Christine
Chung's Publications

*Revenue
Maximization in Online Dial a Ride, *with Ananya
Christman, Nicholas Jaczko,
Marina Milan, Anna Vasilchenko, and Scott Westvold. ATMOS 2017 (17th Workshop on
Algorithmic Approaches for Transportation Modeling, Optimization, and Systems).

We study a
variation of the Online-Dial-a-Ride Problem where each request comes with not
only a source, destination and release time, but also has an associated
revenue. The server’s goal is to maximize its total revenue within a given time
limit, T. We show that the competitive ratio is unbounded for any
deterministic online algorithm for the problem. We then provide a 3-competitive
algorithm for the problem in a uniform metric space and a 6-competitive
algorithm for the general case of weighted graphs (under reasonable assumptions
about the input instance). We conclude with an experimental evaluation of our
algorithm in simulated settings inspired by real-world Dial-a-Ride data.
Experimental results show that our algorithm performs well when compared to an
offline version of the algorithm and a greedy algorithm.

*How well
do Doodle polls do?*, with Danya Alrawi ’16 (undergraduate research student) and Barbara
Anthony. SocInfo 2016
(8th International Conference on Social Informatics)*.*

Web-based
Doodle polls, where respondents indicate their availability for a collection of
times provided by the poll initiator, are an increasingly common way of
selecting a time for an event or meeting.
Yet group dynamics can markedly influence an individual's response, and
thus the overall solution quality. Via
theoretical worst-case analysis, we analyze certain common behaviors of Doodle
poll respondents, including when participants are either more generous with or
more protective of their time, showing that deviating from one's ``true
availability" can have a substantial impact on the overall quality of the
selected time.

Serve or skip: the power of
rejection in online bottleneck matching, with Barbara Anthony. *Journal
of Combinatorial Optimization, *32(4), 1232-1253, November 2016. Earlier version appeared in COCOA 2014 (The 8^{th}
Annual International Conference on Combinatorial Optimization and
Applications).

We consider
the online matching problem, where n server-vertices lie in a metric space and
n request-vertices that arrive over time each must immediately be permanently
assigned to a server-vertex. We focus on the egalitarian bottleneck objective,
where the goal is to minimize the maximum distance between any request and its
server. It has been demonstrated that while there are effective algorithms for
the utilitarian objective (minimizing total cost) in the resource augmentation
setting where the offline adversary has half the resources, these are not
effective for the egalitarian objective. Thus, we propose a new Serve-or-Skip bicriteria analysis model, where the online algorithm may
reject or skip up to a specified number of requests, and propose two greedy
algorithms: GriNN(t)
and Grin*(t).

Fairness in employee scheduling, with Erica Stockwell-Alpert ‘14 (undergraduate research student). MISTA 2015 (Multidisciplinary International Conference on Scheduling Theory and Applications).

We consider the
problem of assigning shifts to employees such that (1) all shift coverage
requirements are satisfied, (2) employees are available to work all shifts they
are assigned, (3) the number of total hours available for distribution is not
exceeded, and (4) each employee is assigned the number of hours they need. Our
focus in this work is on employee satisfaction (the percentage of their desired
hours an employee is assigned) and fairness (the employee satisfaction level
should be as uniform as possible). Because the problem is NP-hard, we propose
an approximation algorithm for maximizing the minimum employee satisfaction.

Competitive cost-savings in data
stream management systems, with Shenoda Guirguis and Anastasia Kurdia. COCOON 2014 (The 20^{th}
International Computing and Combinatorics
Conference).

