Cloud Computing Environment Research Articles

Takeaways from an experimental evaluation of eXpress Data Path (XDP) and Data Plane Development Kit (DPDK) under a Cloud Computing environment

The popularization of the Linux kernel at data center servers became a significant factor to consider when developing or deploying network applications. New “packet processing frameworks” emerged to ensure fast packet processing inside these servers that rely on the Linux kernel, XDP and DPDK being the two main ones. Alongside this, Cloud Computing environments dominated the data center scenario, bringing virtualization to the center of these servers. This research compares DPDK and XDP packet processors when subject to a cloud computing environment with I/O, CPU, and network load. We first describe an architecture that implements frameworks and emulates a cloud environment. We measure throughput and packet loss while varying the number of competing VMs, CPU usage by the frameworks, and packet size and present our results in the form of takeaways. Furthermore, we conclude that the virtual environment can impact the performance of both frameworks depending on the received packet size and the type of workload the cloud environment performs. A significant case to highlight is when the cloud environment performs network load, and the servers receive small-sized packets, creating both throughput degradation and increasing packet loss. We conclude our experiments by conducting statistical non-parametric hypothesis tests to enhance our conclusions and have a closer look at the results.

Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms. Keywords: Cloud computing, Task scheduling, Load balancing, Makespan, Energy-aware, Turnaround time, Response time, Cost of task, QoS, Multi-objective. DOI: 10.7176/IKM/12-5-03 Publication date: September 30 th 2022

High- Professional Quality Videos Using the Black Box Algorithm in Cloud Computing Environment

High- Professional Quality Videos Using the Black Box Algorithm in Cloud Computing Environment

Determinants of Customer Loyalty in a Cloud Computing Environment

This study's two objectives are first to assess the relationships between customer loyalty and potential determinants (customer satisfaction with the website, after sale customer service, and product return handling) in the special PaaS environment where users can produce a greater variety of website features compared to the more widely studied SaaS environment and second to test the impact of the website's customer decision support system as a potential moderator for the relationship between customer satisfaction with the website and customer loyalty. To test the hypotheses, 138 CC client organizations participated by collecting data from their website customers accessing their order entry website applications. The results confirmed the importance of the proposed relationships and enabled several important managerial insights, including the importance of client organization choosing the appropriate CC approach to improve customer loyalty to the website.

Performance Test and Improvement of Computer Network Virtualization Software in Cloud Computing Environment

In order to make up for the problems of inconveniences, unavailability, differences, and exclusivity in the performance test of the computer virtual network software in the cloud computing environment, a method of building a distributed system performance test platform based on cloud computing is proposed. The distributed system performance test method based on the decision tree, the common performance index test method, and the resource scheduling method in the cloud environment is mainly adopted, and the cloud computing and the automatic performance test technology are combined so as to build a prototype system of the cloud performance test platform. Experimental data show that the CPU utilization and the memory utilization can be reduced to 32% and 30%, respectively, by using this method. It is about 30 times better than the result before application. The results show that it can improve the efficiency, availability, and convenience of software performance test greatly. Also, it also can improve the software performance to facilitate people’s life and work in the cloud computing environment.

Application Research of IICA Algorithm in a Limited-Buffer Scheduling Problem

An improved imperialist competition algorithm (IICA) was proposed to resolve the problem of flexible flow shop scheduling that with limited buffer (LBFFSP). The IICA algorithm adds the elite personal retention strategy, reform operation and the discrete processing on the basis of ICA algorithm. This paper uses an individual selection mechanism based on Hamming distance to improve the ability of initial solution. The prospect is to use the IICA to work out related problems in cloud computing environment. The scheduling problem in the cloud-computing environment needs to consider the optimization of the completion time and cost of using cloud resources. At last, simulation tests and example tests are used to check the usefulness of improved imperialist competition algorithm in solving LBFFSP problem. Compared with standard ICA, the improved ICA has improved in the algorithm evolution process. As such, scheme 4 has a better ability to evolve, and its evolution time is greater than that of scheme 3. Scheme 5 has the best algorithm optimization performance.

