Amr Rizk

A Guide to the Stochastic Network Calculus

The aim of the stochastic network calculus is to comprehend statistical multiplexing and scheduling of non-trivial traffic sources in a framework for end-to-end analysis of multi-node networks. To date, several models, some of them with subtle yet important differences, have been explored to achieve these objectives. Capitalizing on previous works, this paper contributes an intuitive approach to the stochastic network calculus, where we seek to obtain its fundamental results in the possibly easiest way. In detail, the method that is assembled in this work uses moment generating functions, known from the theory of effective bandwidths, to characterize traffic arrivals and network service. Thereof, affine envelope functions with an exponentially decaying overflow profile are derived to compute statistical end-to-end backlog and delay bounds for networks.

A measurement study on the application-level performance of LTE

Many of todays Internet applications such as mobile web browsing and live video streaming are delay and throughput sensitive. In face of the great success of cellular networking, especially with the advent of the high-speed LTE access tech-nology, it is noteworthy that there is little consensus on the performance experienced by applications running over cellular networks. In this paper we present application-level performance results measured in a major commercial LTE network. We replicate measurements in a wired access network to provide a reference for the wireless results. We investigate the performance of common web application scenarios over LTE. In addition, we deploy controlled measurement nodes to discover transparent middleboxes in the LTE network. The introduction of middle-boxes to LTE results in a faster connection establishment on the client side and notable performance gain for HTTP. However, this improvement comes at the price of ambiguity as some middlebox operations may introduce unnecessary timeouts. Further, we pinpoint LTE specific delays that arise from network signalling, energy saving algorithms and Hybrid Automatic Repeat reQuest (HARQ). Our analysis provides insights into the interaction between transport protocols and LTE.

Taming SDN Controllers in Heterogeneous Hardware Environments

The ability to deploy applications which control network resources using a global view is a major benefit of the SDN paradigm. The SDN dataplane is comprised of a topology of forwarding elements programmed by a centralized controller over a common, well-defined interface. We believe that the heterogeneity of forwarding devices is an inherent property of software defined networks rather than a side effect. Therefore it is vital to design SDN frameworks in manner which explicitly takes variable hardware capabilities into account. In this work we argue that the concurrent operation of switches with diverse capacities for control message processing leads to unpredictable delays in SDN applications. To address this issue, we use a queuing model to characterize the service of a switchs control interface. Furthermore, we outline a measurement based-approach to derive the corresponding parameters. We propose a simple interface extension for controller frameworks which enables operators to configure delay bounds for transmitted control messages. As a result, developers can create SDN applications which exhibit consistent and predicable behavior.

SQUAD: a spectrum-based quality adaptation for dynamic adaptive streaming over HTTP

The application-layer based control loops of dynamic adaptive streaming over HTTP (DASH) make video bitrate selection a complex problem. In this work, we review and present new insights into the challenges of DASH rate adaptation. We identify several critical issues that contribute to the degradation of DASH performance with respect to the rate control loops of DASH and TCP. We then introduce a novel DASH quality adaptation algorithm SQUAD, which is specifically designed to ensure high quality of experience (QoE). We implement and test our algorithm together with a number of state-of-the-art quality adaptation algorithms. Through extensive experiments on both testbed and cross-Atlantic Internet scenarios, we show that by sacrificing little to none in average quality bitrate, SQUAD provides significantly better QoE in terms of number and magnitude of quality switches.

Sample Path Bounds for Long Memory FBM Traffic

Fractional Brownian motion (fBm) emerged as a useful model for self-similar and long-range dependent Internet traffic. Asymptotic, respectively, approximate performance measures are known from large deviations theory for single queuing systems with fBm traffic. In this paper we prove a rigorous sample path envelope for fBm that complements previous results. We find that both approaches agree in their outcome that overflow probabilities for fBm traffic have a Weibull tail. We show numerical results on the impact of the variability and the correlation of fBm traffic on the queuing performance.

