Junchen Jiang

Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE

Modern video players today rely on bit-rate adaptation in order to respond to changing network conditions. Past measurement studies have identified issues with todays commercial players when multiple bit-rate-adaptive players share a bottleneck link with respect to three metrics: fairness, efficiency, and stability. Unfortunately, our current understanding of why these effects occur and how they can be mitigated is quite limited. In this paper, we present a principled understanding of bit-rate adaptation and analyze several commercial players through the lens of an abstract player model consisting of three main components: bandwidth estimation, bit-rate selection, and chunk scheduling. Using framework, we identify the root causes of several undesirable interactions that arise as a consequence of overlaying the video bit-rate adaptation over HTTP. Building on these insights, we develop a suite of techniques that can systematically guide the tradeoffs between stability, fairness, and efficiency and thus lead to a general framework for robust video adaptation. We pick one concrete instance from this design space and show that it significantly outperforms todays commercial players on all three key metrics across a range of experimental scenarios.

A case for a coordinated internet video control plane

Video traffic already represents a significant fraction of todays traffic and is projected to exceed 90% in the next five years. In parallel, user expectations for a high quality viewing experience (e.g., low startup delays, low buffering, and high bitrates) are continuously increasing. Unlike traditional workloads that either require low latency (e.g., short web transfers) or high average throughput (e.g., large file transfers), a high quality video viewing experience requires sustained performance over extended periods of time (e.g., tens of minutes). This imposes fundamentally different demands on content delivery infrastructures than those envisioned for traditional traffic patterns. Our large-scale measurements over 200 million video sessions show that todays delivery infrastructure fails to meet these requirements: more than 20% of sessions have a rebuffering ratio ≥ 10% and more than 14% of sessions have a video startup delay ≥ 10s. Using measurement-driven insights, we make a case for a video control plane that can use a global view of client and network conditions to dynamically optimize the video delivery in order to provide a high quality viewing experience despite an unreliable delivery infrastructure. Our analysis shows that such a control plane can potentially improve the rebuffering ratio by up to 2× in the average case and by more than one order of magnitude under stress.

CS2P: Improving Video Bitrate Selection and Adaptation with Data-Driven Throughput Prediction

Bitrate adaptation is critical in ensuring good users’ quality-of-experience (QoE) in Internet video delivery system. Several efforts have argued that accurate throughput prediction can dramatically improve (1) initial bitrate selection for low startup delay and high initial resolution; (2) midstream bitrate adaptation for high QoE. However, prior ef- forts did not systematically quantify real-world throughput predictability or develop good prediction algorithms. To bridge this gap, this paper makes three key technical contributions: First, we analyze the throughput characteristics in a dataset with 20M+ sessions. We find: (a) Sessions sharing similar key features (e.g., ISP, region) present similar initial values and dynamical patterns; (b) There is a natural “stateful” dynamical behavior within a given session. Second, building on these insights, we develop CS2P, a better throughput prediction system. CS2P leverages data-driven approach to learn (a) clusters of similar sessions, (b) an initial throughput predictor, and (c) a Hidden-Markov-Model based midstream predictor modeling the stateful evolution of throughput. Third, we develop a prototype system and show by trace-driven simulation and real-world experiments that CS2P outperforms state-of-art by 40% and 50% median pre- diction error respectively for initial and midstream through- put and improves QoE by 14% over buffer-based adaptation algorithm.

Via: Improving Internet Telephony Call Quality Using Predictive Relay Selection

Interactive real-time streaming applications such as audio-video conferencing, online gaming and app streaming, place stringent requirements on the network in terms of delay, jitter, and packet loss. Many of these applications inherently involve client-to-client communication, which is particularly challenging since the performance requirements need to be met while traversing the public wide-area network (WAN). This is different from the typical situation of cloud-to-client communication, where the WAN can often be bypassed by moving a communication end-point to a cloud “edge”, close to the client. Can we nevertheless take advantage of cloud resources to improve the performance of real-time client-to-client streaming over the WAN? In this paper, we start by analyzing data from a large VoIP provider whose clients are spread across over 21,000 AS’es and nearly all the countries, to understand the challenges faced by interactive audio streaming in the wild. We find that while inter-AS and international paths exhibit significantly worse performance than intra-AS and domestic paths, the pattern of poor performance is nevertheless quite scattered, both temporally and spatially. So any effort to improve performance would have to be fine-grained and dynamic. Then, we turn to the idea of overlay routing, but in the context of the well-provisioned, managed network of a cloud provider rather than peer-to-peer as has been considered in past work. Such a network typically has a global footprint and peers with a large number of network providers. When the performance of a call via the direct path is predicted to be poor, the call traffic could be directed to enter the managed network close to one end point and exit it close to the other end point, thereby avoiding wide-area communication over the public Internet. We present and evaluate data-driven techniques to deciding whether to relay a call through the managed network and if so how to pick the ingress and egress relays to maximize performance, all while operating within a budget for relaying calls via the managed overlay network. We show that call performance can potentially improve by 40%-80% on average, with our techniques closely matching it.

