Cloud-based desktop services for thin clients

Cloud computing and ubiquitous network availability have renewed people's interest in the thin client concept. By executing applications in virtual desktops on cloud servers, users can access any application from any location with any device. For this to be a successful alternative to traditional offline applications, however, researchers must overcome important challenges. The thin client protocol must display audiovisual output fluidly, and the server executing the virtual desktop should have sufficient resources and ideally be close to the user's current location to limit network delay. From a service provider viewpoint, cost reduction is also an important issue

Virtual Machines Overloaded In Cloud Computing Using Cloudsim

Cloud computing has a massive pool of resources. Cloud Computing is an Internet based computing. Cloud Computing provides shared computer processing resources and data to computers. Now-a-days most of the companies are working under the Cloud Computing. Cloud application has a different configuration,composition, and deployment. Cloud Computing provides three service models such as Infrastructure as a Service,Platform as a Service,Software as a Service. Based on the service model it classified as Public Cloud, Private Cloud, Hybrid Cloud, Community Cloud. Through this paper,. We suggests the execution of Private cloud system that provides Infrastructure as a Service using CloudSim. CloudSim is framework for modeling and simulation of Cloud Computing Infrastructures and services .CloudSim is a toolkit for Cloud computing that supports modeling and creation of one or more Virtual Machine on a parallel Nodes of a Datacenter, Jobs and their mapping to suitable VMs

Efficient resource management for virtual desktop cloud computing

In virtual desktop cloud computing, user applications are executed in virtual desktops on remote servers. This offers great advantages in terms of usability and resource utilization; however, handling a large amount of clients in the most efficient manner poses important challenges. Especially deciding how many clients to handle on one server, and where to execute the user applications at each time is important. Assigning too many users to one server leads to customer dissatisfaction, while assigning too little leads to higher investments costs. We study different aspects to optimize the resource usage and customer satisfaction. The results of the paper indicate that the resource utilization can increase with 29% by applying the proposed optimizations. Up to 36.6% energy can be saved when the size of the online server pool is adapted to the system load by putting redundant hosts into sleep mode

Cognitive-Aware Network Virtualization Hypervisor for Efficient Resource Provisioning in Software Defined Cloud Networks

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIntegration of different technologies forms an integral part of modern network engineering and 5G technology deployment. Although Software Defined Networking (SDN) and Network Functions Virtualization (NFV) function well independently, integrating these two technologies present the cooperate advantages to service providers and service users. Operations of cloud computing technologies have been enhanced with the advent of SDN and NFV for efficient solutions deployment and infrastructure management in Software Defined Cloud Datacentre Networks (SDCDCN) where dynamic controllability is indispensable for elastic service provision. The provisioning of joint compute and network resources enabled by SDCN is essential to enforce reasonable Service Level Agreements (SLAs) stating the Quality of Service (QoS) while saving energy consumption and resource wastage. This thesis presents a Cognitive- Aware Network virtualization Hypervisor which was developed from merging the programmable dynamic network control attributes of SDN and the network slicing attributes of NFV to provision joint compute and network resources in SDCDCN for QoS fulfilment and energy efficiency. It focuses on the techniques for allocating Virtual Network Requests on physical hosts and switches considering SLA, QoS, and energy efficiency aspects. The thesis advances the state-of the-art with the following key contributions: A modelling and simulation environment for Software Defined Cloud Datacentre Networks abstracting functionalities and behaviours of virtual and physical network resources. The second is a novel dynamic overbooking algorithm for energy efficiency and SLA enforcement with the migration of virtual machines and network flows. Finally, a performance-aware intelligent overbooking for predicting network resource usage and performance for the next defined time interval considering multiple performance indexes

Optimising for energy or robustness? Trade-offs for VM consolidation in virtualized datacenters under uncertainty

The final publication is available at Springer via http://dx.doi.org/10.1007/s11590-016-1065-xReducing the energy consumption of virtualized datacenters and the Cloud is very important in order to lower CO2 footprint and operational cost of a Cloud operator. However, there is a trade-off between energy consumption and perceived application performance. In order to save energy, Cloud operators want to consolidate as many Virtual Machines (VM) on the fewest possible physical servers, possibly involving overbooking of resources. However, that may involve SLA violations when many VMs run on peak load. Such consolidation is typically done using VM migration techniques, which stress the network. As a consequence, it is important to find the right balance between the energy consumption and the number of migrations to perform. Unfortunately, the resources that a VM requires are not precisely known in advance, which makes it very difficult to optimise the VM migration schedule. In this paper, we therefore propose a novel approach based on the theory of robust optimisation. We model the VM consolidation problem as a robust Mixed Integer Linear Program and allow to specify bounds for e.g. resource requirements of the VMs. We show that, by using our model, Cloud operators can effectively trade-off uncertainty of resource requirements with total energy consumption. Also, our model allows us to quantify the price of the robustness in terms of energy saving against resource requirement violations.Peer ReviewedPostprint (author's final draft

Biased project status reports: A survey of IS professionals

This paper summarizes an empirical investigation that explored biased project reporting by Information Systems (IS) professionals. The study is based on a survey of 91 professionals who were involved with system implementations in various governmental agencies. Our investigation assessed the impact of project importance, control, structure, and size on biasing behaviors. To formulate the research hypotheses for our study, we adopted a Message Exchange Perspective. The results reveal that IS professionals are more likely to bias their project status communications when working in projects that are (1) large, (2) important, and (3) lack controls. The practical and research implications of our findings are discussed

Temporal Bandwidth-Intensive Virtual Network Allocation Optimization in Data Centers

Title from PDF of title page, viewed on July 15, 2015Thesis advisor: Deep MedhiVitaIncludes bibliographic references (pages 30-31)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2014In this paper, we consider bandwidth-intensive services for customers that want virtual networks (VN) in a data center environment. In particular, we consider this problem in a temporal context where bandwidth-intensive requests from each VN may arrive randomly at a review point, which may last for a certain duration. Thus, at each review point, the data center network provider must optimally allocate resources for the demand requests. For this problem, we present a mixed-integer programming (MIP) problem formulation where any request from a VN customer may be assigned to any virtual machine so that network resource availability is optimized. We present an overbooking strategy that may be employed to allow for some demands not met in the first try. For comparison, we also consider a base case where the allocation is pinned to a specific destination. Through our study, we show the comparative gains of different schemesIntroduction -- Literature survey -- Model -- Results -- Conclusions -- Appendix A. Node numbering -- Appendix B. NetworkSimulation