GPU is becoming attractive around multiple academic and industrial area because of its massively parallel computing ability. However, there are still some obstacles which the GPU virtualization technologies should overcome to reach their maturity. These obstacles mainly include the problem of resource allocation strategy to guarantee possible higher yield. This shortage has already become an obvious barrier to the practical GPU usage in the cloud for satisfying business and academical requirements. There are many mature pieces of research in the area of oversubscribed cloud computing to enhance economic efficiency. However, the study on GPU oversubscription is almost blank for the just started use of GPU in cloud computing. This paper introduces gOver, an economy-oriented GPU resource oversubscription system based on the GPU virtualization platform. gOver is able to share and modulate GPU resource among workloads in an adaptive and dynamic manner, guaranteeing the QoS level at the same time. We evaluate the proposed gOver strategy with designed experiments with specific workload characteristics. The experimental results show that our dynamic GPU oversubscription solution improves the economic efficiency by 20% over traditional GPU sharing strategy, and outperforms the static oversubscription method by much better stability in QoS control.

Live migration is a crucial feature in existing virtualization platforms. Since memory is dirtied rapidly during the execution of a virtual machine (VM), boosting memory migration speed becomes a significant factor in guaranteeing a high-level success ratio and efficiency. However, the statically-configured migration strategy cannot cope with various workloads running in VMs, resulting in frequently aborted migration processes and low success ratio. This paper proposed a one-for-all migration architecture called Adaptive Live Migration (AdaMig) to address these issues. This QEMU-based solution dynamically switches migration methods and tunes related parameters by monitoring the run-time statistics from the migration process and the physical host. Once AdaMig detects the tendency that migration cannot converge, it will switch to another migration method to synchronize remaining dirty pages. During the whole process, AdaMig also dynamically tunes migration parameters according to current resources available in the physical host and migration efficiency. Experimental results reflect that AdaMig improves the success ratio from 26.7% to 93.3% over various workloads, and migration time is reduced by up to 45.5% in comparison with the original solution in QEMU.

Currently, the virtualization technologies for cloud computing infrastructures supporting extra devices, such as GPU, require additional development and refinement. This requirement is particularly evident in the area of resource sharing and allocation under some performance constraints, like the quality of service (QoS) guarantee, in light of the closed GPU platform. This deficiency significantly limits the applicability range of the cloud platform, which aims to support the efficient and fluent execution of business and academic workloads. This paper introduces gQoS, an adaptive virtualized GPU resource capacity sharing system under the QoS target, which can share and allocate the virtualized GPU resource among workloads adaptively, guaranteeing the QoS level with stability and accuracy. We evaluate the workloads and compare our gQoS strategy with other allocation strategies. The experiments show that our strategy guarantees much better accuracy and stability in QoS control and that the total GPU resource utilization under gQoS can be rewarded with at most a 25.85 percent reduction compared with other strategies.

This paper introduces gMig, an open-source and practical vGPU live migration solution for full virtualization. Taking the advantage of the dirty pattern of GPU workloads, gMig presents the One-Shot Pre-Copy mechanism combined with the hashing based Software Dirty Page technique to achieve efficient vGPU live migration. Particularly, we propose three core techniques for gMig: 1) Dynamic Graphics Address Remapping, which parses and manipulates GPU commands to adjust the address mapping and adapt to a different environment after migration, 2) Software Dirty Page, which utilizes a hashing based approach with sampling pre-filtering to detect page modification, overcomes the commodity GPU's hardware limitation, and speeds up the migration by only sending the dirtied pages, 3) Overlapped Migration Process, which significantly compresses the hanging overhead by overlapping the dirty page verification and transmission concurrently. Our evaluation shows that gMig achieves GPU live migration with an average downtime of 302 ms on Windows and 119 ms on Linux. With the help of Software Dirty Page, the number of GPU pages transferred during the downtime is effectively reduced by up to 80.0 percent . The design of sampling filter and overlapped processing can bring about further 30.0 and 10.0 percent improvements in page processing.

Considering the performance improvement the cloud technology provides by processing workloads in parallel, applications and services are now migrating to online clouds. In a cloud platform, workloads can be executed in a virtualized environment to have a great improvement of the resource utilization. However, there is a new challenge in the allocation problem, which is quantifying and optimizing the fairness and efficiency of heterogeneous resources (CPUs and GPUs) required by applications such as cloud gaming. The solving approach needs scalarization methods of the requirement vector, relevant functions for fairness metrics, and an acceptable algorithm to solve that, where the difficulties mainly locate. We design an iterative, dynamic-adaptive heuristic solving algorithm Fairness-Efficiency Allocation (FEA) and optimize the implementation on a virtualized platform, which collects runtime data, allocates resources and reports differences. Data are recorded and analyzed to discover the effect of the allocation in different situations, including the promotion of fairness and the effect on the frame rate of the workloads. The result indicates that there is a considerable fairness improvement after the resource allocation, especially in situations that many virtual machines are executing simultaneously. Compared with the VGASA strategy, the fairness metric value improved 45 percent in three virtual machines' situation.

Cloud scheduler manages multi-resources (e.g., CPU, GPU, memory, storage etc.) in cloud platform to improve resource utilization and achieve cost-efficiency for cloud providers. The optimal allocation for multi-resources has become a key technique in cloud computing and attracted more and more researchers' attentions. The existing multi-resource allocation methods are developed based on a condition that the job has constant demands for multi-resources. However, these methods may not apply in a real cloud scheduler due to the dynamic resource demands in jobs' execution. In this paper, we study a robust multi-resource allocation problem with uncertainties brought by varying resource demands. To this end, the cost function is chosen as either of two multi-resource efficiency-fairness metrics called Fairness on Dominant Shares and Generalized Fairness on Jobs, and we model the resource demand uncertainties through three typical models, i.e., scenario demand uncertainty, box demand uncertainty and ellipsoidal demand uncertainty. By solving an optimization problem we get the solution for robust multi-resource allocation with uncertainties for cloud scheduler. The extensive simulations show that the proposed approach can handle the resource demand uncertainties and the cloud scheduler runs in an optimized and robust manner.

In this paper, we propose Auto-vGPU, a framework of automated resource sharing for virtualized GPU with self-configuration, to reduce manual intervention in system management while ensuring Service Level Agreement (SLA) targets. Auto-vGPU automatically collects the measurements of system metrics and learns a linear model for each application with dimension reduction. In order to fulfill the automated configuration of controller parameters, we propose a self-control-configuration method featuring the theory of automatic tuning of proportional-integral (PI) regulators. The experimental results of cloud gaming implementation demonstrate that Auto-vGPU is able to automatically build the low-dimension model and configure the control parameters without any manual interventions and the derived controller can adaptively allocate virtualized GPU resource to ensure the high performance of cloud applications.