MOIST: a scalable and parallel moving object indexer with school tracking

Location-Based Service (LBS) is rapidly becoming the next ubiquitous technology for a wide range of mobile applications. To support applications that demand nearest-neighbor and history queries, an LBS spatial indexer must be able to efficiently update, query, archive and mine location records, which can be in contention with each other. In this work, we propose MOIST, whose baseline is a recursive spatial partitioning indexer built upon BigTable. To reduce update and query contention, MOIST groups nearby objects of similar trajectory into the same school, and keeps track of only the history of school leaders. This dynamic clustering scheme can eliminate redundant updates and hence reduce update latency. To improve history query processing, MOIST keeps some history data in memory, while it flushes aged data onto parallel disks in a locality-preserving way. Through experimental studies, we show that MOIST can support highly efficient nearest-neighbor and history queries and can scale well with an increasing number of users and update frequency.

Unleashing the Potential of Data-Driven Networking

The last few years have witnessed the coming of age of data-driven paradigm in various aspects of computing (partly) empowered by advances in distributed system research (cloud computing, MapReduce, etc.). In this paper, we observe that the benefits can flow the opposite direction: the design and management of networked systems can be improved by data-driven paradigm. To this end, we present DDN, a new design framework for network protocols based on data-driven paradigm. We argue that DDN has the potential to significantly achieve better performance through harnessing more data than one single flow. Furthermore, we systematize existing instantiations of DDN by creating a unified framework for DDN, and use the framework to shed light on the common challenges and reusable design principles. We believe that by systematizing this paradigm as a broader community, we can unleash the unharnessed potential of DDN.

Enabling near real-time central control for live video delivery in CDNs

User-created live video streaming is marking a fundamental shift in the workload of live video delivery. However, live-video-specific challenges and the viral nature of user-created content makes it difficult for current CDNs to deliver 1) high-quality, 2) highly-scalable, and 3) highly-responsive service. We present the design and implementation of VDN, a new control plane for CDNs designed to optimize the delivery of live streams within the CDN. VDN satisfies these requirements by using two approaches: 1) optimizing directly for video quality (not just throughput) and 2) combining centralized control with local control, allowing VDN to adapt to traffic dynamics and network failures at fine timescales.

BDS: a centralized near-optimal overlay network for inter-datacenter data replication

Many important cloud services require replicating massive data from one datacenter (DC) to multiple DCs. While the performance of pair-wise inter-DC data transfers has been much improved, prior solutions are insufficient to optimize bulk-data multicast, as they fail to explore the capability of servers to store-and-forward data, as well as the rich inter-DC overlay paths that exist in geo-distributed DCs. To take advantage of these opportunities, we present BDS, an application-level multicast overlay network for large-scale inter-DC data replication. At the core of BDS is a fully centralized architecture, allowing a central controller to maintain an up-to-date global view of data delivery status of intermediate servers, in order to fully utilize the available overlay paths. To quickly react to network dynamics and workload churns, BDS speeds up the control algorithm by decoupling it into selection of overlay paths and scheduling of data transfers, each can be optimized efficiently. This enables BDS to update overlay routing decisions in near realtime (e.g., every other second) at the scale of multicasting hundreds of TB data over tens of thousands of overlay paths. A pilot deployment in one of the largest online service providers shows that BDS can achieve 3-5 x speedup over the providers existing system and several well-known overlay routing baselines.

Chameleon: scalable adaptation of video analytics

Applying deep convolutional neural networks (NN) to video data at scale poses a substantial systems challenge, as improving inference accuracy often requires a prohibitive cost in computational resources. While it is promising to balance resource and accuracy by selecting a suitable NN configuration (e.g., the resolution and frame rate of the input video), one must also address the significant dynamics of the NN configurations impact on video analytics accuracy. We present Chameleon, a controller that dynamically picks the best configurations for existing NN-based video analytics pipelines. The key challenge in Chameleon is that in theory, adapting configurations frequently can reduce resource consumption with little degradation in accuracy, but searching a large space of configurations periodically incurs an overwhelming resource overhead that negates the gains of adaptation. The insight behind Chameleon is that the underlying characteristics (e.g., the velocity and sizes of objects) that affect the best configuration have enough temporal and spatial correlation to allow the search cost to be amortized over time and across multiple video feeds. For example, using the video feeds of five traffic cameras, we demonstrate that compared to a baseline that picks a single optimal configuration offline, Chameleon can achieve 20-50% higher accuracy with the same amount of resources, or achieve the same accuracy with only 30--50% of the resources (a 2-3X speedup).

Demystifying Deep Learning in Networking

We are witnessing a surge of efforts in networking community to develop deep neural networks (DNNs) based approaches to networking problems. Most results so far have been remarkably promising, which is arguably surprising given how intensively these problems have been studied before. Despite these promises, there has not been much systematic work to understand the inner workings of these DNNs trained in networking settings, their generalizability in different workloads, and their potential synergy with domain-specific knowledge. The problem of model opacity would eventually impede the adoption of DNN-based solutions in practice. This position paper marks the first attempt to shed light on the interpretability of DNNs used in networking problems. Inspired by recent research in ML towards interpretable ML models, we call upon this community to similarly develop techniques and leverage domain-specific insights to demystify the DNNs trained in networking settings, and ultimately unleash the potential of DNNs in an explainable and reliable way.