competitiveness will help cities to learn from each other, and provides reference for the government to enhance urban competitiveness. Unlike various studies in the literature exploiting only the non-linear characteristics of urban competitiveness, this paper selects BP (Back Propagation) network as the main framework for evaluation. A Genetic Algorithm BP (GABP) network based on genetic optimization is utilized. The weights are optimized besides the crossover mutation of GA algorithm. To compensate the slow prediction in the stand-alone mode, this work proposes a MapReduce (MP) based method; MR-GABP via cloud computing. The model ensures effective urban competitiveness evaluation with improved convergence speed and threshold generation speed. The systematic experiments conducted verify effectiveness of the method while the results obtained reveal that performance of the method is better than the other methods in terms of accuracy and recall yielded as 95.1% and 92.6% respectively. [ABSTRACT FROM AUTHOR]

With services in cloud manufacturing expanding, cloud manufacturers increasingly use service level agreements (SLAs) to guarantee business processing cooperation between CSPs and CSCs (cloud service providers and cloud service consumers). Although blockchain and smart contract technologies are critical innovations in cloud computing, consensus algorithms in Blockchain as a Service (BaaS) systems often overlook the importance of SLAs. In fact, SLAs play a crucial role in establishing clear commitments between a service provider and a customer. There are currently no effective consensus algorithms that can monitor the SLA and provide service level priority. To address this issue, we propose a novel KNN-based consensus algorithm that classifies transactions based on their priority. Any factor that impacts the priority of the transaction can be used to calculate the distance in the KNN algorithm, including the SLA definition, the smart contract type, the CSC type, and the account type. This paper demonstrates the full functionality of the enhanced consensus algorithm. With this new method, the CSP in BaaS systems can provide improved services to the CSC. Experimental results obtained by adopting the enhanced consensus algorithm show that the SLA is better satisfied in the BaaS systems. [ABSTRACT FROM AUTHOR]

In order to improve the resource scheduling efficiency of the smart education system in colleges and universities and improve the stability and security of educational resource processing, this paper combines cloud-side collaborative computing technology to improve the cloud computing task scheduling algorithm and uses the improved algorithm for task scheduling. Moreover, this paper combines the global optimization and local optimization processes to make the final optimal solution performance better and further reduce the task scheduling execution time in the cluster, thereby improving the computing efficiency of cloud computing. In addition, this paper combines the improved algorithm to construct the system structure framework and verifies the system performance through experiments. The experimental research results show that the smart education system in colleges and universities based on the cloud-side collaborative computing task scheduling algorithm constructed in this paper is very good in all aspects and has a greater advantage in processing educational resources. [ABSTRACT FROM AUTHOR]

Cloud computing is a wide and fastest growing area in terms of computing research and industry these days. It mainly provides services based on IaaS, SaaS, and PaaS. These are the key parameters which decide the role of cloud services to the end users. These services can be offered to the end users through virtualization over the internet. Cloud has many advantages like large scaled computing, flexible infrastructures, pay per use, on demand services and many more. There are some major issues in processing of jobs over cloud computing like security, equal distribution of load, fault tolerance etc. and the biggest challenge over cloud is latency time which means the total time between the data sent by IoT over cloud, processing time and finally reply to the IoT or vice versa. The IoT devices will cause large number of various types of data that majorly could be difficult to handle for traditional system and sometimes over cloud systems to manage. Therefore, Fog computing is the paradigm which helps in upgradation of the current systems. It includes well equipped storage structure, computation, easily accessible data, and application-based services same as to the cloud computing closer to the users. In this survey paper, we introduce a comprehensive summary on the fog cloud networks, its architecture and the comparative study of load balancing algorithms which make fog cloud systems more efficient, and the intensive role of fog devices are like to act in advanced areas as in Tactile Internet. [ABSTRACT FROM AUTHOR]