In
Continuous Data Analytics and in monitoring applications, hundreds of similar
Aggregate Continuous Queries (ACQs) are registered at the Data Stream
Management System (DSMS) to continuously monitor the infinite input stream of
data tuples. Optimizing the processing of these ACQs is crucial in order for
the DSMS to operate at the adequate required scalability. One optimization
technique is to share the results of partial aggregation operations between
different ACQs on the same data stream. However, finding the query execution
plan that attains maximum reduction in total plan cost is computationally
expensive. Weave Share, a multiple ACQs
optimizer that computes query plans in a greedy fashion, was recently shown in
experiments to achieve more than an order of magnitude improvement over the
best existing alternatives. In this
paper we prove that Weave Share approximates the optimal cost-savings to within
a factor of 4 for a practical variant of the problem.

Online bottleneck matching,
with Barbara Anthony. *Journal of Combinatorial Optimization*,
Volume 27, Issue 1, pp. 100-114, January 2014.
(Published online: Feb 2013. Earlier
version appeared in COCOA 2012.)

We consider
the online bottleneck matching problem, where k server-vertices lie in a metric
space and k request-vertices that arrive over time each must immediately be
permanently assigned to a server-vertex.
The goal is to minimize the maximum distance between any request and its
server. Because no algorithm can have a
competitive ratio better than O(k) for this problem, we use resource
augmentation analysis to examine the performance of three algorithms: the naive Greedy algorithm, Permutation, and
Balance. We show that while the
competitive ratio of Greedy improves from exponential (when each server-vertex
has one server) to linear (when each server-vertex has two servers), the
competitive ratio of Permutation remains linear. The competitive ratio of Balance is also
linear with an extra server at each server-vertex, even though it has been
shown that an extra server makes it constant-competitive for the min-weight
matching problem.

Data plan throttling: a
simple consumer choice mechanism, with Barbara Anthony. Proceedings of the IEEE Global Communications
Conference (GLOBECOM 2013), pp. 3173-3178, December 2013.

Despite only
a small portion of unlimited data plan users experiencing throttling each
month, it is a prominent source of complaints from users and a significant
concern for mobile network operators. We propose a simple mechanism that allows
users to choose when they want their data transmission "fast," and
when they want it "slow." Users still have the same cap on total
high-speed transfer before being throttled, and hence may still be subject to
throttling, but now they are given some control. We propose a basic model of
payoffs, and demonstrate that the proposed mechanism would be preferable to the
user over the throttling policies currently in place. We then consider the
impacts that extend beyond a single user, and provide a framework for
determining the aggregate effects of such a mechanism.

Auction-based
admission control for continuous queries in a multi-tenant DSMS, with Lory Al Moakar, Panos Chrysanthis, Shenoda Guirguis, Alexandros Labrinidis,
Panayiotis Neophytou, and Kirk Pruhs. *International Journal of Next-Generation
Computing*, Vol 3, No 3,
November 2012.

The growing popularity of monitoring applications and “Big Data”
analytics used by a variety of users will lead to a multi-tenant data stream
management system. This paper deals with the problem of admission control of
continuous queries, where the stream processing resources are sold to the end
users. We employ variable pricing by means of auction-based mechanisms. The
admission control auction mechanism determines which queries to admit, and how
much to charge the user for each query in a way that maximizes system revenue.
The admission mechanism is required to be strategyproof
and sybil-immune, incentivizing users to use the
system honestly. Specifically, we require that each user maximizes her payoff
by bidding her true value of having a query run. We further consider the
requirement that the mechanism be sybil-immune: that
is, no user can increase her payoff by submitting queries that she does not
value. Given the above requirements, the main challenges come from the
difficulty of effectively utilizing shared processing of continuous queries. We
design several payment mechanisms and experimentally evaluate them.

Completion time scheduling and the
WSRPT algorithm, with Bo Xiong, ‘13
(undergraduate research student). ISCO
2012 (International Symposium on Combinatorial Optimization).

We consider
the online scheduling problem of minimizing the total weighted and unweighted
completion time on identical parallel machines with preemptible
jobs. We show a new general lower bound of 21/19 ≈ 1.105 on
the competitive ratio of any deterministic online algorithm for the unweighted
problem and 16−14√11≈1.114 for the weighted problem. We then
analyze the performance of the natural online algorithm WSRPT (Weighted
Shortest Remaining Processing Time). We show that WSRPT is 2-competitive. We
also prove that the lower bound on the competitive ratio of WSRPT for this
problem is 1.215.

