BULLET: Particle Swarm Optimization Based Scheduling Technique for Provisioned Cloud Resources

Cloud resource scheduling requires mapping of cloud resources to cloud workloads. Scheduling results can be optimized by considering Quality of Service (QoS) parameters as inherent requirements of scheduling. In existing literature, only a few resource scheduling algorithms have considered cost and execution time constraints but efficient scheduling requires better optimization of QoS parameters. The main aim of this research paper is to present an efficient strategy for execution of workloads on cloud resources. A particle swarm optimization based resource scheduling technique has been designed named as BULLET which is used to execute workloads effectively on available resources. Performance of the proposed technique has been evaluated in cloud environment. The experimental results show that the proposed technique efficiently reduces execution cost, time and energy consumption along with other QoS parameters.     

A self-learning fuzzy approach for proactive resource provisioning in cloud environment

The development of a communication infrastructure has made possible the expansion of the popular massively multiplayer online games. In these games, players all over the world can interact with one another in a virtual environment. The arrival rate of new players to the game environment causes fluctuations and players always expect services to be available and offer an acceptable service-level agreement (SLA), especially in terms of response time and cost. Cloud computing emerged in the recent years as a scalable alternative to respond to the dynamic changes of the workload. In massively multiplayer online games applications, players are allowed to lease resources from a cloud provider in an on-demand basis model. Proactive management of cloud resources in the face of workload fluctuations and dynamism upon the arrival of players are challenging issues. This paper presents a self-learning fuzzy approach for proactive resource provisioning in cloud environment, where key is to predict parameters of the probability distribution of the incoming players in each period. In addition, we propose a self-learning fuzzy autoscaling decision-maker algorithm to compute the proper number of resources to be allocated to each tier in the massively multiplayer online games by applying the predicted workload and user SLA. We evaluate the effectiveness of the proposed approach under real and synthetic workloads. The experimental results indicate that the proposed approach is able to allocate resources more efficiently than other approaches.     

Transparently bridging semantic gap in CPU management for virtualized environments

Consolidated environments are progressively accommodating diverse and unpredictable workloads in conjunction with virtual desktop infrastructure and cloud computing. Unpredictable workloads, however, aggravate the semantic gap between the virtual machine monitor and guest operating systems, leading to inefficient resource management. In particular, CPU management for virtual machines has a critical impact on I/O performance in cases where the virtual machine monitor is agnostic about the internal workloads of each virtual machine. This paper presents virtual machine scheduling techniques for transparently bridging the semantic gap that is a result of consolidated workloads. To enable us to achieve this goal, we ensure that the virtual machine monitor is aware of task-level I/O-boundedness inside a virtual machine using inference techniques, thereby improving I/O performance without compromising CPU fairness. In addition, we address performance anomalies arising from the indirect use of I/O devices via a driver virtual machine at the scheduling level. The proposed techniques are implemented on the Xen virtual machine monitor and evaluated with micro-benchmarks and real workloads on Linux and Windows guest operating systems.     

一种面向云多层应用的费用高效的资源管理方法

由于部署方便、维护简单并且不需要搭建自己的私有机房,云数据中心正成为大多数互联网公司 尤其是初创公司和中小规模公司 部署应用程序的首选。在以基础设施为服务的云环境里,互联网公司可以根据应用程序的需要动态租赁云基础设施,从而节省预算开支,并保证应用性能。然而,在现有的业界实践中,云服务提供商提供的负载均衡和资源伸缩服务只能监控虚拟机的使用状态,并不能监控应用程序的运行状态,因此无法准确根据应用程序的服务需求自适应变换资源规模。同时,现有的文献和实践中,也很少有 研究从云基础设施使用者的角度出发,为使用者节省基础设施租赁费用或高效使用已租赁基础设施。据此提出了一种面向基础设施云环境下多层应用的费用高效的资源管理方法,其在降低用户费用的同时,还能充分利用所花费用提高应用程序性能。通过仿真对所提方法业界实际使用的方法 进行比较,结果表明所提方法不仅能够提高应用程序的服务质量和服务性能,也能较大地降低公司在基础设施租赁方面的费用。     

