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...     

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.     

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...     

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.     

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.     

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.     

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。     

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.     

QoS in grid computing

Grid computing is already a mainstream paradigm for resource-intensive scientific applications, but it also promises to become the future model for enterprise applications. The grid enables resource sharing and dynamic allocation of computational resources, thus increasing access to distributed data, promoting operational flexibility and collaboration, and allowing service providers to scale efficiently to meet variable demands. Large-scale grids are complex systems composed of thousands of components from disjoined domains. Planning the capacity to guarantee quality of service (QoS) in such environments is a challenge because global service-level agreements (SLAs) depend on local SLAs. We provide a motivating example for grid computing in an enterprise environment and then discuss the how resource allocation affects SLAs.     

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.     

Malware analysis performance enhancement using cloud computing

Nowadays, computer based technology has taken a central role in every person life. Hence, damage caused by malicious software (malware) can reach and effect many people globally as what could be in the early days of computer. A close look at the current approaches of malware analysis shows that the respond time of reported malware to public users is slow. Hence, the users are unable to get prompt feedback when reporting suspicious files. Therefore, this paper aims at introducing a new approach to enhance malware analyzer performance. This approach utilizes cloud computing features and integrates it with malware analyzer. To evaluate the proposed approach, two systems had been prepared carefully with the same malware analyzer, one of them utilizes cloud computing and the other left without change. The evaluation results showed that the proposed approach is faster by 23 % after processing 3,000 samples. Furthermore, utilizing cloud computing can open door to crowd-source this service hence encouraging malware reporting and accelerate malware detection by engaging the public users at large. Ultimately this proposed system hopefully can reduce the time taken to detect new malware in the wild.     

Dynamic cloud resource management for efficient media applications in mobile computing environments

Single-instruction-set architecture (Single-ISA) heterogeneous multi-core processors (HMP) are superior to Symmetric Multi-core processors in performance per watt. They are popular in many aspects of the Internet of Things, including mobile multimedia cloud computing platforms. One Single-ISA HMP integrates both fast out-of-order cores and slow simpler cores, while all cores are sharing the same ISA. The quality of service (QoS) is most important for virtual machine (VM) resource management in multimedia mobile computing, particularly in Single-ISA heterogeneous multi-core cloud computing platforms. Therefore, in this paper, we propose a dynamic cloud resource management (DCRM) policy to improve the QoS in multimedia mobile computing. DCRM dynamically and optimally partitions shared resources according to service or application requirements. Moreover, DCRM combines resource-aware VM allocation to maximize the effectiveness of the heterogeneous multi-core cloud platform. The basic idea for this performance improvement is to balance the shared resource allocations with these resources requirements. The experimental results show that DCRM behaves better in both response time and QoS, thus proving that DCRM is good at shared resource management in mobile media cloud computing.     

Towards an accurate evaluation of quality of cloud service in service-oriented cloud computing

Cloud computing promises to provide high quality, on-demand services with service-oriented architecture. However, cloud service typically come with various levels of services and performance characteristics, which makes Quality of Cloud Service (QoCS) high variance. Hence, it is difficult for the users to evaluate these cloud services and select them to fit their QoCS requirements. In this paper, we propose an accurate evaluation approach of QoCS in service-oriented cloud computing. We first employ fuzzy synthetic decision to evaluate cloud service providers according to cloud users’ preferences and then adopt cloud model to computing the uncertainty of cloud services based on monitored QoCS data. Finally, we obtain the evaluation results of QoCS using fuzzy logic control. The simulation results demonstrate that our proposed approach can perform an accurate evaluation of QoCS in service-oriented cloud computing.     

云计算环境下任务调度的趋势分析

云计算应用大规模和虚拟化的资源,通过计算机网络随时随地向用户提供基于不同需求的服务。作为影响云服务的关键因素,任务调度被许多专家学者所研究。研究了云计算中的任务调度算法的新特性,如何降低用户成本和云计算中心的能耗,以及实现效率与公平最大化和安全等目标。     

数字图书馆云计算安全分析及管理方法研究

云计算技术在近几年快速兴起,并且快速应用到图书馆系统中来,但随之带来的还有安全问题.本文就云时代的数字图书馆所面临的安全问题进行了阐述,并且就如何解决这些安全问题进行了细致的研究.