Papers by Keyword: Cloud Computing

Abstract: Cloud computing eliminates the need for expensive hardware and software expenditures by revolutionising access to computing resources through internet-based utility services. However, Data Integrity (DI) in this paradigm faces a variety of challenging issues, including complexity, security, privacy, control limits, fallibility of human beings, and financial limitations. The shortcomings of current DI solutions in terms of guaranteeing data verification, preventing replay attacks, and controlling computational overhead have led to an increasing need for access to cloud infrastructures by third-party verifiers. The suggested Cryptographic Accumulator Provable Data Possession with Merkle Hash Tree (CAPDP-MHT) scheme demonstrates significantly improved performance over Provable Data Possession (PDP) and Rivest Shamir Adleman (RSA) algorithms in various domains, as demonstrated by thorough simulation and MATLAB-based evaluation. In particular, CAPDP-MHT outperforms PDP and RSA with an average data verification success rate of 25%, compared to their respective rates of 10% and 5%. Moreover, it identifies replay attacks in about 30 seconds, compared to 45 and 70 seconds for PDP and RSA, respectively. Furthermore, the computational overhead of CAPDP-MHT is about 27 seconds, while that of PDP and RSA is 45 and 60 seconds, respectively. Therefore, as compared to PDP and RSA-based systems, CAPDP-MHT not only exhibits exceptional computing efficiency but also outperforms in reliability.

Abstract: Many organizations have embraced cloud computing in recent years to provide new services, easily expand their IT resources, and reduce the cost of their IT infrastructure. This has been made possible through the implementation of resource allocation strategies by cloud service providers. One of the major challenges during resource allocation is to minimize power consumption while ensuring the required Service Level Agreement (SLA). To solve this problem, a new approach to efficiently allocate resources in cloud computing while optimizing energy consumption and guaranteeing the required service level agreement has been proposed. The main idea of this proposal is to leverage the CNN-LSTM architecture to accurately predict resource utilization in order to make the appropriate resource allocation decision. The proposed solution was validated in two steps: step 1) a comprehensive set of statistical performance analysis and step 2) an intensive simulation of the solution for resource allocation using cloudSim Plus tool. The results of the experimentation demonstrated that the proposed solution can help cloud service providers achieve energy savings while guaranteeing the required SLA.

Abstract: Cloud Computing (CC) is a platform where resources and services are huge such as platforms, infrastructure, software and much more. Cloud computing builds its entire environment on the framework based on the user's requirement. Although many interventions are implemented for the problems that are identified in cloud security systems, intrusion and security issues on various services are rising day by day. This research focuses on cloud security systems where trusted access can be guaranteed for various resources and services using deep learning techniques. Deep Learning techniques can detect the anomaly variation based on selected features to find the intruder in the service provider's environment. A Novel Recurrent Neural Network (NRNN) - Auto Encoder (AE) model with a dataset is used to identify the abnormal and behavioral variation in the network. The proposed algorithm NRNN-AE is basically identifying the uncertainty of different types of malicious theft where the auto-encoder predicts the attacks against the unexpected network security challenges along with a genetic algorithm for optimization. Attacks based on the service are identified on each hidden layer based on classification that is processed in the cloud system. The results are obtained from the comparison of NSL-KDD dataset and KDD Cup 99 dataset for monitoring the behavioral and frequent changes in patterns. The system can improve the detection rate and achieve accuracy of 96% compared to the existing RC-NN model. Also the detection rate is reduced to 0.0008 which has a precision value in both positive and negative rate as a gradual increase in performance.

Abstract: Using mobile cloud technology, local application resources can move into the cloud computing resource pool in the form of web services. The web services discovery and execution processes in the mobile environment are considered as a very difficult challenge. Moreover, these processes may degrade network performance due to the mobile environment. To overcome these issues, we present a new approach for better discovery, selection, execution, and negotiation of OWL-S services by utilizing a multi-agent system in mobile cloud computing. By using the Multi-Agent System (mobile agent and fixed agent), these processes are made with the minimum utilization of resources because the mobile agents migrate to targeted services and interact locally with them. Moreover, the use of mobile agent overcomes the problems related to the mobile environment and wireless devices. Besides, it automatizes the discovery, selection, execution, and negotiation of OWL-S services. The obtained results demonstrate the effectiveness of the proposed system. Moreover, we conclude that the mobile agent is a good solution to eliminate the problem of wireless devices and the mobile environment, and reduced the execution time of service discovery and invocation. Also, the finding indicates that cloud computing is a good solution to eliminate the problem of storage and execution in mobile devices. Besides, the context-aware provides a more accurate service selection.