AutoEmbed: automated multi-provider virtual network embedding

We present AutoEmbed, a fully-automated framework for VN embedding across multiple substrate networks. To automate VN embedding, AutoEmbed deploys functions over three layers: (i) Service Providers, (ii) VN Providers, and (iii) Infrastructure Providers (InPs). AutoEmbed enables VN Providers to partition VN requests among multiple substrate networks based on resource and network topology information that is not treated as confidential by InPs. Subsequently, each VN segment is mapped by the corresponding InP onto its substrate network. AutoEmbed enables the evaluation of various aspects of multi-provider VN embedding, such as the efficiency and scalability of embedding algorithms, the impact of different levels of information disclosure on VN embedding efficiency, and the suitability of VN request specifications.

Statistical end-to-end performance bounds for networks under long memory FBM cross traffic

Fractional Brownian motion (fBm) became known as a useful model for Internet traffic incorporating its self-similar and long-range dependent properties. In this paper we derive end-to-end performance bounds for a through flow in a network of tandem queues under fBm cross traffic. We build on a previously derived sample path envelope for fBm, which possesses a Weibullian decay of overflow probabilities. We employ the sample path envelope and the concept of leftover service curves to model the remaining service after scheduling fBm cross traffic at a system. Using composition results for tandem systems from the stochastic network calculus we derive end-to-end statistical performance bounds for individual flows in networks under fBm cross traffic. We discover that these bounds grow in O(n(log n)1/2−2H) for n systems in series where H is the Hurst parameter of the fBm cross traffic. We show numerical results on the impact of the variability and the correlation of fBm traffic on network performance.

Network Assisted Content Distribution for Adaptive Bitrate Video Streaming

State-of-the-art Software Defined Wide Area Networks (SD-WANs) provide the foundation for flexible and highly resilient networking. In this work we design, implement and evaluate a novel architecture (denoted SABR) that leverages the benefits of SDN to provide network assisted Adaptive Bitrate Streaming. With clients retaining full control of their streaming algorithms we clearly show that by this network assistance, both the clients and the content providers benefit significantly in terms of QoE and content origin offloading. SABR utilizes information on available bandwidths per link and network cache contents to guide video streaming clients with the goal of improving the viewers QoE. In addition, SABR uses SDN capabilities to dynamically program flows to optimize the utilization of CDN caches.; AB@Backed by our study of SDN assisted streaming we discuss the change in the requirements for network-to-player APIs that enables flexible video streaming. We illustrate the difficulty of the problem and the impact of SDN-assisted streaming on QoE metrics using various well established player algorithms. We evaluate SABR together with state-of-the-art DASH quality adaptation algorithms through a series of experiments performed on a real-world, SDN-enabled testbed network with minimal modifications to an existing DASH client. Our measurements show the substantial improvement in cache hitrates in conjunction with SABR indicating a rich design space for jointly optimized SDN-assisted caching architectures for video streaming applications.

Not so QUIC: A Performance Study of DASH over QUIC

Despite known QoE shortcomings, Dynamic Adaptive Streaming over HTTP (DASH) has been tied with TCP for many years now. The advent of HTTP/2 powered by transport protocols such as QUIC provides an excellent opportunity to revisit adaptive bitrate streaming with respect to QoE. QUIC promises improved congestion control, zero-RTT connection establishment and multiplexing logical streams. In this work, we adapt state-of-the-art DASH players with buffer-based and hybrid (rate/buffer-based) quality adaptation logic to use QUIC. Our main focus lies in contrasting the QoE performance of DASH algorithms running on top of QUIC versus TCP in various environments. Interestingly, we find through testbed and Internet measurements that QUIC does not provide a boost to current DASH algorithms but instead a degradation in the chosen quality bitrates.

Measurement-based flow characterization in centrally controlled networks

In this work we outline a framework for measurement-based performance evaluation in SDN environments. The SDN paradigm, which is based on a strict separation of the network logic from the underlying physical substrate, necessitates a comprehensive global view of the network state. To augment the network representation, we propose mechanisms for extracting traffic characteristics from network observations which are used to derive performance metrics. Such metrics can be exploited by SDN applications to optimize the performance of SDN services. Given the bursty nature of network traffic and the well known adverse impact of this property on network performance, we propose an approach for extracting flow autocorrelations from switch counters. Our main contribution is a random sampling approach that reduces the monitoring overhead while enabling a fine grained characterization of the flow autocorrelation structure. We analytically evaluate the impact of random sampling and demonstrate how services may use the estimated traffic properties to compute useful performance metrics.