CMODLB: an efficient load balancing approach in cloud computing environment

The Journal of Supercomputing - A hybrid of supervised (artificial neural network), unsupervised (clustering) machine learning, and soft computing (interval type 2 fuzzy logic system)-based load...     

Robustness improvement of component-based cloud computing systems

The Journal of Supercomputing - With the increasing popularity of Cloud computing systems, the demand for highly dependable Cloud applications has increased significantly. For this, reliability and...     

云计算中基于Chord算法的研究与改进

定义了云服务器为P2P网络拓扑结构的基本节点,引进Chord协议对其进行资源定位与搜索.由于Chord算法一方面没有考虑节点异构的问题,另一方面其查询路由表存在着较大的冗余信息,因此从这两方面着手,对Chord算法进行了改进.建立了一个基于主从式结构的多Chord环模型,对环中节点的查询路由表进行分析研究,提出一个新的路由表计算公式,在删除冗余信息的基础上,充分利用查询路由表的空间资源,将节点的路由查找的覆盖范围从Chord环的1/2提升到3/4,最终提升到整个Chord环.实验结果表明,改进后的算法在平均路由跳数和平均网络延迟都有明显的减少,从而提高了资源搜索的效率.     

基于云计算的校园网络的QoS研究

阐述了目前校园网存在的网络设备重复投入,网络资源利用率低,网络服务质量不高的问题。提出了基于云计算技术可以在不投入硬件资源的情况下,提高网络资源利用率,优化网络性能,提高网络服务质量。     

Energy efficient utilization of resources in cloud computing systems

The energy consumption of under-utilized resources, particularly in a cloud environment, accounts for a substantial amount of the actual energy use. Inherently, a resource allocation strategy that takes into account resource utilization would lead to a better energy efficiency; this, in clouds, extends further with virtualization technologies in that tasks can be easily consolidated. Task consolidation is an effective method to increase resource utilization and in turn reduces energy consumption. Recent studies identified that server energy consumption scales linearly with (processor) resource utilization. This encouraging fact further highlights the significant contribution of task consolidation to the reduction in energy consumption. However, task consolidation can also lead to the freeing up of resources that can sit idling yet still drawing power. There have been some notable efforts to reduce idle power draw, typically by putting computer resources into some form of sleep/power-saving mode. In this paper, we present two energy-conscious task consolidation heuristics, which aim to maximize resource utilization and explicitly take into account both active and idle energy consumption. Our heuristics assign each task to the resource on which the energy consumption for executing the task is explicitly or implicitly minimized without the performance degradation of that task. Based on our experimental results, our heuristics demonstrate their promising energy-saving capability.     

A highly secured and streamlined cloud collaborative editing scheme along with an efficient user revocation in cloud computing

These days with the expanded fame of cloud computing, the interest for cloud-based collaborative editing service is rising. The encryption method is utilized to ensure and secure the data, during the collaborative editing process. In the encryption process, the cloud requires more time to work the collaborative editing. This paper proposes an efficient scheme for reducing the encryption burden over the cooperative users, as the possibilities of cooperative users read and write data by means of any gadget. In the proposed scheme, the encrypted file sent by the data owner is split into smaller segments and stored in the cloud by the cloud service provider (CSP) along with specific tags. Once the cooperative user receives and decrypts the file from the CSP, it modifies and encrypts only the modified segment and resends to the CSP. The CSP after verifying the signature replace the original file segment in the cloud with the modified segment based on the tag information. The scheme that is put forward is performed based on the modified ciphertext-policy hierarchical attribute–based encryption, and the security process is done based on the attribute-based signature schemes. This work employs a proficient attribute updating method to accomplish the dynamic change of users' attributes, consisting granting new attributes, revoking previous attributes, and regranting formerly revoked attributes. A writer's attributes and keys have been revoked, and the stale information cannot be written.     

Towards efficient and fair resource trading in community-based cloud computing

In this paper, we investigate the resource trading problem in a community-based cloud computing setting where multiple tenants communicate in a peer-to-peer (P2P) fashion. Enabling resource trading in a community cloud unleashes the untapped cloud resources, thus presents a flexible solution for managing resource allocation. However, finding an efficient and fair resource allocation is challenging mainly due to the heterogeneity of tenants. Our work first develops a market-oriented model to support resource negotiation and trading. Based on this model, we adopt a multiagent-based technique that allows a group of autonomous tenants to reach an efficient and fair resource allocation. Further, when budget constraint presents, we propose a directed hypergraph model to facilitate resource trading amongst heterogeneous tenants. We analyze the application of the directed hypergraph model to trading decision making, and design a series of heuristic-based resource trading protocols for both budget-unaware and budget-aware scenarios. The performances of the proposed protocols are validated through simulations. The results are in tune with the theoretical analysis and provide insights into practical application issues.     