The power of fair pricing
mechanisms, with Katrina Ligett, Aaron Roth, and
Kirk Pruhs. *Algorithmica*,
Volume 63, Issue 3, pp. 634-644, July 2012.
(Published online: Nov 2011. Earlier version appeared in LATIN
2010.)

We explore
the revenue capabilities of truthful, monotone (“fair”) allocation and pricing
functions for resource-constrained auction mechanisms within a general
framework that encompasses unlimited supply auctions, knapsack auctions, and
auctions with general non-decreasing convex production cost functions. We study
and compare the revenue obtainable in each fair pricing scheme to the profit
obtained by the ideal omniscient multi-price auction. We show that for
capacitated knapsack auctions, no constant pricing scheme can achieve any
approximation to the optimal profit, but proportional pricing is as powerful as
general monotone pricing. In addition, for auction settings with arbitrary
bounded non-decreasing convex production cost functions, we present a
proportional pricing mechanism which achieves a poly-logarithmic approximation.
Unlike existing approaches, all of our mechanisms have fair (monotone) prices,
and all of our competitive analysis is with respect to the optimal profit
extraction.

Expanding
CS1: applications across the liberal arts, with Bridget Baird. *Journal
of Computing Sciences in Colleges*, 25(6), 47-54. (CCSCNE 2010) [Local
copy]

This paper
describes how applications in a variety of disciplines can enhance the teaching
of the CS1 course. Examples are given from a range of disciplines, including
mathematics, economics, linguistics, history, biology, art and music. The
applications are linked to the computer science concepts being discussed. Such
an approach broadens the appeal of the introductory course and also teaches
students valuable problem solving skills.

SRPT is 1.86-competitive for completion
time scheduling, with Tim Nonner and Alex
Souza. SODA 2010 (ACM-SIAM Symposium on
Discrete Algorithms).

We consider
the classical problem of scheduling preemptible jobs,
that arrive over time, on identical parallel machines. The goal is to minimize
the total completion time of the jobs. In standard scheduling notation of
Graham et al. [5], this problem is denoted P|r_j, pmtn|Σ_j c_j. A popular
algorithm called SRPT, which always schedules the unfinished jobs with shortest
remaining processing time, is known to be 2-competitive, see Phillips et al.
[13, 14]. This is also the best known competitive ratio for any online
algorithm. However, it is conjectured that the competitive ratio of SRPT is
significantly less than 2. Even breaking the barrier of 2 is considered a
significant step towards the final answer of this classical online problem. We
improve on this open problem by showing that SRPT is 1.86-competitive. This
result is obtained using the following method, which might be of general
interest: We define two dependent random variables that sum up to the
difference between the cost of an SRPT schedule and the cost of an optimal
schedule. Then we bound the sum of the expected values of these random
variables with respect to the cost of the optimal schedule, yielding the
claimed competitiveness. Furthermore, we show a lower bound of 21/19 for SRPT,
improving on the previously best known 12/11 due to Lu et al. [11].

Admission control mechanisms for
continuous queries in the cloud, with Lory Al Moakar, Panos Chrysanthis,
Shenoda Guirguis,
Alexandros Labrinidis, Panayiotis Neophytou,
and Kirk Pruhs.
ICDE 2010 (IEEE International Conference on Data Engineering).

Amazon,
Google, and IBM now sell cloud computing services. We consider the setting of a
for-profit business selling data stream monitoring/management services and we
investigate auction-based mechanisms for admission control of continuous
queries. When submitting a query, each user also submits a bid of how much she
is willing to pay for that query to run. The admission control auction
mechanism then determines which queries to admit, and how much to charge each
user in a way that maximizes system revenue while being strategyproof
and sybil immune, incentivizing users to use the
system honestly. Specifically, we require that each user maximizes her payoff
by bidding her true value of having her query run. We design several payment
mechanisms and experimentally evaluate them. We describe the provable game
theoretic characteristics of each mechanism alongside its performance with
respect to maximizing profit and total user payoff.

