私有云是企业架构的改变

众所周知,云计算分为公有云,私有云和混合云。对于企业云构架来说,公有云与私有云的差异是对云服务的用户开放程度不同,而企业用户的最终形态将是混合云。     

混合云中基于截止时间和费用约束的调度方法研究*

针对混合云调度中私有云利用率不高和公有云费用偏高的问题,基于性能和费用目标提出了两个调度策略—截止时间优先和费用优先策略,建立了混合云中的任务和资源模型,能够根据用户提交的任务需求自适应选择合适的调度资源,对截止时间要求比较高的任务可以优先调度至公有云,对费用要求高的任务可以优先调度至私有云,而且两种策略均满足截止时间和一定的费用约束,因此相对于其它类似的基准调度方法,本文的两种调度策略在调度完成时间、费用、截止时间超出率和私有云利用率等方面均有很好的表现,尤其是当任务量比较大的时候,两种调度策略表现出更好的自适应性和优势。     

2021私有云平台创新排行榜

云技术自出现到今日的繁荣发展已经很多个年头了,这期间,存储和网络带宽的提升都为云技术的发展铺平了道路,时至今日,私有云作为一种新的信息应用技术,正在为当代企业提供新的动力. 所谓私有云,是能够帮企业快速构建云计算基础架构的系统软件,通过互联网或内部网络仅面向特定用户提供计算服务,有时候也叫作内部云或者公司云.相对于公有云,私有云不仅具有公有云的优势,如自主服务、可伸缩性和弹性等,也具备公有云无法提供的优势,可以提供专用资源的额外控制和定制能力,可以具备更高的安全性和隐私性. 近几年,私有云系统平台在云服务市场中的地位持续上升,可以预见,云服务市场在不久的将来将会进一步扩大,私有云成为新兴企业不可或缺的一部分.在当下,私有云平台已经基本形成规模.     

基于混合云的企业信息平台架构研究

本文基于混合云,探索企业信息平台的架构问题。由于私有云的处理能力有限,在遇到季节性的峰值需求时难以应对;此时,公有云是一种很好的选择,其处理能力远远超出私有云,由于其规模效应,短期而言,成本也低于私有云。然而,将敏感的数据存放到公有云上存在一定的安全隐患,而且,将应用系统部署到公有云上,跨云的负载均衡也存在一定的技术复杂度。本文结合私有云与公有云的优势,给出一种企业信息平台的参考架构,并进行应用系统的负载均衡、数据的安全和混合云互操作性的探讨。     

对搭建企业源代码安全测试云平台的研究

近年来,云计算作为新兴的计算机技术正在蓬勃发展.云计算在提高资源利用率、提高工作效率、降低配置要求等方面有显著作用.云计算分为私有云、公有云和混合云3种方式,从私有云形式对企业安全测试云平台的搭建进行研究,介绍了私有云源代码安全测试平台的架构及其功能.     

云计算解决方案供应商分类大全

正尽管以Amazon为代表的互联网公司在云计算市场上屡获大单,但很多企业却出于安全、成本的考虑,依然选择自建私有云。那么,在上百家云计算解决方案供应商面前,企业该如何作出选择呢?公有云、私有云、混合云,到底选择哪个云?——对于这个问题,业界已经形成共识,三种云分别对应不同的最佳应用场景:公有云"快、准、狠",即资源部署快,规模灵活调配,但是相同的IT资源能力下使用成本高,适用于对IT资源有弹性需求的     

基于QoS模型感知的云作业调度算法

为了提高云作业调度的成功率,保证用户的服务质量,提出了一种基于QoS模型感知的云作业调度算法。首先从作业分为大作业和小作业两种,小作业直接在私有云平台中完成调度,然后将大作业在混合云中台上进行调度,并通过权值较小的子任务优选部署到公有云上,最后采用仿真测试实验检验算法的性能。仿真结果表明,相对于其他云作业调度算法,该算法提高了作业调度的成功率,缩短了作业的实际完工时间,获得了满足用户服务质量的云作业调度结果。     

存储与虚拟化备份的关系日益紧密

有关调查预计,到2020年CIO一半的工作将在公有云上完成;同时用户保护虚拟环境的方式也发生转变,到2016年他们侧重于通过硬件快照或复制方式而非传统的备份方式进行。客户云备份应用大致分为三种模式,一是专门构建私有云;二是公有云和私有云同步做架构混合云平台。三是客户备份恢复完全采用公有云。     

云计算中基于可转换代理签密的可证安全的认证协议

云用户与公有云之间的双向认证是云计算中用户访问公有云的重要前提.2011年,Juang等首次提出了云计算环境下采用代理签名的认证协议,其优点是用户只需到私有云中注册,然后在私有云的帮助下通过公有云的认证.但是,该方案存在3个缺陷:1)为保护用户的隐私,每次会话都需更新用户公钥;2)当私有云中的许多用户同时登录不同的公有云时,私有云会遭遇网络拥堵;3)用户的私有云与访问的公有云之间需要预先共享秘密.为弥补上述不足,提出了一种保护用户隐私的可证安全的可转换代理签密方案,基于该方案设计了一种一轮云计算认证协议.新方案的优点在于用户向私有云注册后,就能通过公有云的认证,而不需要私有云的帮助,并且它还能保护用户的隐私性、抗抵赖性.协议不需要在每次会话开始前更新用户公钥,同时私有云与访问的公有云之间不再需要预先共享秘密.在随机谕言机模型下证明了新协议的安全性,并且比较说明新协议在效率方面优于Juang等的协议.     