云计算环境中高效可扩展的元数据管理方法

针对现有可扩展的元数据管理方法存在性能较低问题,提出一种高效可扩展的元数据管理方法,它首先采用动态二叉映射树来实现元数据服务器精确定位,然后采用延迟更新方法来动态更新二叉映射树,最后提出动态K叉编码树的元数据组织方法以提高元数据服务器扩展时选择迁移元数据的速度。实验结果表明,它有效提高了云计算环境中可扩展元数据管理方法的效率。     

Integrating QoS awareness with virtualization in cloud computing systems for delay-sensitive applications

Cloud computing provides scalable computing and storage resources over the Internet. These scalable resources can be dynamically organized as many virtual machines (VMs) to run user applications based on a pay-per-use basis. The required resources of a VM are sliced from a physical machine (PM) in the cloud computing system. A PM may hold one or more VMs. When a cloud provider would like to create a number of VMs, the main concerned issue is the VM placement problem, such that how to place these VMs at appropriate PMs to provision their required resources of VMs. However, if two or more VMs are placed at the same PM, there exists certain degree of interference between these VMs due to sharing non-sliceable resources, e.g. I/O resources. This phenomenon is called as the VM interference. The VM interference will affect the performance of applications running in VMs, especially the delay-sensitive applications. The delay-sensitive applications have quality of service (QoS) requirements in their data access delays. This paper investigates how to integrate QoS awareness with virtualization in cloud computing systems, such as the QoS-aware VM placement (QAVMP) problem. In addition to fully exploiting the resources of PMs, the QAVMP problem considers the QoS requirements of user applications and the VM interference reduction. Therefore, in the QAVMP problem, there are following three factors: resource utilization, application QoS, and VM interference. We first formulate the QAVMP problem as an Integer Linear Programming (ILP) model by integrating the three factors as the profit of cloud provider. Due to the computation complexity of the ILP model, we propose a polynomial-time heuristic algorithm to efficiently solve the QAVMP problem. In the heuristic algorithm, a bipartite graph is modeled to represent all the possible placement relationships between VMs and PMs. Then, the VMs are gradually placed at their preferable PMs to maximize the profit of cloud provider as much as possible. Finally, simulation experiments are performed to demonstrate the effectiveness of the proposed heuristic algorithm by comparing with other VM placement algorithms.     

QoS constraints-based energy-efficient model in cloud computing networks for multimedia clinical issues

For many applications of multimedia medical devices in clinical and medical issues, cloud computing becomes a very useful way. However, high energy consumption of cloud computing networks for these applications brings forth a large challenge. This paper studies the energy-efficient problem with QoS constraints in large-scale cloud computing networks. We use the sleeping and rate scaling mechanism to propose a link energy consumption model to characterize the network energy consumption. If there is no traffic on a link, we will let it be sleeping. Otherwise, it is activated and we divide its energy consumption into base energy consumption and traffic energy consumption. The former describes the constant energy consumption that exists when the link runs, while the later, which is a quadratic function with respect to the traffic, indicates the relations between link energy consumption and the traffic on the link. Then considering the relation among network energy consumption, number of active links, and QoS constraints, we build the multi-constrained energy efficient model to overcome the high energy consumption in large-scale cloud computing networks. Finally, we exploit the NSF and GEANT network topology to validate our model. Simulation results show that our approach can significantly improve energy efficiency of cloud computing networks.     

移动云环境下高效属性基加密方案研究

目前适应移动云的属性基加密研究主要集中在单一机构,并不满足现实中的属性授权情况。针对以上问题提出了一个新的多机构属性基加密方案,该方案无中央权威,各授权机构可互不影响,分发的属性可添加;利用预计算、外包解密的方法降低用户端的计算开销;方案在随机预言模型下证明是静态安全的。实验结果表明,移动端在云环境下实施数据共享时,该方案可以减少移动设备端20%的计算开销,更符合于移动云环境实际应用场景。     

Adaptive data replication strategy in cloud computing for performance improvement

Cloud computing is becoming a very popular word in industry and is receiving a large amount of attention from the research community. Replica management is one of the most important issues in the cloud, which can offer fast data access time, high data availability and reliability. By keeping all replicas active, the replicas may enhance system task successful execution rate if the replicas and requests are reasonably distributed. However, appropriate replica placement in a large-scale, dynamically scalable and totally virtualized data centers is much more complicated. To provide cost-effective availability, minimize the response time of applications and make load balancing for cloud storage, a new replica placement is proposed. The replica placement is based on five important parameters: mean service time, failure probability, load variance, latency and storage usage. However, replication should be used wisely because the storage size of each site is limited. Thus, the site must keep only the important replicas.We also present a new replica replacement strategy based on the availability of the file, the last time the replica was requested, number of access, and size of replica. We evaluate our algorithm using the CloudSim simulator and find that it offers better performance in comparison with other algorithms in terms of mean response time, effective network usage, load balancing, replication frequency, and storage usage.     

