Multi Objective Data Transformation in Hybrid Clouds Networks for Offloading Data

Deadline-constrained cost-aware workflow scheduling in hybrid cloud

The development of cloud computing technology has been continuously growing since its invention and has attracted the attention of many researchers in the academia and the industry, particularly during the recent years. The majority of organizations, whether large corporate businesses or typical small companies, are moving towards employing this cutting edge technology. Using private cloud provides low cost and privacy for workflow applications execution. However, an organization’s requirements to high performance resources and high capacity storage devices lead them to utilize public clouds. Public cloud leases information technology services in the form of small units and in larger scale compared to private cloud, but this model is potentially exposed to the risk of data breach and is less secure in comparison to a pure private cloud environment. The combination of public and private clouds is known as hybrid cloud, where workflow tasks can be executed on resources residing on either public or private clouds. The objective of this paper is to present a scheduling algorithm for maintaining data privacy in workflow applications, such that the budget is minimized, while the makespan limitation imposed by the user is satisfied.

As businesses navigate the complexities of modern IT infrastructure, hybrid cloud solutions have emerged as a compelling choice, blending the strengths of both public and private cloud environments. The increased adoption of hybrid cloud is driven by the need for flexibility, scalability, and enhanced security, allowing organizations to optimize their operations and adapt to changing demands. By leveraging the best of both worlds, companies can maintain sensitive data on private clouds while taking advantage of the expansive resources and cost efficiencies offered by public clouds. This balanced approach not only ensures business continuity and disaster recovery but also supports the seamless integration of legacy systems with cutting-edge cloud technologies. Moreover, the hybrid cloud model offers organizations the agility to innovate rapidly, respond to market changes, and meet regulatory requirements without compromising performance or security. As more businesses recognize the strategic benefits of hybrid cloud, its adoption is expected to accelerate, making it a cornerstone of digital transformation initiatives. This article explores the rising trend of hybrid cloud adoption, highlighting its key advantages, challenges, and the role it plays in shaping the future of enterprise IT.

Cloud computing is one of the emerging technologies in last decade; this technology provides a service model to organizations and public users. Organization users and developers, they start and maintain their organization without any hardware and software infrastructure they can develop their company with the help of cloud technology. Cloud deployment is categorized into three types, private, public and hybrid cloud environment and here public and private cloud more secure comparatively hybrid cloud because when data is moving from private to public cloud security problem is occur. Data communication is most important task in a network environment so the proposed model is focus on data security in hybrid cloud environment. In a hybrid cloud the participation of private cloud and its nodes very important after that the data transmission is depends on active node, In this node selection is based on genetic algorithm. The result of proposed model is going to compare with private and public cloud security parameters.

In cloud computing, private cloud tends to exhibit high controllability but lack scalability, whereas public cloud is just the opposite. The hybrid cloud formed by combining them can effectively balance controllability and scalability, and has been widely adopted in industry for executing workflows. The network connecting public and private clouds goes through Internet and its bandwidth is rather limited in comparison with that inside clouds. Hence, cross-cloud data transmission can become a bottleneck of executing workflows in such environment. Moreover, multiple data communications may contend for bandwidth resources, thereby incurring delay. To address these concerns, this work establishes a scheduling model for workflow execution across public and private clouds, where a queueing mode is innovatively employed to address potential network communication contention. A contention-cognizant list scheduling (CCLS) heuristic equipped with task duplication is devised to minimize workflow makespan. It adopts a novel task sorting attribute to schedule tasks and cross-cloud data communications by using available computation and communication resources, and employs task duplication to obviate the needs for certain data communications. Experiments are conducted with realistic workflows and diverse settings, and the results verify the superiority of CCLS over the existing ones as it can always achieve the best makespan <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —A crucial challenge for workflow scheduling across public and private clouds is that the cross-cloud bandwidth resource is relatively limited and multiple data communications may contend for it in practice. However, this communication contention issue has largely been neglected in existing investigations. To advance the state of the art, this paper proposes a communication contention-cognizant scheduling approach based on a queueing mode to minimize the makespan for workflow execution across public and private clouds. The proposed approach can be readily put into use and experimental results show that it performs better than traditional scheduling approaches that fail to consider communication contention.