Design of Multiple Share Creation with Optimal Signcryption based Secure Biometric Authentication System for Cloud Environment

Biometric authentication plays a vital role in the cloud computing (CC) environment to accomplish security and privacy. Several kinds of biometrics are used for the authentication process, particularly, the fingerprint is a commonly used biometric, which accomplishes maximum security. But it can be easily stolen, unintentionally revealed, or difficult to remember. Therefore, biometric authentication techniques are needed and an encryption process is included to improve the security of the authentication process. In this aspect, this paper proposes a multiple share creation with an optimal signcryption-based biometric authentication system (MSCOS-BAS- FLNN- PeSeOA) for the cloud environment. The proposed model enables the creation of multiple shares of the input biometric image (fingerprint) and encrypts them for a secure authentication system. In addition, the encryption of individual shares takes place with an optimal signcryption system where the key generation process is carried out using the penguin search optimization algorithm (PeSeOA). Finally, the functional link neural network (FLNN) model is applied for the biometric authentication verification process. A wide range of simulation analyses are performed to highlight the supreme efficacy of the MSCOS-BAS- FLNN- PeSeOA technique. The experimental outcomes pointed out the hopeful performance of the MSCOS-BAS- FLNN- PeSeOA technique over the recent techniques.

Block chain enabled auditing with optimal multi‐key homomorphic encryption technique for public cloud computing environment

SummaryCloud storage is a significant cloud computing (CC) service which enables the client to save and retrieve the data at anytime and anywhere. Due to the increased demand and familiarity of the CC environment, different kinds of security threats and susceptibilities are raised. Data integrity and privacy are the major problems in CC environment where the data can be saved in distinct geographic regions. So, privacy preservation and data integrity become important factors of the user concern related to the CC environment. Several auditing protocols are majorly dependent upon the conventional public key infrastructure, which led to high computational complexity and it is unsuitable for the setting of multiple users. To resolve these issues, this study develops a new block chain enabled auditing with optimal multi‐key homomorphic encryption (BEA‐OMKHE) technique for public cloud environment. The proposed BEA‐OMKHE technique aims to assure data integrity, security, and auditing in public cloud storage. Besides, an OMKHE technique is derived to accomplish data integrating into the cloud environment by the design of end to end encryption system. A secure generation of keys and encryption processes are carried out by the use of MKHE technique; thereby the data becomes highly secure. In addition, the choice of keys is performed by the improved beetle antenna search optimization (IBAS) algorithm. Therefore, the proposed BEA‐OMKHE technique offers an efficient way of enhancing the data integrity in CC method. The performance validation of the BEA‐OMKHE technique takes place and the results are inspected under various aspects. The comparative result analysis ensured the betterment of the BEA‐OMKHE technique interms of different measures such as communication cost, encryption time, decryption time, computation cost, privacy preserving rate, and authentication accuracy.

Secure Content-Based Image Retrieval with Copyright Protection within Cloud Computing Environment

Every day, a tremendous amount of image data is generated as a result of recent advances in imaging and computing technology. Several content-based image retrieval (CBIR) approaches have been introduced for searching image collections. These methods, however, involve greater computing and storage resources. Cloud servers can address this issue by offering a large amount of computational power at a low cost. However, cloud servers are not completely trustworthy, and data owners are concerned about the privacy of their personal information. In this research, we propose and implement a secure CBIR (SCBIR) strategy for searching and retrieving cipher text image databases. In the proposed scheme, the extract aggregated feature vectors to represent the related image collection and use a safe Asymmetric Scalar-Product-Preserving Encryption (ASPE) approach to encrypt these vectors while still allowing for similarity computation. To improve search time, all encrypted features are recursively clustered using the k-means method to create a tree index. The results reveal that SCBIR is faster at indexing and retrieving than earlier systems, with superior retrieval precision and scalability. In addition, our paper introduces the watermark to discover any illegal distributions of the images that are received by unlawful data users. Particularly, the cloud server integrates a unique watermark directly into the encrypted images before sending them to the data users. As a result, if an unapproved image copy is revealed, the watermark can be extracted and the unauthorized data users who spread the image can be identified. The performance of the proposed scheme is proved, while its performance is demonstrated through experimental results.

A Literature Survey on Algorithms for the Optimal Load Balancing in Cloud Computing Environments

Abstract: This paper provides a survey of the existing literature and research carried out in the area of Optimal load balancing in servers. Load balancing in cloud computing is one of the important aspects for efficient delivery of resources and computing . The process of distributing workloads and computing resources in a cloud computing environment is cloud load balancing. It allows enterprises to manage application or workload demands by allocating resources among multiple computers, networks or servers. Hosting the distribution of workload traffic and demands that reside over the internet. It helps enterprises achieve high performance levels for very reasonable costs which is lower than traditional on premises load balancing technology . By taking advantage of clouds' scalability and agility cloud load balancing technology meets rerouted work demands and also improves overall availability. Load balancing can provide health checks for cloud in addition to the workload and traffic distribution .Performance analysis of different existing load balancing algorithms based on different parameters is to be carried out and the algorithm will be optimized for the better performance in cloud. The main purpose of the proposed paper will be to get help in the design of new algorithms in future.