云环境中基于cache负载实时定噪的同驻分析方法

云计算具有使用便捷、可按需定制服务、优化资源利用等特点,成为提供外包服务的主要计算模式。云环境中的虚拟机侧通道攻击是云计算的主要潜在威胁之一,同驻是云环境中侧通道攻击的前提。针对如何在多租户云环境下进行同驻检测,提出基于链式结构的Prime-Probe测量cache负载方法MCLPPLS和针对云环境噪声复杂多变问题的实时噪声分析机制RTNAM。结合MCLPPLS与RTNAM提出一种新型的同驻检测分析方法。实验表明,该方法能减少突发噪声对同驻检测的干扰,有较高的同驻检测正确率及较低的同驻检测时耗,表现出良好的性能。     

Performance analysis based resource allocation for green cloud computing

Cloud computing has become a new computing paradigm that has huge potentials in enterprise and business. Green cloud computing is also becoming increasingly important in a world with limited energy resources and an ever-rising demand for more computational power. To maximize utilization and minimize total cost of the cloud computing infrastructure and running applications, resources need to be managed properly and virtual machines shall allocate proper host nodes to perform the computation. In this paper, we propose performance analysis based resource allocation scheme for the efficient allocation of virtual machines on the cloud infrastructure. We experimented the proposed resource allocation algorithm using CloudSim and its performance is compared with two other existing models.     

A VMM-based intrusion prevention system in cloud computing environment

With the development of information technology, cloud computing becomes a new direction of grid computing. Cloud computing is user-centric, and provides end users with leasing service. Guaranteeing the security of user data needs careful consideration before cloud computing is widely applied in business. Virtualization provides a new approach to solve the traditional security problems and can be taken as the underlying infrastructure of cloud computing. In this paper, we propose an intrusion prevention system, VMFence, in a virtualization-based cloud computing environment, which is used to monitor network flow and file integrity in real time, and provide a network defense and file integrity protection as well. Due to the dynamicity of the virtual machine, the detection process varies with the state of the virtual machine. The state transition of the virtual machine is described via Definite Finite Automata (DFA). We have implemented VMFence on an open-source virtual machine monitor platform—Xen. The experimental results show our proposed method is effective and it brings acceptable overhead.     

基于负载高峰特征的虚拟机放置算法

提出了一种基于虚拟机负载高峰特征的虚拟机放置策略,通过更好地复用物理主机资源来实现资源共享,从而提高资源利用率.在云环境下,当多个虚拟机的负载高峰出现在相同的时间段内时,非高峰时段的资源利用率就会明显偏低;相反,多个虚拟机只要负载高峰能错开在不同的时间,闲置的资源就能更充分地被利用.由于应用的负载通常具有一定的周期性,因此,可以利用虚拟机负载的历史数据作为分析的依据.基于虚拟机的负载高峰特征对虚拟机负载进行建模,建立虚拟机负载之间的相似度矩阵来实现虚拟机联合放置.使用CloudSim模拟实现了所提出的算法,并与基于相关系数的放置算法、随机放置算法进行了比较.实验结果表明:所提算法在平均CPU利用率上有8.9%~12.4%的提高,主机使用量有8.2%~11.0%的节省.     

Development of a Hybrid Manufacturing Cloud

Cloud manufacturing is emerging as a novel business paradigm for the manufacturing industry, in which dynamically scalable and virtualised resources are provided as consumable services over the Internet. A handful of cloud manufacturing systems are proposed for different business scenarios, most of which fall into one of three deployment modes, i.e. private cloud, community cloud, and public cloud. One of the challenges in the existing solutions is that few of them are capable of adapting to changes in the business environment. In fact, different companies may have different cloud requirements in different business situations; even a company at different business stages may need different cloud modes. Nevertheless, there is limited support on migrating to different cloud modes in existing solutions. This paper proposes a Hybrid Manufacturing Cloud that allows companies to deploy different cloud modes for their periodic business goals. Three typical cloud modes, i.e. private cloud, community cloud and public cloud are supported in the system. Furthermore, it enables companies to set self-defined access rules for each resource so that unauthorised companies will not have access to the resource. This self-managed mechanism gives companies full control of their businesses and boosts their trust with enhanced privacy protection. A unified ontology is developed to enhance semantic interoperability throughout the whole process of service provision in the clouds. A Cloud Management Engine is developed to manage all the user-defined clouds, in which Semantic Web technologies are used as the main toolkit. The feasibility of this approach is verified through a group of companies, each of which has complex access requirements for their resources. In addition, a use case is carried out between customers and service providers. This way, optimal service is delivered through the proposed system.     