Abstract: Cyber-Manufacturing System (CMS) is a vision for the factory of the future, where physical components and processes are seamlessly integrated with computing processes to form highly adaptive and responsive manufacturing operations. In CMS, manufacturing resources and capabilities are digitized and shared with users and stakeholders through a local area network (LAN) and the Internet. CMS aims to utilize the manufacturing data obtained during all product lifecycle phases to provide agile and scalable manufacturing solutions. Currently, a centralized cloud-based computing environment supported by the distributed Internet of Things (IoT) devices network is used to enable the typical functionalities—such as manufacturing resource sharing and large-scale manufacturing collaborations. However, facing the explosion of manufacturing data from factory floors, cloud-based computing solutions show limitations in providing low-latency services, performing real-time state analysis, configuring the machines, and controlling other executors in the physical manufacturing end. Furthermore, private production data and technical details cannot be appropriately masked in the public cloud platform. In this research, a Cloud-Fog Continuum Computing Architecture is introduced to better utilize and govern the manufacturing data for manufacturing enterprise stakeholders and customers in CMS. A Hadoop-Raspberry Pi computing system is presented as a proof-of-concept of the proposed continuum computing mechanism to provide machining services in CMS.

Abstract: Cloud computing is provides resource allocation which facilitates the cloud resource provider responsible to the cloud consumers. The main objective of resource manager is to assign the dynamic resource to the task in the execution and measures response time, execution cost, resource utilization and system performance. The resource manager is optimizing the resource and measure the completion time for assign resource. The resource manager is also measure to execute the resource in the optimal way to complete the task in minimum completion time. The virtualization is techniques mandatory to allocate the dynamic resource depends on the users need. There are also green computing techniques involved for enhanced the no of server. The skewness is basically used to enhance the quality of service using the various parameters. The proposed algorithms are considered to allocate the cloud resource as per the users requirement. The advantage of proposed algorithm is to view the analysis of cpu utilization and also reduced the memory usage.

Abstract: This article discusses the disaster recovery technology of online system based on cloud computing, mainly starting from planning a backup strategy to restore the transaction log, pages, files and file groups by page and data restore from a snapshot database. Timely data recovery and fault exercises with a holistic, multi-level data backup and disaster recovery technology could protect the security of the online system.

Abstract: Cloud computing is evolving as a realization of SoA in which IT resources are offered as services on a pay-as-you-go basis. But several functionally equivalent services are discovered, because of the exponential growth in the number of cloud providers and services. The best service can be selected by ranking all the discovered services using their network and other non-network QoS parameters, which is formulated as MCDM problem. In this paper, we proposed a hybrid approach for MCDM problem that contains AHP and PROMETHEE. AHP is used to investigate the structure of the service ranking problem and to determine the weights of the QoS criteria, and PROMETHEE method is applied for the final ranking. The results of evaluation demonstrate the effectiveness of the proposed hybrid method, and furthermore show the impact of network QoS parameters in the final service ranking.

Abstract: The Automatic Test System (ATS) is being increasingly used in the business and professional test. However, there are some potential problems emerged in the application of the distributed test. So we intend to solve the problems by adopting the idea of the Cloud Computing to solve the two challenges: improve the efficient use of the limited and heterogeneous hardware test resources and shorten the test cycle which is defined as the whole time of the test. The paper proposes several structures of the Cloud Test System (CTS): the overall structure, the software and hardware architecture. Theoretically,the study overcomes the challenges of the existing test system, then the foundation of the further study is laid.

Abstract: In order to take full advantage of available resources in existed campus experimental platform and be able to integrate and optimize resources of the platform, increase the rate of resource sharing, and promote the reform of the existing education system, this paper built an instructional assistant collaboration service system based on cloud platform, named briefly IACSS, in campus for teaching assistant. At first, the existed experimental system was discussed deeply. Then, aiming at the lacking of efficiency and resource sharing degree and so on, the system platform of IACSS based on cloud and mobile technology introduces the data fusion and interaction. The architecture of IACSS was given after that. Then, the system platform was built. The system platform of IACSS supports the optimization of resources and personalized services customized mechanism. And it also supports the accessed and cooperated with each other at anytime and anywhere. Nowadays, the system has been successfully deployed in the campus and the test run, the practice shows that the platform can reasonably integrate resources, improve resource sharing rate, improvement of the existing education system.