An Improved and Efficient and Dynamic Load Balancing Approach in Cloud Computing Environments

Cloud Computing Environment (CCE) is a newly rudimentary knowledge in the IT industry. Central to these problems lies in the establishment of an active LB algorithm. The network load can be CPU load, memory limit, postponement, or system load. In the proposed algorithm, Load Balancing (LB) done by considering priority policy. An Efficient and Dynamic Load Balancing Approach in Cloud Computing (EDBA-CC) proposed, and the related algorithm executed on CloudSim (CS). A resource allocation approach that takes into account Resource Utilization (RU) would lead to better energy efficiency. The outcomes indicate the effectiveness of the proposed algorithm. The outcomes exhibited that the EDBA-CC algorithm reduced Response Time (RT). The decreasing in RT is about 2.23 ms when the instruction length was 275 Byte, and the decreasing became more obviously with 10.12 ms for RT and 5.54 ms for RT when the instruction length was 2000 Byte.

Remote Data Auditing in a Cloud Computing Environment

In the current paradigms of information technology, cloud computing is the most essential kind of computer service. It satisfies the need for high-volume customers, flexible computing capabilities for a range of applications like as database archiving and business analytics, and the requirement for extra computer resources to provide a financial value for cloud providers. The purpose of this investigation is to assess the viability of doing data audits remotely inside a cloud computing setting. There includes discussion of the theory behind cloud computing and distributed storage systems, as well as the method of remote data auditing. In this research, it is mentioned to safeguard the data that is outsourced and stored in cloud servers. There are four different techniques of remote data auditing procedures that are presented here for distributed cloud services. There are several difficulties associated with data audit methods; however, these difficulties may be overcome by using a variety of techniques, such as the Boneh-Lynn-Shacham signature or the automated blocker protocol. In addition to that, other difficulties associated with distributed-based remote data auditing solutions are discussed. In addition, a variety of approaches might be researched further for further examination in order to find answers to these impending problems.

The Effectiveness Evaluation Model of the Conversion of New and Old Kinetic Energy in the Cloud Computing Environment

This study constructs an index system for the conversion of new and old kinetic energy (NOKE) from three dimensions of electricity, economy, and energy efficiency in the cloud computing environment. First, an annual evaluation model is constructed based on the conversion of NOKE. Second, an evaluation model based on decision-making trial and evaluation laboratory (DEMATEL)-analytic network process (ANP) and improved grey relational method is established. Finally, the annual evaluation is carried out through examples. The results show that the conversion effect of NOKE in Liaoning Province is 0.7956 in 2019, 0.803 in 2020, and 0.9448 in 2021. The evaluation value increases year by year, indicating that the conversion effect of NOKE in Liaoning Province is becoming more and more significant. A series of NOKE conversion measures have achieved positive results. This study provides the theoretical basis and practical guidance for China to promote the transformation of NOKE.

Performance and Cost-Efficient Spark Job Scheduling Based on Deep Reinforcement Learning in Cloud Computing Environments

Big data frameworks such as Spark and Hadoop are widely adopted to run analytics jobs in both research and industry. Cloud offers affordable compute resources which are easier to manage. Hence, many organizations are shifting towards a cloud deployment of their big data computing clusters. However, job scheduling is a complex problem in the presence of various Service Level Agreement (SLA) objectives such as monetary cost reduction, and job performance improvement. Most of the existing research does not address multiple objectives together and fail to capture the inherent cluster and workload characteristics. In this article, we formulate the job scheduling problem of a cloud-deployed Spark cluster and propose a novel Reinforcement Learning (RL) model to accommodate the SLA objectives. We develop the RL cluster environment and implement two Deep Reinforce Learning (DRL) based schedulers in TF-Agents framework. The proposed DRL-based scheduling agents work at a fine-grained level to place the executors of jobs while leveraging the pricing model of cloud VM instances. In addition, the DRL-based agents can also learn the inherent characteristics of different types of jobs to find a proper placement to reduce both the total cluster VM usage cost and the average job duration. The results show that the proposed DRL-based algorithms can reduce the VM usage cost up to 30%.

The Application of English Vocabulary Education Informatization under 5G and Cloud Computing Environment

In the 5G and cloud computing environment, autonomous learning based on smart mobile devices has a significant effect on improving students’ vocabulary and vocabulary use. This paper adopts a combination of vocabulary testing and interviews, and by building an autonomous English vocabulary learning platform based on 5G and cloud computing technology, learners can learn and use vocabulary in real natural contexts anytime, anywhere through smart mobile devices. Experimental results show that real-time interaction and collaboration with teachers and other learners during the learning process greatly improve the efficiency and quality of their vocabulary learning.

