Pattern-Based Cloud Service Recommendation and Integration for Hybrid Cloud

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.

Virtual computing resources are provided via a cloud system that is both clever and intelligent. Based on the user's request, computing resources are made available. A hybrid cloud is the best option for storing and accessing user data for cloud deployments. Maintaining security in a hybrid cloud environment is time-consuming. This study provides a novel strategy for securing data in the hybrid cloud by ensuring the user's data is protected. Users' data in a hybrid cloud is protected using cryptographic approaches provided in this approach. Using this strategy, users' data may be protected in public and private clouds using various encryption methods. The suggested data security paradigm offers various advantages to both consumers and providers in terms of data security. Three symmetric encryption methods are offered as a service in the cloud. The concept is implemented as a cloud-based application hosted in the cloud, and the effectiveness of three strategies is assessed. They are evaluated in terms of performance and security. Using the recommended encryption methods in a hybrid cloud environment is more efficient than using other methods. The proposed technique can be used for relational data. It can be modified and enhanced to process multimedia data.

ABSTRACTCloud computing is a multifarious computing paradigm incorporating the benefits of service-oriented architecture and utility computing through virtualization. Hybrid cloud, an amalgamation of two or more public and/or private clouds, is gaining high popularity between users due to various reasons involving improved performance, flexible business operations, capacity expansion, optimized costs, and enhanced security. The efficient execution of fine-grained parallel applications onto hybrid cloud system becomes limited due to a number of factors. From the application point of view, it ranges from the dynamicity of the applications to their precedence and communication constraints while for the computational resources, it includes heterogeneity of processors and participating clouds with their interconnection topology. This work proposes a compile time hybrid cloud-based task duplication strategy to execute the fine-grained applications represented as directed acyclic graph (DAG) onto the hybrid cloud environment. The proposed strategy schedules the tasks based on a degree relative to the critical path in the DAG and tries to achieve lower bound of the DAG. Furthermore, it makes an effort to avoid redundant duplication by duplicating only the required parent tasks considering the available idle slots to minimize the execution time of the application. The experimental study reveals that the proposed strategy performs better than its peers in terms of achieving the lower bound more efficiently with lesser degree of duplication for fine-grained jobs. The strategy is highly useful for cloud environment as it results in lower cost of usage of resources with enhanced system utilization.

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.

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.

Although some cloud providers look at the hybrid cloud as blasphemy, there are strong reasons for them to adopt it. Hybrid clouds offer the cost and scale benefits of public clouds while also offering the security and control of private clouds. Task scheduling, one of the most famous combinational optimization problems, plays a key role in the hybrid cloud environment. We propose a graph-based task scheduling algorithm. In order to achieve minimum cost, our algorithm takes into account not only the private resources but also the public resources. This paper also presents an extensive evaluation study and demonstrates that our proposed algorithms minimize the user's cost in a hybrid cloud environment.

Cloud computing consists of three fundamental service models: infrastructure-as-a-service, platform-as-a service and software-as-a-service. The technology “cloud computing” comprises four deployment models: public cloud, private cloud, hybrid cloud and community cloud. This chapter describes the six cloud service and deployment models, the association each of these services and models have with physical/virtual networks. Cloud service models are designed to power storage platforms, infrastructure solutions, provisioning and virtualization. Cloud computing services are developed to support shared network resources, provisioned between physical and virtual networks. These solutions are offered to organizations and consumers as utilities, to support dynamic, static, network and database provisioning processes. Vendors offer these resources to support day-to-day resource provisioning amid physical and virtual machines.

Cloud computing is a key technology for online service providers. However, current online service systems experience performance degradation due to the heterogeneous and time-variant incoming of user requests. To address this kind of diversity, we propose a hierarchical approach for resource management in hybrid clouds, where local private clouds handle routine requests and a powerful third-party public cloud is responsible for the burst of sudden incoming requests. Our goal is to answer (1) from the online service provider’s perspective, how to decide the local private cloud resource allocation, and how to distribute the incoming requests to private and/or public clouds; and (2) from the public cloud provider’s perspective, how to decide the optimal prices for these public cloud resources so as to maximize its profit. We use a Stackelberg game model to capture the complex interactions between users, online service providers and public cloud providers, based on which we analyze the resource allocation in private clouds and pricing strategy in public cloud. Furthermore, we design efficient online algorithms to determine the public cloud provider’s and the online service provider’s optimal decisions. Simulation results validate the effectiveness and efficiency of our proposed approach.

Cloud computing technology is increasingly important in the industry with the promise of increased performance, dynamic resource allocation capabilities and provides a convincing opportunity for organizations to outsource their IT infrastructure under the pay-per-use model offered by many public cloud providers. But there are concerns when selecting between the private cloud and the public cloud that must be taken into account. Many SME enterprises with scarce in-house IT support and limited knowledge about different cloud technologies encounter difficulties in making the most appropriate choice on 'buying' a private cloud or 'renting' a public cloud service. Through a number of interviews with potential cloud adopters from the industry, this paper identifies the most relevant factors concerning cloud adoption and develops a cloud deployment model assessment method called Cloud Deployment Selection Model (CDSM). The model has been validated in real case studies, and recommendations derived have been compared with real adoption cases. Result shows it is able to accurately recommend a suitable cloud deployment model based on the factors identified from many SME organizations, an important tool for SMEs to decide between the private or the public cloud solution.