混合云安全策略研究

安全问题一直阻碍着企业将业务迁移到公有云中.混合云结合了公有云和私有云的优点,越来越受到企业关注,但同时也引入其特有的安全问题.分析了混合云密钥管理、数据加密和跨云认证等问题,并提出了相关安全防护策略.     

Adaptive scheduling for parallel tasks with QoS satisfaction for hybrid cloud environments

A hybrid cloud integrates private clouds and public clouds into one unified environment. For the economy and the efficiency reasons, the hybrid cloud environment should be able to automatically maximize the utilization rate of the private cloud and minimize the cost of the public cloud when users submit their computing jobs to the environment. In this paper, we propose the Adaptive-Scheduling-with-QoS-Satisfaction algorithm, namely AsQ, for the hybrid cloud environment to raise the resource utilization rate of the private cloud and to diminish task response time as much as possible. We exploit runtime estimation and several fast scheduling strategies for near-optimal resource allocation, which results in high resource utilization rate and low execution time in the private cloud. Moreover, the near-optimal allocation in the private cloud can reduce the amount of tasks that need to be executed on the public cloud to satisfy their deadline. For the tasks that have to be dispatched to the public cloud, we choose the minimal cost strategy to reduce the cost of using public clouds based on the characteristics of tasks such as workload size and data size. Therefore, the AsQ can achieve a total optimization regarding cost and deadline constraints. Many experiments have been conducted to evaluate the performance of the proposed AsQ. The results show that the performance of the proposed AsQ is superior to recent similar algorithms in terms of task waiting time, task execution time and task finish time. The results also show that the proposed algorithm achieves a better QoS satisfaction rate than other similar studies.     

Elastic resource provisioning in hybrid mobile cloud for computationally intensive mobile applications

The paper addresses the integration of hybrid cloud with mobile applications. The challenge about hybrid mobile cloud resource provisioning is the trade-offs between energy consumption, performance provided to users and how resources, such as processing power and network, are being utilized. The proposed elastic hybrid mobile cloud resource provisioning model is jointly optimized to improve mobile user experience within the constraints of available resources and user QoS requirement. The paper presents the system utility of hybrid cloud system involving local cloud and public cloud infrastructure. From the perspectives of both mobile applications and cloud providers, the proposed system utility is optimized to improve the performance of mobile applications and the utilization of cloud resources. The proposed elastic hybrid mobile cloud resource provisioning algorithm includes two sub-algorithms. To evaluate and validate performance of the proposed algorithm, a series of experiments are conducted. The comparison results and analyses are discussed. The experimental results show the improvement to previous works.     

Economic aspects of hybrid cloud infrastructure: User?organization perspective

Adoption of cloud infrastructure promises enterprises numerous benefits, such as faster time-to-market and improved scalability enabled by on-demand provisioning of pooled and shared computing resources. In particular, hybrid clouds, by combining the private in-house capacity with the on-demand capacity of public clouds, promise to achieve both increased utilization rate of the in-house infrastructure and limited use of the more expensive public cloud, thereby lowering the total costs for a cloud user organization. In this paper, an analytical model of hybrid cloud costs is introduced, wherein the costs of computing and data communication are taken into account. Using this model, a cost-efficient division of the computing capacity between the private and the public portion of a hybrid cloud can be identified. By analyzing the model, it can be shown that, given fixed prices for private and public capacity, a hybrid cloud incurs the minimum costs. Furthermore, it is shown that, as the volume of data transferred to/from the public cloud increases, a greater portion of the capacity should be allocated to the private cloud. Finally, the paper illustrates analytically that, when the unit price of capacity declines with the volume of acquired capacity, a hybrid cloud may become more expensive than a private or a public cloud.     

