Multi Cloud Computing Research Articles

Integrative Resource Management in Multi Cloud Computing: A DRL Based Approach for multi-objective Optimization

INTRODUCTION: The multi-data canter architecture is being investigated as a significant development in meeting the increasing demands of modern applications and services. The study provides a toolset for creating and managing virtual machines (VMs) and physical hosts (PMs) in a virtualized cloud environment, as well as for simulating various scenarios based on real-world cloud usage trends.
 OBJECTIVES: To propose an optimized resource management model using the Enhanced Flower Pollination algorithm in a heterogeneous environment.
 METHODS: The combination of Q-learning with flower pollination raises the bar in resource allocation and job scheduling. The combination of these advanced methodologies enables our solution to handle complicated and dynamic scheduling settings quickly, making it suited for a wide range of practical applications. The algorithm finds the most promising option by using Q-values to drive the pollination process, enhancing efficiency and efficacy in discovering optimal solutions. An extensive testing using simulation on various datasets simulating real-world scenarios consistently demonstrates the suggested method's higher performance.
 RESULTS: In the end, the implementation is done on AWS clouds; the proposed methodology shows the excellent performance by improving energy efficiency, Co2 Reduction and cost having multi-cloud environment 
 CONCLUSION: The comprehensive results and evaluations of the proposed work demonstrate its effectiveness in achieving the desired goals. Through extensive experimentation on diverse datasets representing various real-world scenarios, the proposed work consistently outperforms existing state-of-the-art algorithms.

Cloud Architectures for Distributed Multi-Cloud Computing: A Review of Hybrid and Federated Cloud Environment

The concept of several clouds has greatly extended the use of cloud computing and gained popularity in academic and business circles. The use of multi-cloud techniques has increased as businesses use cloud computing more and more to meet their computational demands. A thorough analysis of cloud architectures intended for distributed multi-cloud computing is presented in this study, with an emphasis on federated and hybrid cloud systems. The study looks at the opportunities and difficulties of adopting and overseeing a variety of cloud resources from several providers. The review starts out by going over the basic ideas and reasons for using multi-cloud strategies, emphasizing how important flexibility, scalability, and resilience are in contemporary computing settings. The study then explores the nuances of hybrid cloud architectures, with a focus on how private and public cloud resources can be seamlessly combined. In the context of hybrid cloud installations, important factors including data sovereignty, security, and workload orchestration are covered. In addition, the research delves into federated cloud architectures, clarifying how enterprises can coordinate and oversee workloads across several cloud providers. An examination of resource identification, policy enforcement, and interoperability procedures sheds light on the intricacies of federated cloud computing. The review delves into new developments in standards, best practices, and technology that help multi-cloud ecosystems mature. The study analyses the state of research and industry practices now, pointing out gaps and possible directions for future development. The intention is to provide decision-makers, researchers, and practitioners with a comprehensive grasp of the changing cloud architectural scene so they can plan and execute distributed multi-cloud solutions with knowledge. In conclusion, this article provides a thorough overview of hybrid and federated cloud architectures by combining information from many sources. Through a comprehensive analysis of the difficulties and possibilities associated with multi-cloud computing, the study hopes to add to the current conversation on cloud environment design and optimization in the rapidly changing technological landscape.

Securing Multi-Cloud Architectures: A Machine Learning Perspective

Multi-cloud computing, the utilization of multiple cloud computing services in a single heterogeneous architecture, has gained significant traction in recent years due to its potential for enhancing flexibility, resilience, and performance. This paper provides an overview of multi-cloud computing, exploring its key concepts, advantages, challenges, and best practices. It examines the motivations behind adopting multi-cloud strategies, the various deployment models, management approaches, and emerging trends. Additionally, the paper discusses the implications of multi-cloud computing for security, interoperability, and vendor lock-in. Through a comprehensive analysis, this paper aims to offer insights into the complexities and opportunities associated with multi-cloud environments.

Prototyping of Utilization Model for KOMPSAT-3/3A Analysis Ready Data Based on the Open Data Cube Platform in Multi-Cloud Computing Environment: A Case Study

This study introduces a multi-cloud model that combines private and public cloud services for processing and managing satellite images. The multi-cloud service is established by incorporating private clouds within organizations and integrating them with external public cloud services to utilize the data. Private clouds can maintain data security within an organization or between organizations, while public clouds offer easy processing options for general users with access accounts. The model for the private cloud service utilizes open-source OpenStack software to create virtual machines, allowing users to manage analysis ready data (ARD) of the Korea Multi-Purpose Satellite (KOMPSAT)-3/3A images simultaneously. The public cloud service through Amazon Web Services (AWS) offers four services and uses the Open Data Cube (ODC) to manage data and provide web-based time-series visualization and processing. The model utilizes OpenStack to create virtual machines, and the public cloud service through AWS offers various services using ODC to manage data. A system that handles large amounts of satellite imagery in a multi-cloud environment has benefits such as improved availability, cost savings through open-source, and enhanced scalability. We present a prototyped utilization model that can be used with the ODC user interface (UI) that applies the proposed multi-cloud model. The multi-cloud model of this study can be applied to constructing a country-scale data cube system, that deals with large-scale satellite image data. It can also be applied to systems that need to be built with data that is tailored to a specific user’s needs at any institution.

