A Two-Tiered On-Line Server-Side Bandwidth Reservation Framework for the Real-Time Delivery of Multiple Video Streams

The advent of virtualization and cloud computing technologies necessitates the development of effective mechanisms for the estimation and reservation of resources needed by content providers to deliver large numbers of video-on-demand (VOD) streams through the cloud. Unfortunately, capacity planning for the QoS-constrained delivery of a large number of VOD streams is inherently difficult as VBR encoding schemes exhibit significant bandwidth variability. In this paper, we present a novel resource management scheme to make such allocation decisions using a mixture of per-stream reservations and an aggregate reservation, shared across all streams to accommodate peak demands. The shared reservation provides capacity slack that enables statistical multiplexing of peak rates, while assuring analytically bounded frame-drop probabilities, which can be adjusted by trading off buffer space (and consequently delay) and bandwidth. Our two-tiered bandwidth allocation scheme enables the delivery of any set of streams with less bandwidth (or equivalently with higher link utilization) than state-of-the-art deterministic smoothing approaches. The algorithm underlying our proposed frame-work uses three per-stream parameters and is linear in the number of servers, making it particularly well suited for use in an on-line setting. We present results from extensive trace-driven simulations, which confirm the efficiency of our scheme especially for small buffer sizes and delay bounds, and which underscore the significant realizable bandwidth savings, typically yielding losses that are an order of magnitude or more below our analytically derived bounds.

On the Emergence of Highly Variable Distributions in the Autonomous System Topology

Recent studies have noted that vertex degree in the autonomous system (AS) graph exhibits a highly variable distribution [15, 22]. The most prominent explanatory model for this phenomenon is the Barabási-Albert (B-A) model [5, 2]. A central feature of the B-A model is preferential connectivity—meaning that the likelihood a new node in a growing graph will connect to an existing node is proportional to the existing node’s degree. In this paper we ask whether a more general explanation than the B-A model, and absent the assumption of preferential connectivity, is consistent with empirical data. We are motivated by two observations: first, AS degree and AS size are highly correlated [11]; and second, highly variable AS size can arise simply through exponential growth. We construct a model incorporating exponential growth in the size of the Internet, and in the number of ASes. We then show via analysis that such a model yields a size distribution exhibiting a power-law tail. In such a model, if an AS’s link formation is roughly proportional to its size, then AS degree will also show high variability. We instantiate such a model with empirically derived estimates of growth rates and show that the resulting degree distribution is in good agreement with that of real AS graphs.

On the Efficiency and Fairness of Transmission Control Loops: A Case for Exogenous Losses

We postulate that exogenous losses-which are typically regarded as introducing undesirable "noise" that needs to be filtered out or hidden from end points-can be surprisingly beneficial. In this paper we evaluate the effects of exogenous losses on transmission control loops, focusing primarily on efficiency and convergence to fairness properties. By analytically capturing the effects of exogenous losses, we are able to characterize the transient behavior of TCP. Our numerical results suggest that "noise" resulting from exogenous losses should not be filtered out blindly, and that a careful examination of the parameter space leads to better strategies regarding the treatment of exogenous losses inside the network. Specifically, we show that while low levels of exogenous losses do help connections converge to their fair share, higher levels of losses lead to inefficient network utilization. We draw the line between these two cases by determining whether or not it is advantageous to hide, or more interestingly introduce, exogenous losses. Our proposed approach is based on classifying the effects of exogenous losses into long-term and short-term effects. Such classification informs the extent to which we control exogenous losses, so as to operate in an efficient and fair region. We validate our results through simulations.

On the Impact of Low-Rate Attacks

Recent research have exposed new breeds of attacks that are capable of denying service or inflicting significant damage to TCP flows, without sustaining the attack traffic. Such attacks are often referred to as "low-rate" attacks and they stand in sharp contrast against traditional Denial of Service (DoS) attacks that can completely shut off TCP flows by flooding an Internet link. In this paper, we study the impact of these new breeds of attacks and the extent to which defense mechanisms are capable of mitigating the attack's impact. Through adopting a simple discrete-time model with a single TCP flow and a nonoblivious adversary, we were able to expose new variants of these low-rate attacks that could potentially have high attack potency per attack burst. Our analysis is focused towards worst-case scenarios, thus our results should be regarded as upper bounds on the impact of low-rate attacks rather than a real assessment under a specific attack scenario.