云工作流中基于分时虚拟机的任务层调度算法

云计算是新的一种面向市场的商业计算模式,向用户按需提供服务,云计算的商业特性使其关注向用户提供服务的服务质量。任务调度和资源分配是云计算中两个关键的技术,所使用的虚拟化技术使得其资源分配和任务调度有别于以往的并行分布式计算。目前主要的调度算法是借鉴网格环境下的调度策略,研究基于QoS的调度算法,存在执行效率较低的问题。我们对云工作流任务层调度进行深入研究,分析由底层资源虚拟化形成的虚拟机的特性,结合工作流任务的各类QoS约束,提出了基于虚拟机分时特性的任务层ACS调度算法。经过试验,我们提出的算法相比于文献[1]中的算法在对于较多并行任务的执行上存在较大的优势,能够很好的利用虚拟的分时特性,优化任务到虚拟机的调度。     

Self-managed cost-efficient virtual elastic clusters on hybrid Cloud infrastructures

In this study, we describe the ​further development of Elastic Cloud Computing Cluster (EC3), a tool ​for creating self-managed cost-efficient virtual hybrid elastic clusters on top of Infrastructure as a Service (IaaS) clouds. By using spot ​instances and checkpointing techniques, EC3 can significantly reduce the total ​execution cost as well as facilitating automatic fault tolerance. Moreover, EC3 can deploy and manage hybrid clusters across on-premises and public ​cloud resources, thereby introducing ​cloud bursting capabilities. ​We present the results of a case study that we conducted to assess the effectiveness of the tool ​based on the structural dynamic analysis of buildings. In addition, we evaluated the checkpointing algorithms in a real ​cloud environment with existing workloads to study their effectiveness. The results ​demonstrate the feasibility and benefits of this type of ​cluster for computationally intensive applications.     

A multi-parameter scheduling method of dynamic workloads for big data calculation in cloud computing

Workload scheduling in cloud computing is currently an active research field. Scheduling plays an important role in cloud computing performance, especially when the platform is used for big data analysis and as less predictable workloads dynamically enter the clouds. Finding the optimized scheduling solution with different parameters in different environments is still a challenging issue. In dynamic environments such as cloud, scheduling strategies should feature rapid altering to be able to adapt more easily to the changes in input workloads. However, achieving an optimized solution is an important issue, which has a trade-off with the speed of finding the solution. In this article, an ordinal optimization method is proposed that considers the volume of workloads, load balancing and the volume of exchanged messages among virtual clusters, considering the replications. The algorithm in the present paper is based on ordinal optimization (OO) and evolutionary OO. In any time periods, a criterion is calculated to determine the similarity of workloads in two-consequence time periods, which is appropriate for timely changes in the scheduling procedure. In this paper, considering more than one parameter, a proper scheduling would be created for each time period. This scheduler is an organization for the number of virtual machines for each virtual cluster, but if there is a desirable similarity between workloads of two-consequence time periods, this procedure would be ignored. The results show that a more optimized solution is obtained in comparison with the rated methods, such as blind pink, OO, Monte Carlo and eOO in a reasonable time. The suggested method is flexible and it is possible to change the weight ratio of the proposed criteria in different environments to be consistent with different environmental conditions. The results show that proposed method achieved up to 28% performance improvement in comparison with eOO.     

Latency based group discovery algorithm for network aware cloud scheduling

Cloud computing is a big paradigm shift of computing mechanism. It provides high scalability and elasticity with a range of on-demand services. We can execute a variety of distributed applications on cloud’s virtual machines (computing nodes). In a distributed application, virtual machine nodes need to communicate and coordinate with each other. This type of coordination requires that the inter-node latency should be minimal to improve the performance. But in the case of nodes belonging to different clusters of the same cloud or in a multi-cloud environment, there can be a problem of higher network latency. So it becomes more difficult to decide, which node(s) to choose for the distributed application execution, to keep inter-node latency at minimum. In this paper, we propose a solution for this problem. We propose a model for the grouping of nodes with respect to network latency. The application scheduling is done on the basis of network latency. This model is a part of our proposed Cloud Scheduler module, which helps the scheduler in scheduling decisions on the basis of different criteria. Network latency and resultant node grouping on the basis of this latency is one of those criteria. The main essence of the paper is that our proposed latency grouping algorithm not only has no additional network traffic overheads for algorithm computation but also works well with incomplete latency information and performs intelligent grouping on the basis of latency. This paper addresses an important problem in cloud computing, which is locating communicating virtual machines for minimum latency between them and group them with respect to inter-node latency.     