With the development of cloud computing, a growing number of workflows are deployed on cloud platform that can dynamically provides cloud resources on demand for users. In clouds, one basic problem is how to schedule workflow for minimizing the execution cost and the workflow completion time. Aiming at the problem that the maximum completion time and cost of multiple workflows are too high, this paper proposes a model of dynamic multi-workflow scheduling in cloud environment and a new scheduling algorithm which is named as MT (multi-workflow scheduling technology). In MT, the heterogeneity of resources is considered when calculating the priority of tasks. Then, the technique for order of preference by similarity to ideal solution (TOPSIS) method is used to rank the resources when selecting resources for tasks. Finally, MT takes the estimated minimum completion time of the workflow and the cost of the task as two attribute indexes in TOPSIS decision matrix. Also, it uses a fixed reference point instead of calculating ideal solution, which ensures the uniqueness of the evaluation criteria when there is a change in the number of resources. Simulation experiments are illustrated to verify the effectiveness of the proposed algorithm in reducing the maximum completion time and cost of multiple workflows. Compared with the state-of-the-art methods, the maximum completion time and cost can be reduced by at most 17 and 9 % , respectively. [ABSTRACT FROM AUTHOR]

With the popularity of smart devices and the Internet, the volume of multimedia data is growing rapidly, and content-based image retrieval (CBIR) can search for similar images from large-scale images to realize the utilization of the data. For data owners, outsourcing the management and maintenance of image data to cloud service providers can effectively reduce costs, but there is a privacy leakage problem. In this paper, we focus on image feature extraction, index design, and image similarity recognition methods under a dual server model with content-based image security similarity recognition as the research topic, the work done such as proposing a BOVW (Bag of Visual Word) feature-based image security similarity recognition scheme. The scheme combines SIFT (scale-invariant feature transform) feature secure extraction and locally sensitive hashing algorithm to achieve secure extraction of BOVW features of images. To protect the BOVW features of images, an inverted index based on word frequency division is designed, the index is stored in chunks, and an image secure similarity recognition scheme based on CNN (convolutional neural networks) features is proposed. The scalable hash index based on dimensional division is designed based on the image CNN features secure extraction algorithm. The security and performance of the proposed scheme are theoretically analyzed and experimentally verified. Based on different image datasets, the impact of different parameters on the performance of the scheme is tested, and optimized parameters are given. The experimental results show that the proposed scheme in this paper can effectively improve the efficiency of analyzing the similarity of plant botanical art images compared to the existing schemes. [ABSTRACT FROM AUTHOR]

With the development of computationally intensive applications, the demand for edge cloud computing systems has increased, creating significant challenges for edge cloud computing networks. In this paper, we consider a simple three-tier computational model for multiuser mobile edge computing (MEC) and introduce two major problems of task scheduling for federated learning in MEC environments: (1) the transmission power allocation (PA) problem, and (2) the dual decision-making problems of joint request offloading and computational resource scheduling (JRORS). At the same time, we factor in server pricing and task completion, in order to improve the user-friendliness and fairness in scheduling decisions. The solving of these problems simultaneously ensures both scheduling efficiency and system quality of service (QoS), to achieve a balance between efficiency and user satisfaction. Then, we propose an adaptive greedy dingo optimization algorithm (AGDOA) based on greedy policies and parameter adaptation to solve the PA problem and construct a binary salp swarm algorithm (BSSA) that introduces binary coding to solve the discrete JRORS problem. Finally, simulations were conducted to verify the better performance compared to the traditional algorithms. The proposed algorithm improved the convergence speed of the algorithm in terms of scheduling efficiency, improved the system response rate, and found solutions with a lower energy consumption. In addition, the search results had a higher fairness and system welfare in terms of system quality of service. [ABSTRACT FROM AUTHOR]

Connectivity and cloud computing are key elements in the future of electric mobility. They allow manufacturers to provide advanced fleet management and predictive diagnostic services. In particular, cloud computing dramatically enhances data availability and enables the use of more complex and accurate state estimation algorithms for electric vehicle lithium-ion batteries. A tuning procedure for a moving window least squares algorithm to estimate the parameters of a 2-RC equivalent circuit battery model is presented in this paper. The tuning procedure uses real data collected from a test vehicle and uploaded to the Stellantis-CRF cloud. The tuned algorithm was applied to eight months of road tests and showed very small estimation errors. The errors are comparable to other literature data, even when the literature results were obtained in laboratory tests. The estimated model parameters are tracked through time and seem accurate enough to show the first signs of battery aging. [ABSTRACT FROM AUTHOR]

