180 Park Ave - Building 103
Florham Park, NJ
http://www.jeffpang.net
Jeffrey Pang is a Senior Member of the Technical Staff at AT&T Labs - Research. He received his Ph.D and M.S. in Computer Science from Carnegie Mellon University and his B.A. in Computer Science from U.C. Berkeley. His research focus is on the intersection between the fields of networking, distributed systems, security, and mobility.
SMOG: A Cloud Platform for Seamless Wide Area Migration of Networked Games
Jeffrey Pang, Seungjoon Lee, Jacobus Van , Virajith Jalaparti, Matthew Caesar
The 11th Annual Workshop on Network and Systems Support for Games,
2012.
[PDF]
[BIB]
IEEE Copyright
This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in 2012 , 2012-11-22
{Highly interactive network applications such as online games are rapidly growing in popularity but remain challenging for game providers to support, due to their inherent need for low latency. While cloud computing has proven a useful infrastructure for other applications, existing cloud computing facilities are insufficient for games, due to the unpredictability of their workload, their demands on latency and scale, and the need to support game-specific requirements (e.g., players may wish to play with certain other players already in the game). In this work, we explore whether dynamic optimization of latency and scaling of games can be achieved by supplementing cloud computing infrastructure with seamless wide area virtual machine migration using network based route control. We propose SMOG, a framework that dynamically migrates game servers to their optimal location, and uses orchestrated route control to optimize the network path to the server to minimize observable effects of live server migration. Through deployment of a prototype implementation on a Tier-1 ISP’s backbone and a user study, we found SMOG can decrease average end-user latency by up to 60% while performing migration in a manner transparent to game players. While this paper’s focus is online games, SMOG is general enough to be used for a variety of latency-sensitive interactive applications such as video conferencing and interactive video streaming.}
Obtaining In-Context Measurements of Cellular Network Performance
Aaron Gember, Jeffrey Pang, Alexander Varshavsky, Ramon Caceres, Aditya Akella
ACM Internet Measurement Conference,
2012.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in 2012 , 2012-11-14.
{Network service providers, and other parties, require an accurate understanding of the performance cellular networks deliver to users. In particular, they often seek a measure of the network performance users experience solely when they are interacting with their device—a measure we call in-context. Acquiring such measures is challenging due to the many factors, including time and physical context, that influence cellular network performance. This paper makes two contributions. First, we conduct a large scale measurement study, based on data collected from a large cellular provider and from hundreds of controlled experiments, to shed light on the issues underlying in-context measurements. Our novel observations show that measurements must be conducted on devices which (i) recently used the network as a result of user interaction with the device, (ii) remain in the same macro-environment (e.g., indoors and stationary), and in some cases the same micro-environment (e.g., in the user’s hand), during the period between normal usage and a subsequent measurement, and (iii) are currently sending/receiving little or no user-generated traffic. Second, we design and deploy a prototype active measurement service for Android phones based on these key insights. Our analysis of 1650 measurements gathered from 12 volunteer devices shows that the system is able to obtain average throughput measurements that accurately quantify the performance experienced during times of active device and network usage.}
Characterizing Geospatial Dynamics of Application Usage in a 3G Cellular Data Network
Jeffrey Pang, Jia Wang, Lusheng Ji, Zubair Shafiq, Alex X. Liu
IEEE INFOCOM 2012,
2012.
[PDF]
[BIB]
IEEE Copyright
This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in IEEE INFOCOM 2012. , 2012-03-25
(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Internet Measurement Conference 2011. , 2012-03-25.
{Recent studies on cellular network measurement have provided the evidence that significant geospatial correlations, in terms of traffic volume and application access, exist in cellular network usage. Such geospatial correlation patterns provide local optimization opportunities to cellular network operators for handling the explosive growth in the traffic volume observed in recent years. In this paper, we aim to characterize the geospatial dynamics of application usage in a 3G cellular data network. Our analysis is based on two simultaneously collected traces from the radio sub-network (containing location records) and the core sub-network (containing traffic records) of an operational cellular network in the United States. To better understand the application usage in our data, we first cluster cell locations based on their application distributions and then study the geospatial dynamics of application usage across different geographical regions. Our study reveals that the cell clustering results are significantly different for traffic volume in terms of byte or packet count, session count, and unique user count distributions across different geographical regions. The results of our measurement study present operators with fine-grained opportunities to tune network parameter settings. However, our results also suggest that care should be exercised so that cells are not optimized solely with respect to traffic volume based on byte or packet count, or session count because this may negatively impact other low volume applications that most users in those cells use.}
Can you GET me now? Estimating the Time-to-First-Byte of HTTP transactions with Passive Measurements
Emir Halepovic, Jeffrey Pang, Oliver Spatscheck
ACM Internet Measurement Conference 2012,
2012.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in 2012 , 2012-11-14.