Research on Optimization Strategy of Task Scheduling Software Based on Genetic Algorithm in Cloud Computing Environment

In order to improve the task scheduling strategy, a method based on genetic algorithm in cloud computing environment was proposed. First, the independent task scheduling algorithm and associated task scheduling algorithm commonly used in cloud computing are studied and compared, respectively, and their application characteristics, advantages, and disadvantages are analyzed in detail. Second, an independent task scheduling strategy based on multipopulation genetic algorithm is proposed for independent task scheduling in cloud environment, considering the scheduling time, scheduling cost, and system resource utilization of task set. The implementation steps of the algorithm are given in detail. Finally, the simulation experiment is carried out on Cloud Sim platform. The experimental results show that computing resource M is 10, subtask N is 2000, population size S is 80, and ETC matrix and RCU array are randomly generated by the system. As the number of iterations increases, the scheduling scheme formed by MCGA and CGA is more obvious and close to the subtask execution cost optimization. Finally, the optimized scheme is basically formed. However, the scheduling scheme formed by TGA has no obvious optimization effect on the subtask execution cost. It is proved that the algorithm proposed in this paper can effectively optimize the task scheduling efficiency and improve the utilization of cloud computing resources at the same time, providing a feasible idea and method for task scheduling in the cloud computing environment.

Integration and Optimization of British and American Literature Information Resources in the Distributed Cloud Computing Environment.

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.

A simulation task partition method based on cloud computing resource prediction using ensemble learning

The calculation processing and interactions between complex system simulation entities in a cloud-computing environment exhibit dynamic change. However, parallelized task partitioning methods often ignore these dynamic characteristics, which makes it difficult to improve execution efficiency for complex system simulations. Therefore, we propose a two-stage simulation task partition method that computes the resource prediction information. In the first stage, the features of collected data are analyzed to select the optimal feature dimensions. A stacking ensemble learning method is used to predict the simulation runtime under the condition of allocating various computing resources, and the number of resources required for the shortest runtime is obtained by sorting the predicted results. In the second stage, a multi-weight graph structure is designed to represent the dynamic interaction of simulation entities, with the goal of minimizing the load imbalance on each computing node, as well as the maximum number of communications between nodes. Subsequently, the simulated annealing optimization algorithm is used to partition multiple weight graphs and map simulation tasks to resources. We compared our multi-weight graph partition method with the Metis and Chacos models. The experimental results demonstrate that, while all three methods can achieve the shortest runtime with the predicted number of computing resources, our algorithm can further reduce the runtime by up to 26%.

Bi-Objective Optimizing for Data-Intensive ‎Scientific Workflow Scheduling in Cloud ‎Computing

Cloud Computing is increasingly recognized as a new way to use on-demand, computing, ‎storage and network services in a transparent and efficient way. Cloud Computing environment ‎consists of large customers requesting for cloud resources. Nowadays, task scheduling problem ‎and data placement are the current research topic in cloud computing. In this work, a new ‎technique for workflow scheduling and data placement are proposed based on genetic ‎algorithm to fulfill a final bi-objective goal such as minimizing total workflow response time and ‎cost of their execution. the scheduling of scientific workflows is considered to be an NP-complete ‎problem, i.e. a problem not solvable within polynomial time with current resources. The ‎performance of this proposed algorithm has been evaluated using CloudSim toolkit, Simulation ‎results show the effectiveness of the proposed algorithm in comparison with well-known ‎algorithms such as genetic algorithm with Random data placement. ‎

Rotating behind Security: A Lightweight Authentication Protocol Based on IoT-Enabled Cloud Computing Environments.

With the rapid development of technology based on the Internet of Things (IoT), numerous IoT devices are being used on a daily basis. The rise in cloud computing plays a crucial role in solving the resource constraints of IoT devices and in promoting resource sharing, whereby users can access IoT services provided in various environments. However, this complex and open wireless network environment poses security and privacy challenges. Therefore, designing a secure authentication protocol is crucial to protecting user privacy in IoT services. In this paper, a lightweight authentication protocol was designed for IoT-enabled cloud computing environments. A real or random model, and the automatic verification tool ProVerif were used to conduct a formal security analysis. Its security was further proved through an informal analysis. Finally, through security and performance comparisons, our protocol was confirmed to be relatively secure and to display a good performance.