Video streaming is crucial for AI applications that gather videos from sources to servers for inference by deep neural nets (DNNs). Unlike traditional video streaming that optimizes visual quality, this new type of video streaming permits aggressive compression/pruning of pixels not relevant to achieving high DNN inference accuracy. However, much of this potential is left unrealized, because current video streaming protocols are driven by the video source (camera) where the compute is rather limited. We advocate that the video streaming protocol should be driven by real-time feedback from the server-side DNN. Our insight is two-fold: (1) server-side DNN has more context about the pixels that maximize its inference accuracy; and (2) the DNN's output contains rich information useful to guide video streaming. We present DDS (DNN-Driven Streaming), a concrete design of this approach. DDS continuously sends a low-quality video stream to the server; the server runs the DNN to determine where to re-send with higher quality to increase the inference accuracy. We find that compared to several recent baselines on multiple video genres and vision tasks, DDS maintains higher accuracy while reducing bandwidth usage by upto 59% or improves accuracy by upto 9% with no additional bandwidth usage.

https://dl.acm.org/doi/pdf/10.1145/3387514.3405887

Server-Driven Video Streaming for Deep Learning Inference

We describe the results of a randomized controlled trial of video-streaming algorithms for bitrate selection and network prediction. Over the last eight months, we have streamed 14.2 years of video to 56,000 users across the Internet. Sessions are randomized in blinded fashion among algorithms, and client telemetry is recorded for analysis. 
We found that in this real-world setting, it is difficult for sophisticated or machine-learned control schemes to outperform a "simple" scheme (buffer-based control), notwithstanding good performance in network emulators or simulators. We performed a statistical analysis and found that the variability and heavy-tailed nature of network and algorithm behavior create hurdles for robust learned algorithms in this area. 
We developed an ABR algorithm that robustly outperforms other schemes in practice, by combining classical control with a learned network predictor, trained with supervised learning in situ on data from the real deployment environment. 
To support further investigation, we are publishing an archive of traces and results each day, and will open our ongoing study to the community. We welcome other researchers to use this platform to develop and validate new algorithms for bitrate selection, network prediction, and congestion control.

Learning in situ: a randomized experiment in video streaming

Adaptive Bitrate (ABR) video streaming is widely used for over-the-top (OTT) video delivery. Recently, streaming providers have been moving towards using Variable Bitrate (VBR) encodings for the video content, spurred by the potential of improving user QoE (Quality of Experience) and reducing network bandwidth requirements compared to Constant Bitrate (CBR) encodings. However VBR introduces new challenges for ABR streaming, whose nature and implications are little understood. We explore these challenges across diverse video genres, encoding technologies, and platforms. We identify distinguishing characteristics of VBR encodings that impact user QoE and should be factored in any ABR adaptation decision. Traditional ABR adaptation strategies designed for the CBR case are not adequate for VBR. We develop novel best practice design principles to guide ABR rate adaptation for VBR encodings. As a proof of concept, we design a novel and practical control-theoretic rate adaptation scheme, CAVA (Control-theoretic Adaption for VBR-based ABR streaming), incorporating these concepts. Extensive evaluations show that CAVA substantially outperforms existing state-of-the-art adaptation techniques, validating the importance of these design principles.

ABR streaming of VBR-encoded videos: characterization, challenges, and solutions

DASH, or Dynamic Adaptive Streaming over HTTP, relies on a rate adaptation component to decide on which representation to download for each video segment. A plethora of rate adaptation algorithms has been proposed in recent years. The decisions of which bitrate to download made by these algorithms largely depend on several factors: estimated network throughput, buffer occupancy, and buffer capacity. Yet, these algorithms are not informed by a fundamental relationship between these factors and the chosen bitrate, and as a result, we found that they do not perform consistently in all scenarios, and require parameter tuning to work well under different buffer capacity. In this paper, we model a DASH client as an M/D/1/K queue, which allows us to calculate the expected buffer occupancy given a bitrate choice, network throughput, and buffer capacity. Using this model, we propose QUETRA, a simple rate adaptation algorithm. We evaluated QUETRA under a diverse set of scenarios and found that, despite its simplicity, it leads to better quality of experience (7% - 140%) than existing algorithms.

QUETRA: A Queuing Theory Approach to DASH Rate Adaptation

Microservice architecture (MSA) is an architectural style for distributed software systems, which promotes the use of fine-grained services with their own lifecycles. Several benefits of MSA have been reported in the literature, including increased modularity, flexible configuration, easier development, easier maintenance, and increased productivity. On the other hand, the adoption of MSA for a specific software system is not trivial and a number of challenges have been reported in the literature. These challenges should be evaluated carefully concerning project requirements before successful MSA adoption. Unfortunately, there has been no attempt to systematically review and categorize these challenges and the potential solution directions. This article aims at identifying the state of the art of MSA and describing the challenges in applying MSA together with the identified solution directions. A systematic literature review (SLR) is performed using the published literature since the introduction of MSA in 2014. Overall, 3842 papers were discovered using a well-planned review protocol, and 85 of them were selected as primary studies and analyzed regarding research questions. Nine basic categories of challenges were identified and detailed into 40 sub-categories, for which potential solution directions were explored. MSA seems feasible, but the identified challenges could impede the expected benefits when not taken into account. This study identifies and synthesizes the reported challenges and solution directions, but further research on these directions is needed to leverage the successful MSA adoption.