A Review on Security issues in Multi Cloud Computing and prevention by security measures

A Review on Security issues in Multi Cloud Computing and prevention by security measures

Research on influencing factors of artificial intelligence multi-cloud scheduling applied talent training based on DEMATEL-TAISM

With the rapid development of Internet of Things (IoT) technology and the rising popularity of IoT devices, an increasing number of computing intensive IoT applications have been developed. However, due to the limited resources of IoT devices, cloud computing systems are required to compute intensive IoT applications. Furthermore, to be subject to a single cloud computing service provider, multi-cloud computing has become an IoT service cloud computing solution. As a result of the complexity of multi-cloud scheduling, the application of artificial intelligence is an important technology to solve IoT multi-cloud scheduling. The corresponding talent training plays an important role in the development and implementation of the IoT artificial intelligence multi-cloud scheduling. First, this paper studies the key influencing factors of IoT artificial intelligence multi-cloud scheduling applied talent training. Combined with the characteristics of the development of China’s artificial intelligence industry, this paper summarizes the influencing factors from the four-dimensional training path of government departments, universities, enterprises and scientific research institutes. The purpose of artificial intelligence multi-cloud scheduling applied talent training is to build an artificial intelligence multi-cloud scheduling applied talent training influencing factor index system. Then, the DEMATEL method is used to establish multiple correlation matrices according to the direct influence correlation between the factors and calculate the degree of influence, the degree of being influenced, the center degree and the cause degree of the factors. Using the improved AISM method, based on the idea of game confrontation, from the two opposite extraction rules of result priority and cause priority, a group of confrontation level topological maps with comprehensive influence values reflecting the interacting factors are obtained, and relevant suggestions are presented to provide a reference for the training of artificial intelligence multi-cloud scheduling applied talent.

Security service edge: choosing the right solution

Security service edge (SSE) solutions are designed to address the fundamental security challenges that organisations face in the era of hybrid work, applications in multi-cloud environments, edge computing, and digitisation of their operational technology (OT). But how do you select the right solution?

Architectural based Resilience Modelling of Multi Cloud Computing Systems

Architectural based Resilience Modelling of Multi Cloud Computing Systems

Task Scheduling for Multi-Cloud Computing Subject to Security and Reliability Constraints

The rise of multi-cloud systems has been spurred. For safety-critical missions, it is important to guarantee their security and reliability. To address trust constraints in a heterogeneous multi-cloud environment, this work proposes a novel scheduling method called matching and multi-round allocation (MMA) to optimize the makespan and total cost for all submitted tasks subject to security and reliability constraints. The method is divided into two phases for task scheduling. The first phase is to find the best matching candidate resources for the tasks to meet their preferential demands including performance, security, and reliability in a multi-cloud environment; the second one iteratively performs multiple rounds of re-allocating to optimize tasks execution time and cost by minimizing the variance of the estimated completion time. The proposed algorithm, the modified cuckoo search (MCS), hybrid chaotic particle search (HCPS), modified artificial bee colony (MABC), max-min, and min-min algorithms are implemented in CloudSim to create simulations. The simulations and experimental results show that our proposed method achieves shorter makespan, lower cost, higher resource utilization, and better trade-off between time and economic cost. It is more stable and efficient.

PrEstoCloud

Fog computing extends multi-cloud computing by enabling services or application functions to be hosted close to their data sources. To take advantage of the capabilities of fog computing, serverless and the function-as-a-service (FaaS) software engineering paradigms allow for the flexible deployment of applications on multi-cloud, fog, and edge resources. This article reviews prominent fog computing frameworks and discusses some of the challenges and requirements of FaaS-enabled applications. Moreover, it proposes a novel framework able to dynamically manage multi-cloud, fog, and edge resources and to deploy data-intensive applications developed using the FaaS paradigm. The proposed framework leverages the FaaS paradigm in a way that improves the average service response time of data-intensive applications by a factor of three regardless of the underlying multi-cloud, fog, and edge resource infrastructure.

