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With the advent of 5G, supporting high-quality game streaming applications on edge devices has become a reality. This is evidenced by a recent surge in cloud gaming applications on mobile devices. In contrast to video streaming applications, interactive games require much more compute power for supporting improved rendering (such as 4K streaming) with the stipulated frames-per second (FPS) constraints. This in turn consumes more battery power in a power-constrained mobile device. Thus, the state-of-the-art gaming applications suffer from lower video quality (QoS) and/or energy efficiency. While there has been a plethora of recent works on optimizing game streaming applications, to our knowledge, there is no study that systematically investigates the design pairs on the end-to-end game streaming pipeline across the cloud, network, and edge devices to understand the individual contributions of the different stages of the pipeline for improving the overall QoS and energy efficiency. In this context, this paper presents a comprehensive performance and power analysis of the entire game streaming pipeline consisting of the server/cloud side, network, and edge. Through extensive measurements with a high-end workstation mimicking the cloud end, an open-source platform (Moonlight-GameStreaming) emulating the edge device/mobile platform, and two network settings (WiFi and 5G) we conduct a detailed measurement-based study with seven representative games with different characteristics. We characterize the performance in terms of frame latency, QoS, bitrate, and energy consumption for different stages of the gaming pipeline. Our study shows that the rendering stage and the encoding stage at the cloud end are the bottlenecks to support 4K streaming. While 5G is certainly more suitable for supporting enhanced video quality with 4K streaming, it is more expensive in terms of power consumption compared to WiFi. Further, fluctuations in 5G network quality can lead to huge frame drops thus affecting QoS, which needs to be addressed by a coordinated design between the edge device and the server. Finally, the network interface and the decoder units in a mobile platform need more energy-efficient design to support high quality games at a lower cost. These observations should help in designing more cost-effective future cloud gaming platforms.
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Configuring and Coordinating End-to-end QoS for Emerging Storage Infrastructure
Modern data center storage systems are invariably networked to allow for consolidation and flexible management of storage. They also include high-performance storage devices based on flash or other emerging technologies, generally accessed through low-latency and high-throughput protocols such as Non-volatile Memory Express (NVMe) (or its derivatives) carried over the network. With the increasing complexity and data-centric nature of the applications, properly configuring the quality of service (QoS) for the storage path has become crucial for ensuring the desired application performance. Such QoS is substantially influenced by the QoS in the network path, in the access protocol, and in the storage device. In this article, we define a new transport-level QoS mechanism for the network segment and demonstrate how it can augment and coordinate with the access-level QoS mechanism defined for NVMe, and a similar QoS mechanism configured in the device. We show that the transport QoS mechanism not only provides the desired QoS to different classes of storage accesses but is also able to protect the access to the shared persistent memory devices located along with the storage but requiring much lower latency than storage. We demonstrate that a proper coordinated configuration of the three QoS’s on the path is crucial to achieve the desired differentiation, depending on where the bottlenecks appear.
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- Award ID(s):
- 2011252
- PAR ID:
- 10509623
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Modeling and Performance Evaluation of Computing Systems
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 2376-3639
- Page Range / eLocation ID:
- 1 to 32
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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With the emergence of microsecond-scale NVMe storage devices, the Linux kernel storage stack overhead has become significant, almost doubling access times. We present XRP, a framework that allows applications to execute user-defined storage functions, such as index lookups or aggregations, from an eBPF hook in the NVMe driver, safely bypassing most of the kernel’s storage stack. To preserve file system semantics, XRP propagates a small amount of kernel state to its NVMe driver hook where the user-registered eBPF functions are called. We show how two key-value stores, BPF-KV, a simple B+-tree key-value store, and WiredTiger, a popular log-structured merge tree storage engine, can leverage XRP to significantly improve throughput and latency.more » « less
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null (Ed.)Deep neural networks (DNNs) have recently gained unprecedented success in various domains. In resource-constrained systems, QoS-aware DNNs are designed to meet latency requirements of mission-critical deep learning applications. However, none of the existing DNNs have been designed to satisfy both latency and memory bounds simultaneously as specified by end-users in the resource-constrained systems. In this paper, we propose BLINKNET, a runtime system that is able to guarantee both latency and memory/storage bounds via efficient QoS-aware per-layer approximation. We implement BLINKNET in Apache TVM and evaluate it using Cifar10-quick and VGG network models. Our experimental results show that BLINKNET can meet the latency and memory requirements with 2% accuracy loss on average.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3631606
