Virtual machine consolidation is attractive in cloud computing platforms for several reasons including reduced infrastructure costs, lower energy consumption and ease of management. However, the interference between co-resident workloads caused by virtualization can violate the service level objectives (SLOs) that the cloud platform guarantees. Existing solutions to minimize interference between virtual machines (VMs) are mostly based on comprehensive micro-benchmarks or online training which makes them computationally intensive. In this paper, we present CloudScope, a system for diagnosing interference for multi-tenant cloud systems in a lightweight way. CloudScope employs a discrete-time Markov Chain model for the online prediction of performance interference of co-resident VMs. It uses the results to optimally (re)assign VMs to physical machines and to optimize the hypervisor configuration, e.g. the CPU share it can use, for different workloads. We have implemented CloudScope on top of the Xen hypervisor and conducted experiments using a set of CPU, disk, and network intensive workloads and a real system (MapReduce). Our results show that CloudScope interference prediction achieves an average error of 9%. The interference-aware scheduler improves VM performance by up to 10% compared to the default scheduler. In addition, the hypervisor reconfiguration can improve network throughput by up to 30%.

Cloud-based software systems are expected to deliver reliable performance under dynamic workload while efficiently managing resources. Conventional monitoring frameworks provide limited support for flexible and intuitive performance queries. In this paper, we present a prototype monitoring and control platform for clouds that is a better fit to the characteristics of cloud computing (e.g. extensible, user-defined, scalable). Service Level Objectives (SLOs) are expressed graphically as Performance Trees, while violated SLOs trigger mitigating control actions.

As the computing industry enters the Cloud era, multicore architectures and virtualisation technologies are replacing traditional IT infrastructures. However, the complex relationship between applications and system resources in multicore virtualised environments is not well understood. Workloads such as web services and on-line financial applications have the requirement of high performance but benchmark analysis suggests that these applications do not optimally benefit from a higher number of cores. In this paper, we try to understand the scalability behaviour of network/CPU intensive applications running on multicore architectures. We begin by benchmarking the Petstore web application, noting the systematic imbalance that arises with respect to per-core workload. Having identified the reason for this phenomenon, we propose a queueing model which, when appropriately parametrised, reflects the trend in our benchmark results for up to 8 cores. Key to our approach is providing a fine-grained model which incorporates the idiosyncrasies of the operating system and the multiple CPU cores. Analysis of the model suggests a straightforward way to mitigate the observed bottleneck, which can be practically realised by the deployment of multiple virtual NICs within our VM. Next we make blind predictions to forecast performance with multiple virtual NICs. The validation results show that the model is able to predict the expected performance with relative errors ranging between 8 and 26 per cent.

Web services are integrated software components for the support of interoperable machine-to-machine interaction over a network. Web services have been widely employed for building service-oriented applications in both industry and academia in recent years. The number of publicly available Web services is steadily increasing on the Internet. However, this proliferation makes it hard for a user to select a proper Web service among a large amount of service candidates. An inappropriate service selection may cause many problems (e.g., ill-suited performance) to the resulting applications. In this paper, we propose a novel collaborative filtering-based Web service recommender system to help users select services with optimal Quality-of-Service (QoS) performance. Our recommender system employs the location information and QoS values to cluster users and services, and makes personalized service recommendation for users based on the clustering results. Compared with existing service recommendation methods, our approach achieves considerable improvement on the recommendation accuracy. Comprehensive experiments are conducted involving more than 1.5 million QoS records of real-world Web services to demonstrate the effectiveness of our approach.

With the on-demand ability of cloud computing, the performance requirement of a cloud application can be satisfied by adding a certain amount of computing resources to or removing some from the application in response to the workload fluctuation. However, the problem of the availability of application influenced by VM-based physical relative locations during resource scaling process is a challenge and has not been widely discussed yet. In this paper, the authors present a novel availability-based computing model to describe availability attribute of one application in the hierarchical topology of clouds. Moreover, the authors propose an availability-aware scaling mechanism by performing both vertical and horizontal resizing to explore how and where to allocate computing resource. Simulation results indicate that our model captured the availability of cloud applications properly and the proposed dynamic scaling approach achieves the objectives of meeting availability demands and minimizing the total cost.