CC's (Cloud Computing) networks are distributed and dynamic as signals appear/disappear or lose significance. MLTs (Machine learning Techniques) train datasets which sometime are inadequate in terms of sample for inferring information. A dynamic strategy, DevMLOps (Development Machine Learning Operations) used in automatic selections and tunings of MLTs result in significant performance differences. But, the scheme has many disadvantages including continuity in training, more samples and training time in feature selections and increased classification execution times. RFEs (Recursive Feature Eliminations) are computationally very expensive in its operations as it traverses through each feature without considering correlations between them. This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets. The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers. The proposed AKFA (Adaptive Kernel Firefly Algorithm) is for selecting features for CNM (Cloud Network Monitoring) operations. AKFA methodology is demonstrated using CNSD (Cloud Network Security Dataset) with satisfactory results in the performance metrics like precision, recall, F-measure and accuracy used. [ABSTRACT FROM AUTHOR]

Information technology has played a key role in the development of the tourism planning service industry and has now become an important foundation for the survival and rapid development of the industry. In this context, due to the fast updating and popularization of information technology, it has greatly promoted the development of the tourism industry. In order to meet the current public demand for tourism information, this paper integrates cloud computing, VR technology, and big data analysis technology to build a smart platform for intelligent perception tourism information services. The system can obtain tourism information through mobile Internet terminals. Among them, the database of the smart tourism planning platform is the most important module. Aiming at the many difficulties in adapting data encryption in cloud storage applications, this article designs an adaptive CloudCrypt data encryption system based on cloud computing technology and proposes dynamic JavaScript dynamic analysis and automatic identification of data technology, through adaptive different cloud applications to obtain data encryption protection. CloudCrypt is suitable for typical cloud applications, such as mail and storage. The entry cost of the system is extremely low; it can fully guarantee the security of the tourism information platform database and can integrate wireless sensor networks into the tourism information platform. The network system composed of sensor nodes activates detection, calculation, and communication modules through wireless communication and has the advantages of low cost, low power consumption, and fast networking speed. In this paper, through the construction of integrated wireless sensor technology and cloud computing database, it is applied to the construction of a tourism smart platform, thereby promoting the development of smart travel technology. [ABSTRACT FROM AUTHOR]

In order to solve the security problem of privacy information exposed by plaintext access strategy in cloud computing environment, this paper proposes a new attribute encryption anonymity algorithm which can be hidden and updated. The algorithm proposed in this paper hides the access policy attribute information through a randomized technology, which can support full hiding of the policy, and the prevention and control are more flexible. The experimental results show that, compared with the previous decryption algorithms, under the premise of the same attributes, the encryption time decreases in a few seconds. When the number of attributes is 1, the encryption time decreases by 10 s, and when the number of attributes is 10, the encryption time decreases by about 100 s, and the efficiency is greatly improved. [ABSTRACT FROM AUTHOR]

Cloud computing is the most widely adapted computing model to process scientific workloads in remote servers accessed through the internet. In the IaaS cloud, the virtual machine (VM) is the execution unit that processes the user workloads. Virtualization enables the execution of multiple virtual machines (VMs) on a single physical machine (PM). Virtual machine placement (VMP) strategically assigns VMs to suitable physical devices within a data center. From the cloud provider's perspective, the virtual machine must be placed optimally to reduce resource wastage to aid economic revenue and develop green data centres. Cloud providers need an efficient methodology to minimize resource wastage, power consumption, and network transmission delay. This paper uses NSGA-III, a multi-objective evolutionary algorithm, to simultaneously reduce the mentioned objectives to obtain a non-dominated solution. The performance metrics (Overall Nondominated Vector Generation and Spacing) of the proposed NSGA-III algorithm is compared with other multi-objective algorithms, namely VEGA, MOGA, SPEA, and NSGA-II. It is observed that the proposed algorithm performs 7% better that the existing algorithm in terms of ONVG and 12% better results in terms of spacing. ANOVA and DMRT statistical tests are used to cross-validate the results. [ABSTRACT FROM AUTHOR]