User subscription-based resource management for Desktop-as-a-Service platforms

The Desktop-as-a-Service (DaaS) idiom consists of utilizing a cloud or other server infrastructure to host the user's desktop environment as a virtual desktop. Typical for cloud and DaaS services is the pay-as-you-go pricing model in combination with the availability of multiple subscription types to accommodate the needs of the users. However, optimal cost-efficient allocation of the virtual desktops to the infrastructure proves to be a combinatorial NP-hard problem, for which a heuristic is presented in the current article. We present a cost model for the DaaS service, from which a revenue of different configurations of virtual desktops to the servers can be derived. In this cost model, both subscription fee and penalties for degraded service are recorded, that are described in service-level agreements (SLAs) between the service provider and the users, and make realistic assumptions that different subscription types result in particular SLA contracts. The heuristic proposed states that for a given user base for which the virtual desktops (VDs) must be hosted, the VDs should be spread evenly over the infrastructure. Experiments through discrete event simulation show that this heuristic yields an approximation within 1 % of the theoretically achievable revenue

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

Towards a Performance Interference-aware Virtual Machine Placement Strategy for Supporting Soft Real-time Applications in the Cloud

REACTION 2014. 3rd International Workshop on Real-time and Distributed Computing in Emerging Applications. Rome, Italy. December 2nd, 2014.It is standard practice for cloud service providers (CSPs) to overbook physical system resources to maximize the resource utilization and make their business model more profitable. Resource overbooking can lead to performance interference, however, among the virtual machines (VMs) hosted on the physical resources causing performance un-predictability for soft real-time applications hosted in the VMs, which is unacceptable to these applications. Balancing these conflicting requirements needs a careful design of the placement strategies for hosting soft real-time applications such that the performance interference effects are minimized while still allowing resource overbooking. These placement decisions cannot be made offline because workloads change at run time. Moreover, satisfying the priorities of collocated VMs may require VM migrations, which require an online solution. This paper presents a machine learning-based, online placement solution to this problem where the system is trained using a publicly available trace of a large data center owned by Google. Our approach first classifies the VMs based on their historic mean CPU and memory usage, and performance features. Subsequently, it learns the best patterns of collocating the classified VMs by employing machine learning techniques. These extracted patterns are those that provide the lowest performance interference level on the specified host machines making them amenable to hosting soft real-time applications while still allowing resource overbooking.This work was supported in part by the National Science Foundation CAREER CNS 0845789 and AFOSR DDDAS FA9550-13-1-0227.Publicad

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

Requirements for the implementation of schedule repair technology in the Experiment Scheduling Program

The following list of requirements specifies the proposed revisions to the Experiment Scheduling Program (ESP2) which deal with schedule repair. These requirements are divided into those which are general in nature, those which relate to measurement and analysis functions of the software, those which relate specifically to conflict resolution, and those relating directly to the user interface. (This list is not a complete list of requirements for the user interface, but only a list of those schedule repair requirements which relate to the interface.) Some of the requirements relate only to uses of the software in real-time operations. Others are clearly for future versions of the software, beyond the upcoming revision. In either case, the fact will be clearly stated