Cloud computing has been regarded as a preferred technology for many developers to build cloud applications due to its rapid provisioning and elastic scaling. With the increase in the number of cloud providers, the owners of cloud applications have more options to deploy their applications. For example, considering the availability and performance of the cloud applications, they would deploy the applications into a cloud federation which is a cloud of clouds. For cloud providers, it is also attractive to join in a cloud federation because the utilization of their computing resource will be improved and their computing power will be extended in cloud federation. This paper analyzes the motivation of building cloud federation and the models of cloud federation, and proposes a design of the framework of multi-objective constrained resource management for cloud federation, which is composed of the cloud federation center and the extended cloud federation enabling components of cloud providers. The key technique of resource management in cloud federation is also discussed in this paper, including dynamic profit-driven resource provisioning, availability-aware placement and power-saved consolidation. The proposed framework could satisfy various requirements of the different roles in cloud federation and reach a win-win target. Keywords-cloud federation; multi-objective constrained; vertical and horizontal federation; resource management

Cloud computing has attracted increasing attention in recent years. With the growth in the number and frequency of applications being deployed into clouds, the burden of resource management of cloud providers is becoming heavier. The resource deployment must satisfy the need about the performance, availability and reliability of applications from the view of clients, but also ensure the high resource utilization of the cloud providers. In this paper, we design a multi-objective serial optimization with priorities approach, named RMORM, to find the resource deployment in clouds rapidly. This approach is of great practical significance and engineering value and scalable to add new constraints.

The availability of Web applications influenced by Virtual Machine (VM)-based physical locations during resource scaling is a crucial concern for customers and cloud providers. In this paper, we present a novel computing model to describe availability attribute of one application in hierarchical structured cloud. Meanwhile, we propose an availability-aware approach to explore how and where to allocate computing resource via vertical and horizontal scaling. Partial experimental results in simulation environment are also presented.

Cloud computing promises customers the on-demand ability to dynamically provision virtualization resources in face of workload variations. Most existing scaling approaches addressed this problem by allocating application to a certain amount of cloud resources. However, the problem of the availability of application influenced by VM-based physical locations during resource scaling process is a serious challenge due to dynamic complex workload and has not been widely discussed yet. In this paper, we present a novel availability-based computing model to describe availability attribute of one application in the hierarchical structured cloud. Moreover, we propose an availability-aware policy by performing both vertical and horizontal scaling to explore how and where to allocate computing resource. Simulation results indicate that our model captured the availability of cloud applications properly and proposed scaling approach achieves the objectives of meeting availability demands and minimizing the total communication cost.

A number of challenges in implementing cloud technique related to further improving Web application performance and decreasing the cost. In order to achieve high profits, cloud-based web application providers must carefully balance cloud resources and dynamic workloads. However, this task is usually difficulty because of the complex nature of most web application. In this paper, we presented a predictive performance model to analyze such applications and to determine when and how much resource to allocate to each tier of an application. In addition, we proposed a new profit model to describe revenues specified by the Service Level Agreement (SLA) and costs generated by leased resources. Furthermore, we employed profit driven model to guide our resource management algorithms to maximize the profits earned to the service providers. We also designed and implemented a simulation experiment on CloudSim that adopts our proposed methodology. Experimental results indicated that our model faithfully captures the performance and resources are allocated properly in response to the changing workload, thus the goal of maximizing the profit has been achieved.

Service-Oriented Architecture (SOA) provides a flexible framework for service composition. In a service market scenario, given a functional description of service, different providers may offer diverse service implementations that match such a functional description, but differ for some QoS attributes. It is increasingly vital to provide a service selection and recommendation mechanisms that best meet the QoS requirements of the service user. Different from most of the existing approaches to service selection, we consider a Web service selection and ranking mechanism with multi-QoS attributes, focusing on simulating degree of consumer satisfaction and hypothesizing consumer preference historical information. Efficient service selection mechanism and heuristic algorithm for consumer preference of multi-QoS are presented in this article and their performances are studied by simulations.