{Cellular network operators have a compelling interest to monitor HTTP transaction latency because it is an important component of the user experience. Existing techniques to monitor latency require active probing or use passive analysis to estimate round-trip time (RTT). Unfortunately, it is impractical to use active probing to monitor entire cellular networks, and RTT is only one component of HTTP latency in cellular networks. This paper presents a new passive tech- nique to estimate HTTP transaction latency that overcomes the scaling and completeness limitations of prior approaches. We validate our technique in an operational cellular network and present results for traffic in the wild.}
A First Look at Cellular Machine-to-Machine Traffic – Large Scale Measurement and Characterization
Zubair Shafiq, Lusheng Ji, Alex Liu, Jeffrey Pang, Jia Wang
SIGMETRICS,
2012.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in 2012 , 2012-06-11.
{Cellular network based Machine-to-Machine (M2M) communication is fast becoming a market-changing force for a wide spectrum of businesses and applications such as telematics, smart metering, point-of-sale terminals, and home security and automation systems. In this paper, we aim to answer the following important question: Does traffic generated by M2M devices impose new requirements and challenges for cellular network design and management? To answer this question, we take a first look at the characteristics of M2M traffic and compare it with traditional smartphone traffic. We have conducted our measurement analysis using a week-long traffic trace collected from a tier-1 cellular network in the United States. We characterize M2M traffic from a wide range of perspectives, including temporal dynamics, device mobility, application usage, and network performance.
Our experimental results show that M2M traffic exhibits significantly different patterns than smartphone traffic in multiple aspects. For instance, M2M devices have a much larger ratio of uplink to downlink traffic volume, their traffic typically exhibits different diurnal patterns, they are more likely to generate synchronized traffic resulting in bursty aggregate traffic volumes, and are less mobile compared to smartphones. On the other hand, we also find that M2M devices are generally competing with smartphones for net- work resources in co-located geographical regions. These and other findings suggest that better protocol design, more careful spectrum allocation, modified pricing schemes, and careful structuring of quality of service profiles may be needed to accommodate the rise of M2M devices.}
Predicting Handoffs in 3G Networks
Ramon Caceres, Jeffrey Pang, Alexander Varshavsky, Umar Javed, Dongsu Han, Srinivasan Sesah
3rd ACM SOSP Workshop on Networking, Systems, and Applications on Mobile Handhelds (MobiHeld),
2011.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in 3rd ACM SOSP Workshop on Networking, Systems, and Applications on Mobile Handhelds (MobiHeld) , 2011-10-23.
{}
Internet-scale Visualization and Detection of Performance Events
Shobha Venkataraman, Jeffrey Pang, Subhabrata Sen, Oliver Spatscheck
Usenix Annual Technical Conference,
2011.
[PDF]
[BIB]
USENIX Copyright
The definitive version was published in Proceedings of the Annual Technical Conference, Usenix. , 2011-06-15
{Operators typically monitor the performance of network server farms
using rule-based scripts to automatically flag "events of interest" on
an array of active and passive performance measurement feeds.
However, such automatic detection is typically limited to events with
known properties. A different challenge involves detecting the
"unknown unknowns" -- the events of interest whose properties are
unknown, and therefore, cannot be defined beforehand. Visualization
can significantly aid the rapid discovery of such unknown patterns, as
network operators, with domain expertise, may quickly notice
unexpected shifts in traffic patterns when represented visually.
However, the volume of Internet-wide raw performance data can easily
overwhelm human comprehension, and therefore, an effective
visualization needs to be sparse in representation, yet discriminating
of good and poor performance.
This paper presents a tool that can be used to visualize performance metrics at Internet-scale. At its core, the tool builds decision trees over the IP address space using performance measurements, so that IP addresses with similar performance characteristics are clustered together, and those with significant performance differences are separated. These decision trees need to be dynamic -- i.e., learnt online, and adapt to changes in the underlying network. We build these adaptive decision trees by extending online decision-tree learning algorithms to the unique challenges of classifying performance measurements across the Internet, and
our tool then visualizes these adaptive decision trees, distinguishing parts of the
network with good performance from those with poor performance. We
show that the differences in the visualized decision trees helps us
quickly discover new patterns of usage and novel anomalies in latency
measurements at a large server farm.
}
Identifying Diverse Usage Behaviors of Smartphone Apps
Qiang Xu, Alexandre Gerber, Z. Morley Mao, Jeffrey Pang, Shobha Venkataraman, Jeffrey Erman
in Proc. of ACM Internet Measurement Conference (IMC),
2011.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 20XX. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proc. of ACM Internet Measurement Conference (IMC). , 2011-11-01.