Workflows have been used to represent a variety of applications involving high processing and storage demands. As a solution to supply this necessity, the cloud computing paradigm has emerged as an on-demand resources provider. While public clouds charge users in a per-use basis, private clouds are owned by users and can be utilized with no charge. When a public cloud and a private cloud are merged, we have what we call a hybrid cloud. In a hybrid cloud, the user has elasticity provided by public cloud resources that can be aggregated to the private resources pool as necessary. One question faced by the users in such systems is: Which are the best resources to request from a public cloud based on the current demand and on resources costs? In this paper we deal with this problem, presenting HCOC: The Hybrid Cloud Optimized Cost scheduling algorithm. HCOC decides which resources should be leased from the public cloud and aggregated to the private cloud to provide sufficient processing power to execute a workflow within a given execution time. We present extensive experimental and simulation results which show that HCOC can reduce costs while achieving the established desired execution time.

Cloud computing has caused a revolution in how companies do business giving them flexibility, scalability, and cost-efficiency like never before. It lets organizations use computing resources through the internet instead of relying on the usual in-house setup making cloud computing a key part of today's IT plans. From new startups to big global companies, businesses are using cloud solutions more and more to boost their ability to adapt cut down on running costs, and come up with new ideas. The cloud ecosystem is big and varied, with different deployment models and service types to suit various business needs. Public, private, and hybrid clouds are the three main deployment models each with its own pros and cons. public clouds, like those from Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform, offer affordable scalability and global reach. Private clouds, on the other hand, are for organizations that have strict security or compliance needs giving them dedicated environments. Hybrid clouds mix the best parts of both letting businesses find a balance between security and scalability by combining private and public cloud resources

Universities all around the world play a vital role in improving the society. But in the current economic crisis, they are facing difficulties in providing necessary resources for research and educational purposes. The solution of this problem is in the use of Cloud Computing. It is a new distributive computing model which provides applications and services over the Internet. In this paper, we investigate, study, and simulate a cloud for a university environment. We propose an architecture based on the hybrid cloud model which uses both the public and private clouds. It consists of two main parts, i.e., the Cloud Management System and the Hybrid Cloud. The proposed architecture is simulated using CloudSim. The evaluation of this architecture is performed for two separate scenarios. In the first one, the number of tasks is kept constant; and in the second, the number of tasks is changed. The results obtained are encouraging and support the use of a hybrid cloud solution for a university. The public cloud is used only to get a better performance or when the load is too high for the private cloud. The results show that high performance can be obtained while keeping the cost low.

After years of development, cloud computing has developed into public cloud, private cloud, community cloud, hybrid cloud and other cloud forms. However, the government, financial and other industries have more concerns about security, operation and maintenance, cost and other issues in the process of cloud. In this regard, the industry, academia and other parties have launched the frontier exploration, give different solutions. To meet users’ new needs, we proposed a new model that combines the advantages of public and private clouds. It is called proprietary cloud. Proprietary cloud provides users with more efficient management by incorporating years of experience of public cloud providers. On the one hand, it meets the rigid needs of users’ independent control and exclusive resources, on the other hand, it reduces the maintenance cost and downtime probability of users, and achieves the desired effect of users. It mainly includes self-built model and exclusive model deployment modes. The main difference is whether to accept public cloud management. This paper introduces and analyzes proprietary cloud in detail from the aspects of deployment architecture, mode differences and common deployment patterns. It concludes that proprietary cloud is an indispensable cloud computing deployment pattern in the future.KeywordsCloud computingDigital transformationProprietary cloud

Cloud computing is a Service Oriented Architecture which reduced information technology overhead for the end-user and provide great flexibility, reduced total cost of ownership, on-demand services and many other things [1]. Hence it delivered all IT related capability as services rather than product .Services on cloud are divided into three broad categories: Software as a Service, Infrastructure as a Service & Platform as a Service. Same as services cloud is also classified as Private Cloud, Public Cloud & Hybrid Cloud. Private cloud is increasingly become popular between every private organization either large or small wants .To deploy public or private cloud there are many open source software available some are Eucalyptus, Nimbus, OpenStack, Open Nebula Cloud stack and Amazon Web Services .This paper, I provide a study of the main open source software such as Eucalyptus, OpenStack and OpenNebula for cloud implementation. It is believe that the comparison presented in this paper would benefit developers in selecting best open source Software.