基于Ceilometer Alarm API的操作设计和实现

随着云计算技术的不断发展,越来越多的企业采用OpenStack来构建私有云或公有云平台.云平台正逐步替代传统服务器,用来承载着企业和用户的IT业务.为了保证云平台的服务质量,本文基于OpenStack的报警功能接口——Ceilometer Alarm API设计和实现了对于云平台虚拟机监控报警功能的交互操作页面.通过使用该功能,用户可以监控虚拟机运行时的性能状态,保证云平台的可靠运行.     

云计算技术发展分析及其应用研究

云计算被视作一个重大的革命,将会在根本上改变商业模式以及工作方式。随着自动化管理技术、网络技术、分布式存储技术以及虚拟化技术的不断发展,云计算也随之产生,文章针对云计算技术进行了较深入的分析研究。     

Efficient Energy-Aware Resource Management Model (EEARMM) Based Dynamic VM Migration

In cloud environment, an efficient resource management establishes the allocation of computational resources of cloud service providers to the requests of users for meeting the user’s demands. The proficient resource management and work allocation determines the accomplishment of the cloud infrastructure. However, it is very difficult to persuade the objectives of the Cloud Service Providers (CSPs) and end users in an impulsive cloud domain with random changes of workloads, huge resource availability and complicated service policies to handle them, With that note, this paper attempts to present an Efficient Energy-Aware Resource Management Model (EEARMM) that works in a decentralized manner. Moreover, the model involves in reducing the number of migrations by definite workload management for efficient resource utilization. That is, it makes an effort to reduce the amount of physical devices utilized for load balancing with certain resource and energy consumption management of every machine. The Estimation Model Algorithm (EMA) is given for determining the virtual machine migration. Further, VM-Selection Algorithm (SA) is also provided for choosing the appropriate VM to migrate for resource management. By the incorporation of these algorithms, overloading of VM instances can be avoided and energy efficiency can be improved considerably. The performance evaluation and comparative analysis, based on the dynamic workloads in different factors provides evidence to the efficiency, feasibility and scalability of the proposed model in cloud domain with high rate of resources and workload management.     

From cloud computing to cloud manufacturing

Cloud computing is changing the way industries and enterprises do their businesses in that dynamically scalable and virtualized resources are provided as a service over the Internet. This model creates a brand new opportunity for enterprises. In this paper, some of the essential features of cloud computing are briefly discussed with regard to the end-users, enterprises that use the cloud as a platform, and cloud providers themselves. Cloud computing is emerging as one of the major enablers for the manufacturing industry; it can transform the traditional manufacturing business model, help it to align product innovation with business strategy, and create intelligent factory networks that encourage effective collaboration. Two types of cloud computing adoptions in the manufacturing sector have been suggested, manufacturing with direct adoption of cloud computing technologies and cloud manufacturing—the manufacturing version of cloud computing. Cloud computing has been in some of key areas of manufacturing such as IT, pay-as-you-go business models, production scaling up and down per demand, and flexibility in deploying and customizing solutions. In cloud manufacturing, distributed resources are encapsulated into cloud services and managed in a centralized way. Clients can use cloud services according to their requirements. Cloud users can request services ranging from product design, manufacturing, testing, management, and all other stages of a product life cycle.     

基于云模型的虚拟人摇头动作不确定性控制*

以虚拟人摇头表达否定性信息为例,设计了最大摇头角否定云、摇头次数否定云和摇头速度否定云。分别运用正向云生成算法产生云滴,获得摇头动作序列的最大摇头角度、摇头次数和摇头速度。三个控制参数均以各自的期望值为中心正态分布。仿真实验表明,所提出的基于云模型的虚拟人摇头控制算法能够产生呈规律性差异的摇头动作序列控制曲线。给出了三类云模型数字特征的建议值。