Deadline-constrained cost-aware workflow scheduling in hybrid cloud

<p>Previous studies have documented that the surface solar radiation (SSR) over most regions of China has shifted from the ‘global dimming’ since the 1950s to the ‘global brightening’ after 2005. In this paper, the potential factors that affect the annual trends of SSR over East China from 2005 to 2018 based on different satellite-derived products are analyzed. Then, due to the lack of long-term various aerosol species from observation data, the focus of this study is to calculate the contributions from direct effects of different types of cloud fraction on SSR relative to the effects of total cloud fraction over East China during the same period using a BCC_RAD radiative transfer model. The results show that clouds and aerosols are the primary factors that affect the SSR over East China from 2005 to 2018, followed by water vapor and ozone.</p><p>The annual mean all-sky SSR from 2005 to 2018 is significantly increased over the North China Plain, Northeast China, Yunnan, and Eastern Sichuan provinces, with the increases up to 0.6 W m<sup>-2</sup> yr<sup>-1</sup>. This is probably due to the combined reductions of aerosols and clouds during this period, but clouds even play a more important role over Shanxi and northern Shaanxi. Changes in aerosols dominate the increase of SSR over Hunan, Jiangxi, and Fujian provinces, whereas clouds contribute more to the decreases of SSR over Guangdong, Guangxi, Guizhou, and Zhejiang provinces. Meanwhile, the simulations indicate that the marked annual mean decreases in high cloud fraction, especially for low cloud fraction, are the main causes of simulated increases in SSR due to total cloud fraction over most regions of East China, while the increases in high, medium-high, especially for medium-low cloud fraction, play more important roles in reductions of SSR over southern China. Moreover, the direct effects of various types of cloud fraction on changes in SSR for each season are also examined. It seems that the direct effects of low cloud fraction on SSR are likely the strongest among all kinds of clouds. Take southern China as an example, the direct effects of medium-low and low cloud fraction are stronger for spring and autumn, while contributions from low cloud fraction are largest in winter. However, the combined increases in high, medium-high, medium-low cloud fraction exceed decreases in low cloud fraction, thus causing the reduction in SSR in summer. This study highlights that different types of clouds may have different impacts on SSR not only on the annual mean scale but also on seasonal scales.</p><p>Keywords: surface solar radiation, aerosols, different types of cloud fraction</p>

Over the last few years, the context of big data has gained a significant traction due to many factors. While the public cloud model had been deeply studied to face the increasing demand for large-scale data processing capabilities, many organizations are now focusing on the hybrid cloud model, where the classic scenario is enriched with a private (company owned) cloud -- e.g., for the management of sensible data. In this work, we propose HyMR, a policy to enable autonomic cloud bursting for clusters of virtual machines operating MapReduce jobs over a hybrid cloud. This policy -- together with an infrastructure level system for resource provisioning in hybrid clouds -- can be used to face the temporary (or permanent) lack of computational resources on the private cloud, allowing cloud bursting in the context of big data applications. By means of an empirical evaluation of the system scale-up/-down performance, we show that HyMR policy allows the user to significantly reduce the data-processing time.

Recently hybrid cloud solutions integrating public and private cloud is proposed to address the privacy and security concerns faced by Enterprises in their data offloading decisions. In these solutions, the transformed data is kept in public cloud while transformation keys are kept in private cloud. The existing works for data transformation used in hybrid clouds does not address multiple objectives of privacy, security, fine grained access control, utility preservation for mining and data retrieval efficiency. This work proposes a multi objective data transformation technique for hybrid cloud to address all these objectives. The proposed solution is built on attribute based hierarchical data access control with hierarchy selection based on joint consideration of security, utility preservation and retrieval efficiency. The proposed solution is able to provide 5% higher security strength, 1.34% higher clustering accuracy over perturbed data and 29.95 % higher data retrieval efficiency over perturbed data compared to existing works.

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.

Background & Objective: Detecting and mitigating Distributed Denial of Service (DDoS) attacks is a serious problem. In addition, new features and network deployments such as Software- Defined Networking (SDN) may open the door for new threats that did not previously exist. : Recent publications and patent are reviewed to find new techniques developed for integrating different mechanisms to secure networks against DDoS. Methods: This work presents a simple model for integrating different mechanisms to secure both SDN and legacy network in a hybrid cloud environment, it is called FocusON. It aims at mitigating DDoS attacks of a victim network. In addition, separating network monitoring from its control aims at mitigating DDoS attacks of a victim network. Traffic pattern analysis is apart from attack detection mechanism that gives a conceptual representation of a specific kind of DDoS attacks. DDoS detection is a completely automated process. Once called, for the reaction, the active response will be taken against the real IP source of the attacker. : The communication time overhead was tested in order to evaluate the remote server response time in case of deploying our proposed model mechanisms and without our proposed model. : Here we introduce a response mechanism that consists of an analysis of event logs, traffic patterns, and IP traceback. The proposed model categorizes the underlying network according to the location into a victim network and the source of attack (public cloud). Results & Conclusion:: The proposed model implemented in a hybrid cloud environment using the network of SDN and legacy network. The experimental setup was built using our network lab connected to the Amazon public cloud.