{as gateways to Internet services, rather than traditional web
browsers. Application marketplaces for iOS, Android, and
Windows Mobile platforms have made it attractive for developers
to deploy apps and easy for users to discover and
start using many network-enabled apps quickly. For example,
it was recently reported that the iOS AppStore has more
than 350K apps and more than 10 billion downloads. Furthermore,
the appearance of tablets and mobile devices with
other form factors, which also use these marketplaces, has
increased the diversity in apps and their user population.
Despite the increasing importance of apps as gateways to
network services, we have a much sparser understanding
of how, where, and when they are used compared to traditional
web services, particularly at scale. This paper takes
a first step in addressing this knowledge gap by presenting
results on smartphone app usage at a national level using
anonymized network measurements from a tier-1 cellular
carrier in the U.S. We identify traffic from distinct marketplace
apps based on HTTP signatures and present aggregate
results on their spatial and temporal prevalence, locality, and
correlation.}
Characterizing Fairness for 3G Wireless Networks
Vaneet Aggarwal, Rittwik Jana, Kadangode Ramakrishnan, Jeffrey Pang, N Shankaranarayanan
IEEE LANMAN 2011,
2011.
[PDF]
[BIB]
IEEE Copyright
This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in IEEE LANMAN 2011. , 2011-10-13
{The end to end system data performance over a 3G cellular network depends on many factors such as the number of users, interference, multipath propagation, radio resource management techniques as well as the interaction between these mechanisms and the transport protocol's flow and congestion mechanisms. Using controlled experiments in a public cell site, we investigate the interaction between TCP and the 3G UMTS/HSPA network's resource allocation, and its effect on fairness in the throughput achieved across multiple (up to 26) TCP flows in a loaded cell sector. Our field measurement results indicate that TCP fairness fluctuates significantly when the air interface (radio link) is the bottleneck. We also observe that TCP fairness is substantially better when the backhaul link (a fixed wired link) is the bottleneck, instead of the air interface. We speculate that the fairness of TCP flows is adversely impacted by the mismatch between the resource allocation mechanisms of TCP's flow and congestion control and that of the Radio Access Network (RAN).}
AccuLoc: Practical Localization of Performance Measurements in 3G Networks
Qiang Xu, Alexandre Gerber, Z. Morley Mao, Jeffrey Pang
in Proc. of ACM MobiSys,
2011.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Mobisys Conference , 2011-06-27.
{Operators of 3G data networks need to distinguish the performance
of each geographic area in their 3G networks to
detect and resolve localized network problems. This is because
the quality of the �last mile� radio link between 3G
base stations and end-user devices is a crucial factor in the
end-to-end performance that each user experiences. It is relatively
straightforward to measure the performance of all IP
traffic in the 3G network from a small number of vantage
points in the core network. However, the location information
available about each mobile device (e.g., the cell sector
that it is in) is often too stale to be accurate because
of user mobility. Moreover, it is impractical to collect fine grained
location information about all mobile devices on an
on-going basis in large 3G networks. Thus, it is not practical
to accurately assign IP performance measurements to
fine-grained geographic regions of the 3G network using off the-
shelf components. Fortunately, previous studies have observed
that human mobility patterns are very predictable. In
this paper, we exploit this predictability to develop a novel
clustering algorithm that accurately assigns IP performance
measurements to fine-grained geographic regions. At the
GGSNs, we can either localize a subscriber into only 4 cell
sectors with the accuracy of 70% over 5 consecutive days
based on a one-day snapshot of fine-grained 3GPP events,
or increase the accuracy 20% through lightweight handover
statistics hourly collected. We present results from a prototype
in a real 3G network that shows our clustering provides
more accurate performance localization results than existing
approaches. Our results also shed light on the mobility patterns
of 3G devices.}
Speed Testing without Speed Tests: Estimating Achievable Download Speed from Passive Measurements
Jeffrey Pang, Shobha Venkataraman, Oliver Spatscheck, Alexandre Gerber
in Proc. of ACM Internet Measurement Conference (IMC),
2010.
[PDF]
[BIB]
1 Copyright
(c) ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution.
The definitive version was published in ACM Internet Measurement Conference , 2010-11-01.
{How fast is the network? The speed at which real users can download content at different locations and at different times is an important metric for service providers. Knowledge of this speed helps determine where to provision more capacity and helps detect network problems. However, most network-level estimates of these speeds today are obtained using active �speed tests� that place substantial load on the network and are not necessarily representative of actual user experiences due to limited vantage points. These problems are exacerbated in wireless networks where the physical locations of users play an important role in performance. To redress these problems, this paper presents a new technique to estimate achievable download speed using only flow records collected passively. Estimating achievable speed passively is non-trivial because the measured throughput of real flows is often not comparable to the achievable steady-state TCP rate. This can be because, for example, flows are small and never exit TCP slow start or are rate-limited by the content-provider. Our technique addresses these issues by constructing a Throughput Index, a list of flow types that accurately estimate achievable speed. We show that our technique estimates achievable throughput more accurately than other techniques in a large 3G wireless network.}
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