A Comparative Analysis of Public Cloud Platforms and Introduction of Multi-Cloud

Cloud computing is an accepted widely, emerging paradigm for its ‘pay as you go’ approach, massive economies of scale, and global in minutes concept. Over the years, different cloud providers have emerged with various services to meet the requirements of the end-user. Because of an increase in the diversity of services, the complexity increases. Customers cannot decide the optimal service to fulfill their requirements. This paper provides a comparative analysis of services of top public cloud providers namely, AWS, GCP, Oracle, and Microsoft Azure. Public cloud-provider strives to be efficient in every technological aspect, though some are better for certain tasks than others. This paper, as a solution, introduces the concept of Multi-Cloud computing, to leverage the benefits of the different cloud providers and to maximize their utility in single network architecture.

Effective Method for Managing Automation and Monitoring in Multi-cloud Computing: Panacea for Multi-cloud Security Snags

Effective Method for Managing Automation and Monitoring in Multi-cloud Computing: Panacea for Multi-cloud Security Snags

Priority Intensed Meta Task Scheduling Algorithm for Multi Cloud Environment (PIMTSA)

Cloud Computing plays a momentous role for the speedy computation and provides resources to the user on-demand. The conventional single cloud has assorted number of confrontations and they are named as vendor lock-in, resource un-availability, latency, etc. To resolve these conflicts, multi cloud computing is utilized to provide heterogeneous services to the cloud users on demand. Task scheduling is a mechanism used to assign user requests to the cloud server. The proposed PIMTSA algorithm works well for the independent batch of tasks in the static environment. The algorithm is constituted to miniaturize the makespan of the multi cloud systems. It is designed for the batch mode offline multi cloud systems. The priority based approach works to cater the needs of the cloud user. High priority and low priority tasks are the two different priority levels defined by the user. High priority tasks are given more importance and are executed in the high speed VM with minimum completion time. But, the High priority tasks with maximum execution time are given more priority. After the execution of the high priority tasks, the low priority tasks with minimum execution time are allocated to VMs with minimum completion time in the clouds. This algorithm provides a high reliable and scalable computing service to the user with minimum time. In future this work can be extended for dynamic multi cloud environment.

Security and Privacy in Multi-Cloud Environments

In the modern-day panorama of computing, the adoption of multi-cloud environments has emerge as increasingly more commonplace, providing businesses stronger flexibility, scalability, and resource optimization. However, this proliferation introduces complex demanding situations related to safety and privateness. This research makes a speciality of examining the intricacies of protection and privacy concerns within multi-cloud setups, mainly addressing demanding situations and featuring solutions inside the geographical regions of facts encryption, identification control, and compliance. A important mission in multi-cloud environments is the implementation of powerful statistics encryption strategies throughout numerous systems. Protecting data confidentiality and integrity as it traverses distinctive cloud offerings is of paramount importance. This have a look at seriously evaluates present encryption methodologies, assesses their suitability in multi-cloud scenarios, and indicates progressive tactics to reinforce records safety. Identity control emerges as another important factor of the multi-cloud safety paradigm. The secure orchestration of user identities throughout disparate cloud services needs cautious exam. This studies explores the complexities surrounding identification federation, authentication mechanisms, and get entry to controls inside multi-cloud environments. By figuring out vulnerabilities and proposing strong identity management solutions, the study contributes to the improvement of secure multi-cloud infrastructures. Additionally, making sure compliance with regulatory frameworks and industry standards poses a powerful venture in multi-cloud protection. The examine scrutinizes the complex panorama of compliance necessities, encompassing diverse geographical and enterprise-specific policies. By analyzing the gaps and intricacies in present compliance frameworks, the research proposes a comprehensive method to make sure adherence to guidelines whilst operating in a multi-cloud environment. In conclusion, this studies addresses the challenges and solutions inherent in ensuring strong protection and privacy measures within multi-cloud environments. By specializing in facts encryption, identity management, and compliance, the observe provides valuable insights for companies navigating the complex terrain of multi-cloud computing.

Secure medical health care content protection system (SMCPS) with watermark detection for multi cloud computing environment

The aim of this work is to introduce a new secure system for Medical Healthcare records. The proposed design influences cloud infrastructures to give cost competence, fast exploitation, scalability, and flexibility to have room for unstable workloads. The research system could be utilized to safeguard diverse medical health care records. The proposed Secure Medical Healthcare Content Protection System (SMCPS) system contains of two major steps: (i) Hybrid Key Generation (HKG) and (ii) storage management. In the initial step of the work, keys values are created for medical healthcare records by using two encryption techniques such as Advanced Encoding Standard (AES) cryptography and Message Digest (MD5) algorithm for safe multiparty computation. This proposed HKG algorithm is different from usual key generation algorithm, since the proposed HKG algorithm adds the key values of AES and MD5. New key value is created from these two algorithms, thus increases the security level of the system. The HKG algorithm creates dynamic and representative keys of medical healthcare records, it is computationally resourceful to calculate and contrast in addition it needs small storage. In the second step of the work, Compressive Sensing (CS) based framework for storage management of medical healthcare records. The distributed matching engine achieves greater adaptability and it is created to among different healthcare records gathered from UCI machine learning vault for 100 patients. The applicability is further examined for the applicability of sparse coding-based frameworks including the various numbers of medical healthcare records which influences the ability of Multi Cloud Computing (MCC) environment. The experimentation work are performed by using Miscellaneous database for watermark pattern and any medical healthcare records are taken as input in MCC environment results outcomes prove that safe watermark recognition in the CS domain is possible for medical healthcare records are implemented in JAVA environment. The proposed work provides a CS based watermark recognition which influences safe MCC environment. The results are measured as in terms of Peak Signal to Noise Ratio (PSNR), the Mean Square Error (MSE) and the Structural Similarity Index (SSIM), security and communication cost.