A user requirements-oriented privacy policy self-adaption scheme in cloud computing

In an ever-changing environment, Software as a Service (SaaS) can rarely protect users’ privacy. Being able to manage and control the privacy is therefore an important goal for SaaS. Once the participant of composite service is substituted, it is unclear whether the composite service satisfy user privacy requirement or not. In this paper, we propose a privacy policies automatic update method to enhance user privacy when a service participant change in the composite service. Firstly, we model the privacy policies and service variation rules. Secondly, according to the service variation rules, the privacy policies are automatically generated through the negotiation between user and service composer. Thirdly, we prove the feasibility and applicability of our method with the experiments. When the service quantity is 50, ratio that the services variations are successfully checked by monitor is 81%. Moreover, ratio that the privacy policies are correctly updated is 93.6%.     

基于云计算的电子政务一体化服务平台建设探索

云计算具备共享资源、灵活调度、业务协同、智能可靠等优点,适应电子政务节约化建设、智慧化管理的发展需求。为此,在研究基于云计算的电子政务服务模式的基础上,提出了建设政务云综合服务平台的设想,并具体分析了平台总体框架、功能组成以及建设原则。     

Secure and efficient data collaboration with hierarchical attribute-based encryption in cloud computing

With the increasing trend of outsourcing data to the cloud for efficient data storage, secure data collaboration service including data read and write in cloud computing is urgently required. However, it introduces many new challenges toward data security. The key issue is how to afford secure write operation on ciphertext collaboratively, and the other issues include difficulty in key management and heavy computation overhead on user since cooperative users may read and write data using any device. In this paper, we propose a secure and efficient data collaboration scheme, in which fine-grained access control of ciphertext and secure data writing operation can be afforded based on attribute-based encryption (ABE) and attribute-based signature (ABS) respectively. In order to relieve the attribute authority from heavy key management burden, our scheme employs a full delegation mechanism based on hierarchical attribute-based encryption (HABE). Further, we also propose a partial decryption and signing construction by delegating most of the computation overhead on user to cloud service provider. The security and performance analysis show that our scheme is secure and efficient.     

云计算下负载均衡的多维QoS约束任务调度机制

高效的任务调度机制能够更好地满足用户的QoS需求,实现各物理主机间的负载均衡,从而提高云计算环境的整体性能。而传统的任务调度往往只考虑任务的响应时间或安全性等,且负载均衡策略是静态的。根据云计算的弹性化和虚拟化等新特性,综合考虑任务的性能QoS和信任QoS,提出一种在云计算环境下的任务调度机制,采用虚拟机迁移技术实现动态负载均衡。通过在CloudSim2.1仿真环境下的分析和比较,该任务调度机制不但可以提高用户满意度,而且可以有效实现负载均衡。     

云计算环境下多有向无环图工作流的节能调度算法

针对多有向无环图(DAG)工作流节能调度算法中存在的节能效果不佳、适用范围较窄和无法兼顾性能优化等问题,提出了一种新的多DAG工作流节能调度方法--MREO。MREO在对计算密集型和通信密集型任务特点进行分析的基础上,通过整合独立任务,减少了处理器的数量,并利用回溯和分支限界算法对任务整合路径进行动态的优化选择,有效降低了整合算法的复杂度。实验结果证明,MREO在保证多DAG工作流性能的前提下,能够有效降低系统的计算和通信能量开销,获得了良好的节能效果。     

Ontology-based access control model for security policy reasoning in cloud computing

There are many security issues in cloud computing service environments, including virtualization, distributed big-data processing, serviceability, traffic management, application security, access control, authentication, and cryptography, among others. In particular, data access using various resources requires an authentication and access control model for integrated management and control in cloud computing environments. Cloud computing services are differentiated according to security policies because of differences in the permitted access right between service providers and users. RBAC (Role-based access control) and C-RBAC (Context-aware RBAC) models do not suggest effective and practical solutions for managers and users based on dynamic access control methods, suggesting a need for a new model of dynamic access control that can address the limitations of cloud computing characteristics. This paper proposes Onto-ACM (ontology-based access control model), a semantic analysis model that can address the difference in the permitted access control between service providers and users. The proposed model is a model of intelligent context-aware access for proactively applying the access level of resource access based on ontology reasoning and semantic analysis method.     

Secure access policy for efficient resource in mobile computing environment

As the Internet was activated and the mobile environment developed, it has become more common to access dynamic XML data regardless of location and time. XML is widely used for information exchange and representation of data for databases, applications, etc., using the advantage to describe information. As a result, large-capacity XML data becomes increasingly complex, and demand for data access policies is increasing. Security issues such as authorization of access to resources, authentication, security enhancement and privacy arise. The mobile computing environment differs from existing information systems in several ways, so it is difficult to apply the existing access control as it is. Therefore, this paper proposes a secure access policy method for query processing to enable efficient resource management in dynamic XML data environment. The results of the evaluation are also presented to show that the additionally proposed method is efficient and excellent.