On the price of stability
for undirected network design, with Giorgos
Christodoulou, Katrina Ligett, Evangelia
Pyrga, and Rob van Stee. WAOA 2009 (Workshop on Approximation and
Online Algorithms).

We continue
the study of the effects of selfish behavior in the network design problem. We
provide new bounds for the price of stability for network design with fair cost
allocation for undirected graphs. We consider the most general case, for which
the best known upper bound is the Harmonic number H_n
, where n is the number of agents, and the best previously known lower bound is
12/7 ≈ 1.778. We
present a nontrivial lower bound of 42/23 ≈ 1.8261.
Furthermore, we show that for two players, the price of stability is exactly
4/3, while for three players it is at least 74/48 ≈ 1.542 and
at most 1.65. These are the first improvements on the bound of H_n for general networks. In particular, this demonstrates
a separation between the price of stability on undirected graphs and that on
directed graphs, where H_n is tight. Previously, such
a gap was only known for the cases where all players have a shared source, and
for weighted players.

Stochastic stability in internet
router congestion games, with Evangelia Pyrga. SAGT 2009
(Symposium on Algorithmic Game Theory).
For a more complete version of this work, see the relevant chapter of my
PhD thesis.

Congestion
control at bottleneck routers on the internet is a long standing problem. Many
policies have been proposed for effective ways to drop packets from the queues
of these routers so that network endpoints will be inclined to share router
capacity fairly and minimize the overflow of packets trying to enter the
queues. We study just how effective some of these queuing policies are when
each network endpoint is a self-interested player with no information about the
other players’ actions or preferences. By employing the adaptive learning model
of evolutionary game theory, we study policies such as Droptail,
RED, and the greedy-flow-punishing policy proposed by Gao et al. [10] to find
the stochastically stable states: the states of the system that will be reached
in the long run.

The price of stochastic anarchy,
with Katrina Ligett, Kirk Pruhs
and Aaron Roth. SAGT 2008 (Symposium on
Algorithmic Game Theory).

We consider
the solution concept of stochastic stability, and propose the price of
stochastic anarchy as an alternative to the price of (Nash) anarchy for
quantifying the cost of selfishness and lack of coordination in games. As a
solution concept, the Nash equilibrium has disadvantages that the set of
stochastically stable states of a game avoid: unlike Nash equilibria,
stochastically stable states are the result of natural dynamics of
computationally bounded and decentralized agents, and are resilient to small
perturbations from ideal play. The price of stochastic anarchy can be viewed as
a smoothed analysis of the price of anarchy, distinguishing equilibria that are
resilient to noise from those that are not. To illustrate the utility of
stochastic stability, we study the load balancing game on unrelated machines.
This game has an unboundedly large price of Nash anarchy even when restricted
to two players and two machines. We show that in the two player case, the price
of stochastic anarchy is 2, and that even in the general case, the price of
stochastic anarchy is bounded. We conjecture that the price of stochastic
anarchy is O(m), matching the price of strong Nash anarchy without requiring
player coordination. We expect that stochastic stability will be useful in
understanding the relative stability of Nash equilibria in other games where
the worst equilibria seem to be inherently brittle.

The online transportation
problem: on the exponential boost of one extra server, with Kirk Pruhs and Patchrawat Uthaisombut. LATIN
2008 (Latin American Theoretical Informatics Symposium).

We present a
poly-log-competitive deterministic online algorithm for the online
transportation problem on hierarchically separated trees when the online
algorithm has one extra server per site. Using metric embedding results in the
literature, one can then obtain a poly-log-competitive randomized online
algorithm for the online transportation on an arbitrary metric space when the
online algorithm has one extra server per site.