/pdf/challenges-and-solution-directions-of-microservice-1e9ncciq.pdf

Challenges and Solution Directions of Microservice Architectures: A Systematic Literature Review

Recently, HTTP-based adaptive video streaming has been widely adopted in the Internet. Up to now, HTTP-based adaptive video streaming is standardized as Dynamic Adaptive Streaming over HTTP (DASH), where a client-side video player can dynamically pick the bitrate level according to the perceived network conditions. Actually, not only the available bandwidth is varying, but also the chunk sizes in the same bitrate level significantly fluctuate, which also influences the bitrate adaptation. However, existing bitrate adaptation algorithms do not accurately involve the chunk size variation, leading to performance losses. In this paper, we theoretically analyze the influence of chunk size variation on bitrate adaptation performance. Based on DASH system features, we build a general model describing the playback buffer evolution. Applying stochastic theories, we respectively analyze the influence of the chunk size variation on rebuffering probability and average bitrate level. Furthermore, based on theoretical insights, we provide several recommendations for algorithm designing and rate encoding, and also propose a simple bitrate adaptation algorithm. Extensive simulations verify our insights as well as the efficiency of the proposed recommendations and algorithm.

Modeling and analyzing the influence of chunk size variation on bitrate adaptation in DASH

Today, considerable Internet traffic is sent from datacenter and heads for users. The network characteristics of connections served by servers in datacenters are usually diverse. As a result, a specific congestion control algorithm hardly accommodates the heterogeneity and performs well in various scenarios. In this work, we present Rein — a novel framework for Internet congestion control. With Rein, diverse congestion control algorithms can be assigned purposely to connections in one server to adapt to heterogeneity. We design and implement Rein in Linux, and the experiments validate that Rein is capable of smoothly switching among various candidate algorithms on the fly to achieve potential performance gain. Meanwhile, the overheads introduced by Rein are moderate and acceptable.

One Rein to Rule Them All: A Framework for Datacenter-to-User Congestion Control

Recent academic and industrial work is exploring the challenges of using RDMA over Ethernet, to support highly reliable, latency-sensitive services in today's datacenters. Previous work on the high-speed packet I/O like netmap, DPDK, etc., and high-performance user-level stacks like mTCP, IX etc., rekindles our inspirations to implement a high-performance software RDMA over commodity Ethernet devices.This paper summarizes and explores the much-overlapped design philosophy between RDMA and the high-performance user-level stacks. Inspired by these, we design SoftRDMA, which is a user-level iWARP stack, based on One-Copy and deliberate threading model design. SoftRDMA's system implementation includes user-level iWARP/TCP/IP protocols and the DPDK packet I/O. No special hardware or software is required beyond. It provides the basic verbs of iWARP for RDMA communication. In our evaluation, SoftRDMA demonstrates comparable latency and throughput performance against the hardware-supported iWARP scheme. It achieves microsecond latency for short message and nearly full line rate for long message transfer.

SoftRDMA: Rekindling High Performance Software RDMA over Commodity Ethernet

More and more commercial video players use bitrate adaptation to adjust video quality according to varying network conditions. Optimization-based approaches are widely used for bitrate adaptation in Dynamic Adaptive Streaming over HTTP (DASH). Essentially, the optimization problem is solved based on the prediction of buffer dynamics. However, stochastic chunk size deviates observably the buffer occupancy from the expected value, making the evolution hard to predict. In order to get rid of this effect and improve the prediction accuracy for buffer occupancy, we propose an algorithm based on markov decision process with incorporating chunk size information so that only the network capacity variation need to be considered in the decision-making process. Experiment results show that our solution can effectively eliminate performance oscillation induced by variable chunk size and achieve a good QoE.

Improving Optimization-Based Rate Adaptation in DASH System

Previous studies have shown that userspace TCP stacks have higher performance than the kernel version. One conventional wisdom is that using native APIs instead of system calls can reduce the context switching overheads. However, due to the non-compatible APIs, countless existing BSD-based applications have not benefited from the userspace TCP stacks yet. There is a gap between existing BSD-based applications and the userspace TCP stack. We propose a transparent socket layer (TSL) that allows applications transparently using the userspace TCP stacks. TSL consists of three components: (i) The transplant layer redirects original system calls into TSL's customized versions. (ii) The function call entry table holds the immediate state of function calls. (iii) The backend interacts with the table and executes the rest of TCP processing. In our evaluation, TSL has 2.3X~2.8X request rate compared with the kernel. We also show that TSL can benefit real applications, such as Netperf and Nginx.

Making Userspace TCP Stacks Transparent to Applications

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