Matthew K. Mukerjee

Supporting network evolution and incremental deployment with XIA

eXpressive Internet Architecture (XIA) [1] is an architecture that natively supports multiple communication types and allows networks to evolve their abstractions and functionality to accommodate new styles of communication over time. XIA embeds an elegant mechanism for handling unforeseen communication types for legacy routers. In this demonstration, we show that XIA overcomes three key barriers in network evolution (outlined below) by (1) allowing end-hosts and applications to start using new communication types (e.g., service and content) before the network supports them, (2) ensuring that upgrading a subset of routers to support new functionalities immediately benefits applications, and (3) using the same mechanisms we employ for 1 and 2 to incrementally deploy XIA in IP networks.

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.

Using Indirect Routing to Recover from Network Traffic Scheduling Estimation Error

Increasingly, proposals for new datacenter networking fabrics employ some form of traffic scheduling-often to avoid congestion, mitigate queuing delays, or avoid timeouts. Fundamentally, practical implementations require estimating upcoming traffic demand. Unfortunately, as our results show, it is difficult to accurately predict demand in typical datacenter applications more than a few milliseconds ahead of time. We explore the impact of errors in demand estimation on traffic scheduling in circuit-switched networks. We show that even relatively small estimation errors such as shifting the arrival time of at most 30% of traffic by a few milliseconds can lead to suboptimal schedules that dramatically reduce network efficiency. Existing systems cope by provisioning extra capacity-either on each circuit, or through the addition of a separate packet-switched fabric. We show through simulation that indirect traffic routing is a powerful technique for recovering from the inefficiencies of suboptimal scheduling under common datacenter workloads, performing as well as networks with 16% extra circuit bandwidth or a packet switch with 6% of the circuit bandwidth.

Redesigning CDN-Broker Interactions for Improved Content Delivery

Various trends are reshaping Internet video delivery: exponential growth in video traffic, rising expectations of high video quality of experience (QoE), and the proliferation of varied content delivery network (CDN) deployments (e.g., cloud computing-based, content provider-owned datacenters, and ISP-owned CDNs). More fundamentally though, content providers are shifting delivery from a single CDN to multiple CDNs, through the use of a content broker. Brokers have been shown to invalidate many traditional delivery assumptions (e.g., shifting traffic invalidates short- and long-term traffic prediction) by not communicating their decisions with CDNs. In this work, we analyze these problems using data from a CDN and a broker. We examine the design space of potential solutions, finding that a marketplace design (inspired by advertising exchanges) potentially provides interesting tradeoffs. A marketplace allows all CDNs to profit on video delivery through fine-grained pricing and optimization, where CDNs learn risk-adverse bidding strategies to aid in traffic prediction. We implement a marketplace-based system (which we dub Video Delivery eXchange or VDX) in CDN and broker data-driven simulation, finding significant improvements in cost and data-path distance.

Wireless map-based handoffs for mobile robots

Most wireless solutions today are centered around people-centric devices like laptops and cell phones that are insufficient for mobile robots. The key difference is that people-centric devices use wireless connectivity in bursts under primarily stationary settings while mobile robots continuously transmit data even while moving. When mobile robots use existing wireless solutions, it results in intolerable and seemingly random interruptions in wireless connectivity when moving [1]. These wireless issues stem from suboptimal switching across wireless infrastructure access points (APs), also called AP handoffs. These poor handoff decisions are due to stateless handoff algorithms that make wireless decisions solely from immediate and noisy scans of surrounding wireless conditions. In this paper, we propose to overcome these motion-based wireless connectivity issues for autonomous robots using highly informed handoff algorithms that combine fine-grain wireless maps with accurate robot localization. Our results show significant wireless performance improvements for continuously moving robots in real environments without any modifications to the wireless infrastructure.