[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications, where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on the existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this article, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art GNNs into four categories, namely, recurrent GNNs, convolutional GNNs, graph autoencoders, and spatial–temporal GNNs. We further discuss the applications of GNNs across various domains and summarize the open-source codes, benchmark data sets, and model evaluation of GNNs. Finally, we propose potential research directions in this rapidly growing field.

/pdf/a-comprehensive-survey-on-graph-neural-networks-4lw9i4v183.pdf

A Comprehensive Survey on Graph Neural Networks

Social Network Analysis

Netflix is the leading provider of on-demand Internet video streaming in the US and Canada, accounting for 29.7% of the peak downstream traffic in US. Understanding the Netflix architecture and its performance can shed light on how to best optimize its design as well as on the design of similar on-demand streaming services. In this paper, we perform a measurement study of Netflix to uncover its architecture and service strategy. We find that Netflix employs a blend of data centers and Content Delivery Networks (CDNs) for content distribution. We also perform active measurements of the three CDNs employed by Netflix to quantify the video delivery bandwidth available to users across the US. Finally, as improvements to Netflix's current CDN assignment strategy, we propose a measurement-based adaptive CDN selection strategy and a multiple-CDN-based video delivery strategy, and demonstrate their potentials in significantly increasing user's average bandwidth.

Unreeling netflix: Understanding and improving multi-CDN movie delivery

Recent spates of cyber-attacks and frequent emergence of applications affecting Internet traffic dynamics have made it imperative to develop effective techniques that can extract, and make sense of, significant communication patterns from Internet traffic data for use in network operations and security management. In this paper, we present a general methodology for building comprehensive behavior profiles of Internet backbone traffic in terms of communication patterns of end-hosts and services. Relying on data mining and information-theoretic techniques, the methodology consists of significant cluster extraction, automatic behavior classification and structural modeling for in-depth interpretive analyses. We validate the methodology using data sets from the core of the Internet. The results demonstrate that it indeed can identify common traffic profiles as well as anomalous behavior patterns that are of interest to network operators and security analysts.

/pdf/profiling-internet-backbone-traffic-behavior-models-and-3ekzs0iix5.pdf

Profiling internet backbone traffic: behavior models and applications

VBR compressed video is known to exhibit significant, multiple-time-scale bit rate variability. In this paper, we consider the transmission of stored video from a server to a client across a high speed network, and explore how the client buffer space can be used most effectively toward reducing the variability of the transmitted bit rate.We present two basic results. First, we present an optimal smoothing algorithm for achieving the greatest possible reduction in rate variability when transmitting stored video to a client with given buffer size. We provide a formal proof of optimality, and demonstrate the performance of the algorithm on a set of long MPEG-1 encoded video traces. Second, we evaluate the impact of optimal smoothing on the network resources needed for video transport, under two network service models: Deterministic Guaranteed service [1, 9] and Renegotiated CBR (RCBR) service [8, 7]. Under both models, we find the impact of optimal smoothing to be dramatic.

Supporting stored video: reducing rate variability and end-to-end resource requirements through optimal smoothing

We advocate the notion of service overlay network (SON) as an effective means to address some of the issues, in particular, end-to-end quality of service (QoS), plaguing the current Internet, and to facilitate the creation and deployment of value-added Internet services such as VoIP, Video-on-Demand, and other emerging QoS-sensitive services. The SON purchases bandwidth with certain QoS guarantees from the individual network domains via bilateral service level agreement (SLA) to build a logical end-to-end service delivery infrastructure on top of the existing data transport networks. Via a service contract, users directly pay the SON for using the value-added services provided by the SON. In this paper, we study the bandwidth provisioning problem for a SON which buys bandwidth from the underlying network domains to provide end-to-end value-added QoS sensitive services such as VoIP and Video-on-Demand. A key problem in the SON deployment is the problem of bandwidth provisioning, which is critical to cost recovery in deploying and operating the value-added services over the SON. The paper is devoted to the study of this problem. We formulate the bandwidth provisioning problem mathematically, taking various factors such as SLA, service QoS, traffic demand distributions, and bandwidth costs. Analytical models and approximate solutions are developed for both static and dynamic bandwidth provisioning. Numerical studies are also performed to illustrate the properties of the proposed solutions and demonstrate the effect of traffic demand distributions and bandwidth costs on SON bandwidth provisioning.

/pdf/service-overlay-networks-slas-qos-and-bandwidth-provisioning-188z1menqe.pdf

Service overlay networks: SLAs, QoS, and bandwidth provisioning

We deduce key design features behind the YouTube video delivery system by building a distributed active measurement infrastructure, and collecting and analyzing a large volume of video playback logs, DNS mappings and latency data. We find that the design of YouTube video delivery system consists of three major components: a “flat” video id space, multiple DNS namespaces reflecting a multi-layered logical organization of video servers, and a 3-tier physical cache hierarchy. We also uncover that YouTube employs a set of sophisticated mechanisms to handle video delivery dynamics such as cache misses and load sharing among its distributed cache locations and data centers.

/pdf/vivisecting-youtube-an-active-measurement-study-1qipny71r9.pdf

Zhi-Li Zhang

Papers

Unreeling netflix: Understanding and improving multi-CDN movie delivery

Profiling internet backbone traffic: behavior models and applications

Supporting stored video: reducing rate variability and end-to-end resource requirements through optimal smoothing

Service overlay networks: SLAs, QoS, and bandwidth provisioning

Vivisecting YouTube: An active measurement study