Cyber laboratory for FPGA design experiments combines the actual laboratory and the remote laboratory in a seamless way in a hybrid cloud environment. The hybrid cloud consists of on premise private cloud to support device dependent services and a public cloud to support rest of the laboratory services. The on premise private cloud allows concurrent use of device dependent tasks, such as FPGA run services, by making use of the Web services. The elastic nature of the public cloud is suitable for coping with dynamically changing student experimental usages. The number of servers in the public cloud can be scale out or shrink according to the number of simultaneous students accesses. Expensive license CAD tools, such as Verilog-HDL synthesis tools, are allocated for a limited number of the servers in the public cloud and the private cloud, where Verilog-HDL synthesis Web services are realized to achieve remote sharing of these tools. For remote laboratory case, student virtual machines are allocated in the public cloud and remotely issue the FPGA run services and the Verilog-HDL Synthesis services. The existing faculty file/database and education support system serves as an adhesive agent to combine the on premise cloud and the public cloud to minimize the data transfer among them and to perform student VM migrations between remote and actual laboratories. The Cyber Laboratory achieves an efficient use of laboratory platforms and can reduce TCO (total cost of ownership), as the public cloud can cope with the dynamically changing experimental loads.

With the development of cloud computing, hybrid cloud has attracted extensive attention. It is a flexible collaboration of private clouds and public clouds, which can meet the increasing demands of energy consumption while reduce overhead. However, the clouds in hybrid clouds have to share data with each other. Therefore, hybrid connection mechanism remains a big issue. VPN is a common solution used as a bridge for connecting private clouds and public clouds. Using VPN to construct a direct connected network is a simple solution to implement where all the clients in hybrid cloud connect to a specified OpenVPN server residing in public cloud. Unfortunately, it will result in many problems impacting the efficiency of network. In this paper, we propose a proxy based virtual network solution. To compare the network QoS of the proxy based virtual network solution to the direct connected virtual network solution, we designed three controlled experiments measuring the bandwidth and delay time of the intra-cloud and inter-cloud communication. Then we compare the network performance of direct connected virtual network solution and proxy based virtual network with a Hadoop application on a hybrid cloud environment. The experiment result indicates that the proxy based virtual network solution outperforms the direct connected virtual network solution.

As cloud computing becomes prevalent, more and more sensitive information is being centralized into the cloud, which raises a new challenge on how to efficiently share the outsourced data in a fine-grained manner. Although searchable encryption allows for privacy-preserving keyword search over encrypted data in public cloud, it could not work effectively for supporting fine-grained access control over encrypted data simultaneously. In this paper, we consider to tackle the challenge above under a hybrid architecture in which a private cloud is introduced as an access interface between users and public cloud. We firstly propose a basic scheme allowing both exact keyword search and fine-grained access control over encrypted data. Furthermore, an advanced scheme supporting fuzzy keyword search is presented. In both schemes, overhead computation is securely outsourced to private cloud but only left behind the file encryption and decryption at user side. Finally, we demonstrate approaches to realize outsourcing cryptographic access control mechanism and further relieve the computational cost at user side.KeywordsCloud ComputingKeyword SearchPublic CloudPrivate CloudEncrypt DataThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The workloads of social computing are tremendous, bursty and unpredictable. A hybrid cloud, which combines a private cloud and a public cloud, is a promising solution to processing the workloads. For most corporations, they leverage one public cloud. However, with fierce competition among public cloud providers, public cloud services change frequently, which may lead to service unavailability and a less cost-effective hybrid cloud solution. As a result, leveraging multiple public clouds in the hybrid cloud is a potential solution. In this paper, we identify such a problem in current hybrid cloud and analyze the necessity of load balancing for hybrid cloud applications. Focusing on cost minimization and performance guarantee, we propose a Least Cost per Connection (LCC) algorithm so as to choose the most cost-effective clouds along with adapting changes among multiple public clouds. The simulation results show that our solution can significantly decrease the outsourcing cost as well as guarantee Quality of Service (QoS) of applications.

Infrastructures for High-Performance Computing: Cloud Computing