Cost model based service placement in federated hybrid clouds

As cloud federation allows companies in need of computational resources to use computational resources hosted by different cloud providers, it reduces the cost of IT infrastructure by lowering capital and operational expenses. This is the result of economies of scale and the possibility for organizations to purchase just as much computing and storage resources as needed whenever needed. However, a clear specification of cost savings requires a detailed specification of the costs incurred. Although there are some efforts to define cost models for clouds, the need for a comprehensive cost model, which covers all cost factors and types of clouds, is undeniable. In this paper, we cover this gap by suggesting a cost model for the most general form of a cloud, namely federated hybrid clouds. This type of cloud is composed of a private cloud and a number of interoperable public clouds. The proposed cost model is applied within a cost minimization algorithm for making service placement decisions in clouds. We demonstrate the workings of our cost model and service placement algorithm within a specific cloud scenario. Our results show that the service placement algorithm with the cost model minimizes the spending for computational services.     

Online cost-rejection rate scheduling for resource requests in hybrid clouds

The hybrid cloud computing model has attracted considerable attention in recent years. Due to security and controllability of private cloud, some resource requests are required to be scheduled in private parts of hybrid cloud. However, these requests could be rejected because of the resource limitation of private parts of hybrid cloud. In this paper, all resource requests are classified into two categories, i.e., the special requests required to process in private cloud, and the normal requests insensitive in private or public clouds. By considering the normal requests undivided to dispatch a cloud and divided to dispatch different clouds, we propose the online cost-rejection rate scheduling strategy (OCS) for the normal requests undivided, and the online scheduling strategy for some normal requests divided (OCS-divided), which could make suitable requests placement decisions in real-time and minimize the cost of renting public cloud resources with a low rate of rejected requests. Then, we transform both online models into two one-shot optimization problems by taking advantage of Lyapunov optimization techniques, and employ the optimal decay algorithm to solve the one-shot problems. The simulation results demonstrate that our scheduling strategies can achieve the trade-off between cost and rejection rate, and process the real-time resource requests in hybrid clouds. OCS-divided can achieve an average cost saving of about 25% compared with OCS and maximize the resource utilization.     

Failure-aware resource provisioning for hybrid Cloud infrastructure

Hybrid Cloud computing is receiving increasing attention in recent days. In order to realize the full potential of the hybrid Cloud platform, an architectural framework for efficiently coupling public and private Clouds is necessary. As resource failures due to the increasing functionality and complexity of hybrid Cloud computing are inevitable, a failure-aware resource provisioning algorithm that is capable of attending to the end-users quality of service (QoS) requirements is paramount. In this paper, we propose a scalable hybrid Cloud infrastructure as well as resource provisioning policies to assure QoS targets of the users. The proposed policies take into account the workload model and the failure correlations to redirect users’ requests to the appropriate Cloud providers. Using real failure traces and a workload model, we evaluate the proposed resource provisioning policies to demonstrate their performance, cost as well as performance–cost efficiency. Simulation results reveal that in a realistic working condition while adopting user estimates for the requests in the provisioning policies, we are able to improve the users’ QoS about 32% in terms of deadline violation rate and 57% in terms of slowdown with a limited cost on a public Cloud.     

Optimal scheduling across public and private clouds in complex hybrid cloud environment

The hybrid cloud extends the private cloud model by using both local and remote resources. The private cloud will rely on the resources leased from public cloud providers for the execution of private cloud applications. The paper presents optimal scheduling across public and private clouds in complex hybrid cloud environment. The contributions of this paper have three aspects. 1) The proposed hybrid cloud scheduling policy considers the benefits of private cloud applications and public cloud provider, it can adapt to the changes in the system to find the scheduling optimization. The scheduling optimization is decomposed and conducted across the private cloud and public cloud. 2) Secondly, The paper describes negotiations in hybrid cloud marketplace and gives an example to explain how these rules are resolved by the cloud marketplace. 3) Thirdly, the paper proposes an optimal scheduling algorithm across public and private clouds. The paper also describes negotiations in hybrid cloud marketplace and gives an example to explain how these rules are resolved by the cloud marketplace. In the simulations, the profit of public cloud provider and resource utilization of the proposed algorithm are better than other related works.     

Optimization–Simulation Model for Planning Supply Transport to Large Infrastructure Public Works Located in Congested Urban Areas

This article proposes an optimization–simulation model for planning the transport of supplies to large public infrastructure works located in congested urban areas. The purpose is to minimize their impact on the environment and on private transportation users on the local road network. To achieve this goal, the authors propose and solve an optimization problem for minimizing the total system cost made up of operating costs for various alternatives for taking supplies to the worksite and the costs supported by private vehicle users as a result of increased congestion due to the movement of heavy goods vehicles transporting material to the worksite. The proposed optimization problem is a bi-level Math Program model. The upper level defines the total cost of the system, which is minimized taking into account environmental constraints on atmospheric and noise pollution. The lower level defines the optimization problem representing the private transportation user behavior, assuming they choose the route that minimizes their total individual journey costs. Given the special characteristics of the problem, a heuristic algorithm is proposed for finding optimum solutions. Both the model developed and the specific solution algorithm are applied to the real case of building a new port at Laredo (Northern Spain). A series of interesting conclusions are obtained from the corresponding sensitivity analysis.