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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. [ABSTRACT FROM AUTHOR]

The past decade witnessed the tremendous growth of online services and applications. Together with the increase of cloud computing, more and more computation are hosted by Internet data centers (IDCs). Today’s IDCs are achieving significant advances in communication and computation capabilities. However, along with the increasing demand from IDC clients, power consumption for powering up and cooling these IDCs has been skyrocketing. Most existing works optimize the power consumption of either servers or Computer Room Air Conditioners (CRACs), and overlook the correlation between the power consumption of these two types of equipment. In this paper, we propose an adaptive power control method which leverages the correlation between the power consumption of servers and CRACs. To capture the workload uncertainties and thermal dynamics, we exploit Recursive-Least Square based Model Predictive Control (MPC) to solve the power control problem. Performance evaluations shows the effective power peak reduction using our approach. [ABSTRACT FROM AUTHOR]

Content service is a type of Internet cloud service that provides end-users plentiful contents. To ensure high performance for content delivering, content service utilizes a technology known as content multihoming: contents are generated from multiplegeographically distributed data centers and delivered by multiple distributed content distribution networks (CDNs). The electricity costs for data centers and the usage costs for CDNs are major contributors to the contents service cost. As electricity prices vary across data centers and usage costs vary across CDNs, scheduling data centers and CDNs has a tremendous consequence for optimizing content service cost. In this paper, we propose a novel framework named Cost Optimization for Internet Content Multihoming (COMIC). COMIC dynamically balances end-users’ loads among data centers and CDNs so as to minimize the content service cost. Using real-lifeelectricity prices and CDN traces, the experiments demonstrate that COMIC effectively reduces the content service cost by more than 20 percent. [ABSTRACT FROM PUBLISHER]

As the virtualization technology for GPUs matures, cloud gaming has become an emerging application among cloud services. In addition to the poor default mechanisms of GPU resource sharing, the performance of cloud games is inevitably undermined by various runtime uncertainties such as rendering complex game scenarios. The question of how to handle the runtime uncertainties for GPU resource sharing remains unanswered. To address this challenge, we propose vGASA, a virtualized GPU resource adaptive scheduling algorithm in cloud gaming. vGASA interposes scheduling algorithms in the graphics API of the operating system, and hence the host graphic driver or the guest operating system remains unmodified. To fulfill the service level agreement as well as maximize GPU usage, we propose three adaptive scheduling algorithms featuring feedback control that mitigates the impact of the runtime uncertainties on the system performance. The experimental results demonstrate that vGASA is able to maintain frames per second of various workloads at the desired level with the performance overhead limited to 5-12 percent. [ABSTRACT FROM AUTHOR]