Role of Management Centre in Multi Cloud Computing System

Role of Management Centre in Multi Cloud Computing System

Rate aware Meta task Scheduling Algorithm for multi cloud computing (RAMTSA)

Multicloud attains a larger momentum by gratifying the needs of the more number of heterogeneous cloud users. The inherent parts that are used in the multicloud systems are analogous, scheduling and rescheduling. In the analogous aspect, requests from the first user are given more priority and tasks are delegated to the virtual machines. In the scheduling phase, round robin based mechanism is used to accomplish the tasks in all the clouds. Rescheduling is a mechanism of shifting the executed tasks of one cloud to the another cloud. The expenditure of exceution of the tasks around 15 % are merely reduced in the rescheduling phase. This article is merely focused to dwindle the exceution costs of the VMs in the clouds. The denouement of the propounded algorithm manifests better when it is compared with conventional min-min algorithm.

Autonomous multimedia cluster computing based on Cooperative Cognition data behavior measurement under multi cloud computing

In order to meet the needs of multimedia communication in multi cloud environment and improve the experience quality of mobile multimedia users, based on multi cloud computing, based on Cooperative cognitive data behavior measurement, this paper proposes an autonomous multimedia cluster computer system and its architecture. First of all, dispersed edge clouds are distributed and integrated, and cooperate to provide multimedia storage and computing functions. In cloudy environment, a hierarchical access service point is designed between edge cloud and core cloud. On this basis, a multimedia cluster computing system suitable for cloudy environment is built. Secondly, a mulch-dimensional mapping mechanism is built between the link management interface array and the task scheduling array in the edge cloud array. Mulch-dimensional multimedia data and real-time task scheduling cooperation cognition, and core cloud are used to interact with DP vectors with dedicated channels. On this basis, we propose an autonomous computer system based on Cooperative Cognition and multimedia data behavior measure. Finally, it is analyzed by three groups of experiments. The resource utilization of the multimedia cluster computing system, the behavior measurement accuracy based on the cooperative cognitive multimedia data behavior measure and the performance of the proposed autonomous multimedia cluster computing (AMC-CCC) in large-scale real-time multimedia communication applications. The results show that the proposed AMC-CCC mechanism has excellent performance in multimedia QoS, resource management and data behavior measurement.

Load Balancing in Multi Cloud Computing Environment with Genetic Algorithm

Cloud is a pool of resources that is available on pay per use model. It provides services to the user which is increasing rapidly. Load balancing is an issue because it cannot handle so many requests at a time. It is also known as NP complete problem. In traditional system the functions consist of various parameter values to maximise it in order to achieve best optimal individualsolutions. Challenge is when there are many parameters of solutionsin the system space. Another challenge is to optimize the function which is much more complex. In this paper, various techniques to handle load balancing virtually (VM) as well as physically (nodes) using genetic algorithm is discussed.

A strategic performance of virtual task scheduling in multi cloud environment

An emerging technology of cloud computing has major issue as scheduling the task and resource allocating. To avoid this issue, there are suggestions on different scheduling algorithm and that has its own merits and demerits. The proposed virtual task scheduling in multi cloud environment using three different scheduling algorithms such as equal load balancing (ELB) algorithm, high priority scheduling algorithm and rate based scheduling (RBS) algorithm. These different scheduling algorithms are used based on the number of tasks and number of virtual machine in multi cloud environment architecture. If the number of task is equal to number of virtual machine, ELB scheduling algorithm is used. If the number of tasks is greater than the number of virtual machine, high prioritization scheduling algorithm is used. If the number of tasks are lesser than the number of virtual machines, RBS algorithm is used. By using these three different scheduling algorithms we can improve the makespan, average efficiency of the multi cloud computing. Simulation results have analyzed the overall performance by comparing these three different scheduling algorithms. From these simulation results, proposed virtual task scheduling which increases the makespan and reduces the delay and energy consumption.