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Research

The Research team at Verizon Digital Media Services (VDMS) is an interdisciplinary group of researcher scientists and engineers that work on the Edgecast Content Delivery Network (CDN) to make it the fastest, most reliable and secure content delivery platform in the world. The team works on Internet-scale technologies that influence how our services cache and deliver content, shape and route traffic, and process and analyze data.

Papers

2017

Suffering from Buffering? Detecting QoE Impairments in Live Video Streams

Authors

Abstract

Fueled by increasing network bandwidth and de- creasing costs, the popularity of over-the-top large-scale live video streaming has dramatically increased over the last few years. In this paper, we present a measurement study of adaptive bitrate video streaming for a large-scale live event. Using logs from a commercial content delivery network, we study live video delivery for the annual Academy Awards event that was streamed by hundreds of thousands of viewers in the United States. We analyze the relationship between Quality-of-Experience (QoE) and user engagement. We first study the impact of buffering, average bitrate, and bitrate fluctuations on user engagement. To account for interdependencies among QoE metrics and other confounding factors, we use quasi-experiments to quantify the causal impact of QoE metrics on user engagement. We further design and implement a Principal Component Analysis (PCA) based technique to detect live video QoE impairments in real- time. We use PCA and Hampel filters to detect QoE impairments and report 92% accuracy with 20% increase in true positive rate with our proposed approach as compared to baselines.

Published

Peering vs. Transit: Performance Comparison of Peering and Transit Interconnections

Authors

Abstract

The economic aspects of peering and transit in- terconnections between ISPs have been extensively studied in prior literature. Prior research primarily focuses on the economic issues associated with establishing peering and transit connectiv- ity among ISPs to model peer and transit selection strategies. Performance comparisons, on the other hand, while understood intuitively, have not been empirically quantified and incorporated in such models. To fill this gap, in this paper we conduct a large scale measurement based performance comparison of peering and transit interconnection strategies. We use JavaScript to conduct application layer latency measurements between 510K clients in 900 access ISPs and multi-homed CDN servers located at 33 IXPs around the world. Overall, we find that peering paths significantly outperformed transit paths for 91% ASes in our data. Peering paths have significantly less propagation delays as compared to transit paths for more than 95% of ASes. Peering paths outperform transit paths in terms of propagation delay due to shorter path lengths. Peering paths also have significantly less queueing delays as compared to transit paths for more than 50% ASes.

Published

Distributed Load Balancing in Key-Value Networked Caches

Authors

Abstract

Modern web services rely on a network of distributed cache servers to efficiently deliver content to users. Load imbalance among cache servers can substantially degrade content delivery performance. Due to the skewed and dynamic nature of real-world workloads, cache servers that serve viral content experience higher load as compared to other cache servers. We propose a novel distributed load balancing protocol called Meezan to address the load imbalance among cache servers. Meezan replicates popular objects to mitigate skewness and adjusts hash space boundaries in response to load dynamics in a novel way. Our theoretical analysis shows that Meezan achieves near perfect load balancing for a wide range of operating parameters. Our trace driven simulations shows that Meezan reduces load imbalance by up to 52% as compared to prior solutions.

Published

2016

Riptide: Jump-Starting Back-Office Connections in Cloud Systems

Authors

Abstract

Large-scale cloud networks are constantly driven by the need for improved performance in communication between datacenters. Indeed, such back-office communication makes up a large fraction of traffic in many cloud environments. This communication often occurs frequently, carrying control messages, coordination and load balancing information, and customer data. However, ensuring such inter-datacenter traffic is delivered efficiently requires optimizing connections over large physical distances, which is non-trivial. Worse still, many large cloud networks are subject to complex configuration and administrative restrictions, limiting the types of solutions that can be implemented. In this paper, we propose improving the efficiency of datacenter to datacenter communication by learning the congestion level of links in between. We then use this knowledge to inform new connections made between the relevant datacenters, allowing us to eliminate the overhead associated with traditional slow-start processes in new connections. We further present Riptide, a tool which implements this approach. We present the design and implementation details of Riptide, showing that it can be easily executed on modern Linux servers deployed in the real world. We further demonstrate that it successfully reduces total transfer times in a production global-scale content delivery network (CDN), providing up to a 30% decrease in tail latency. We further show that Riptide is simple to deploy and easy to maintain within a complex existing network.

Published

Characterizing Caching Workload of a Large Commercial Content Delivery Network

Authors

Abstract

Content Delivery Networks (CDNs) have emerged as a dominant mechanism to deliver content over the Internet. Despite their importance, to our best knowledge, large-scale performance analysis of CDN cache performance is lacking in prior literature. A CDN serves many content publishers simultaneously and thus has unique workload characteristics; it typically deals with extremely large content volume and high content diversity from multiple content publishers. CDNs also have unique performance metrics; other than hit ratio, CDNs also need to minimize network and disk load on cache servers. In this paper, we present measurement and analysis of caching workload at a large commercial CDN serving thousands of popular content publishers. Using detailed logs from four geo- graphically distributed CDN cache servers, we analyze over 600 million content requests accounting for more than 1.3 petabytes worth of traffic. We analyze CDN workload from a wide range of perspectives, including request composition, size, popularity, and temporal dynamics. Using real-world logs, we also evaluate cache replacement algorithms, including two enhancements designed based on our CDN workload analysis: N-hit and content-aware caching. The results show that these enhancements achieve substantial performance gains in terms of cache hit ratio, disk load, and origin traffic volume.

Published

QoE Analysis of a Large-Scale Live Video Streaming Event

Authors

Abstract

Streaming video has received a lot of attention from industry and academia. In this work, we study the characteristics and challenges associated with large-scale live video delivery. Using logs from a commercial Content Delivery Network (CDN), we study live video delivery for a major entertainment event that was streamed by hundreds of thousands of viewers in North America. We analyze Quality-of-Experience (QoE) for the event and note that a significant number of users suffer QoE impairments. As a consequence of QoE impairments, these users exhibit lower engagement metrics.

Published