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649
Selfish Routing and the Price of Anarchy
 MATHEMATICAL PROGRAMMING SOCIETY NEWSLETTER
, 2007
"... Selfish routing is a classical mathematical model of how selfinterested users might route traffic through a congested network. The outcome of selfish routing is generally inefficient, in that it fails to optimize natural objective functions. The price of anarchy is a quantitative measure of this in ..."
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Cited by 252 (11 self)
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of this inefficiency. We survey recent work that analyzes the price of anarchy of selfish routing. We also describe related results on bounding the worstpossible severity of a phenomenon called Braess’s Paradox, and on three techniques for reducing the price of anarchy of selfish routing. This survey concentrates
HOW TO DETECT MIDDLEBOXES: GUIDELINES ON A METHODOLOGY VAHAB POURNAGHSHBAND 1, SEPIDEH HASHEMZADEH
"... Internet middleboxes such as VPNs, firewalls, and proxies can significantly change handling of traffic streams. They play an increasingly important role in various types of IP networks. If end hosts can detect them, these hosts can make beneficial, and in some cases, crucial improvements in security ..."
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Internet middleboxes such as VPNs, firewalls, and proxies can significantly change handling of traffic streams. They play an increasingly important role in various types of IP networks. If end hosts can detect them, these hosts can make beneficial, and in some cases, crucial improvements in security and performance But because middleboxes have widely varying behavior and effects on the traffic they handle, no single technique has been discovered that can detect all of them. Devising a detection mechanism to detect any particular type of middlebox interference involves many design decisions and has numerous dimensions. One approach to assist with the complexity of this process is to provide a set of systematic guidelines. This paper is the first attempt for introducing a set of general guidelines (as well as the rationale behind them) to assist researchers with devising detection methodologies to detect middleboxes by the endhosts. The presented guidelines take some inspiration from previous work of other researchers using various and often ad hoc approaches. These guidelines, however, are mainly based on our experience from conducting research in this area to detect such middleboxes. To assist researchers for using these guidelines we also provide an example of how to bring them into play for detection of network compression.
Faulttolerant and 3Dimensional Distributed Topology Control Algorithms in Wireless Multihop Networks
 in Proceedings of the 11th IEEE International Conference on Computer Communications and Networks (ICCCN
, 2002
"... We can control the topology of a multihop wireless network by varying the transmission power at each node. The lifetime of such networks depends on battery power at each node. This paper presents a distributed faulttolerant topology control algorithm for minimum energy consumption in these net ..."
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Cited by 86 (9 self)
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We can control the topology of a multihop wireless network by varying the transmission power at each node. The lifetime of such networks depends on battery power at each node. This paper presents a distributed faulttolerant topology control algorithm for minimum energy consumption in these networks. More precisely, we present algorithms which preserve the connectivity of a network upon failing of, at most, k nodes (k is constant) and simultaneously minimize the transmission power at each node to some extent. In addition, we present simulations to support the effectiveness of our algorithm. We also demonstrate some optimizations to further minimize the power at each node. Finally, we show how our algorithms can be extended to 3dimensions.
IncentiveCentered Design of MoneyFree Mechanisms
, 2013
"... This work is dedicated to my family: My sister Maria, my parents Giannis and Sofia, my brother Giorgos, and my wife Maria. iv Acknowledgements First and foremost I would like to thank my research advisor, Professor Richard Cole. Richard was always there for me throughout these five years at Courant; ..."
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hours with me discussing nontechnical issues regarding career and life related choices. As the years flew by, our brainstorming sessions became more and more enjoyable and these sessions are one of the things that I will miss the most. Second, I would like to thank Dr. Gagan Goel and Dr. Vahab Mirrokni
On maximizing Welfare when Utility Functions are Subadditive
 Proc. of ACM STOC
, 2006
"... We consider the problem of maximizing welfare when allocating m items to n players with subadditive utility functions. Our main result is a way of rounding any fractional solution to a linear programming relaxation to this problem so as to give a feasible solution of welfare at least half that of th ..."
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Cited by 90 (6 self)
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We consider the problem of maximizing welfare when allocating m items to n players with subadditive utility functions. Our main result is a way of rounding any fractional solution to a linear programming relaxation to this problem so as to give a feasible solution of welfare at least half that of the value of the fractional solution. This approximation ratio of 1/2 improves over an Ω(1 / log m) ratio of Dobzinski, Nisan and Schapira [STOC 2005]. We also show an approximation ratio of 1 − 1/e when utility functions are fractionally subadditive. A result similar to this last result was previously obtained by Dobzinski and Schapira [Soda 2005], but via a different rounding technique that requires the use of a so called ”XOS oracle”. The randomized rounding techniques that we use are oblivious in the sense that they only use the primal solution to the linear program relaxation, but have no access to the actual utility functions of the players. This allows us to suggest new incentive compatible mechanisms for combinatorial auctions, extending previous work of Lavi
On spectrum sharing games
 In proc. of PODC 2004
, 2004
"... Each access point (AP) in a WiFi network must be assigned a channel for it to service users. There are only finitely many possible channels that can be assigned. Moreover, neighboring access points must use different channels so as to avoid interference. Currently these channels are assigned by admi ..."
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Cited by 78 (3 self)
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Each access point (AP) in a WiFi network must be assigned a channel for it to service users. There are only finitely many possible channels that can be assigned. Moreover, neighboring access points must use different channels so as to avoid interference. Currently these channels are assigned by administrators who carefully consider channel conflicts and network loads. Channel conflicts among APs operated by different entities are currently resolved in an ad hoc manner or not resolved at all. We view the channel assignment problem as a game, where the players are the service providers and APs are acquired sequentially. We consider the price of anarchy of this game, which is the ratio between the total coverage of the APs in the worst Nash equilibrium of the game and what the total coverage of the APs would be if the channel assignment were done by a central authority. We provide bounds on the price of anarchy depending on assumptions on the underlying network and the type of bargaining allowed between service providers. The key tool in the analysis is the identification of the Nash equilibria with the solutions to a maximal coloring problem in an appropriate graph. We relate the price of anarchy of these games to the approximation factor of local optimization algorithms for the maximum�colorable subgraph problem. We also study the speed of convergence in these games.
Market Sharing Games Applied to Content Distribution in AdHoc Networks
 MOBIHOC'04
, 2004
"... ..."
Approximation Algorithms for Data Placement in Arbitrary Networks
 in Proceedings of the 12th Annual ACMSIAM Symposium on Discrete Algorithms
, 2001
"... Abstract We develop approximation algorithms for the problem of placing replicated data in arbitrary networks, where the nodes may both issue requests for data objects and have capacity for storing data objects, so as to minimize the average dataaccess cost. We introduce the data placement problem ..."
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Cited by 81 (4 self)
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Abstract We develop approximation algorithms for the problem of placing replicated data in arbitrary networks, where the nodes may both issue requests for data objects and have capacity for storing data objects, so as to minimize the average dataaccess cost. We introduce the data placement problem tomodel this problem. We have a set of caches F, a set of clients D, and a set of data objects O. Each cache i can store at most ui data objects. Each client j 2 D has demand dj for a specific data object o(j) 2 O and has to be assigned to a cache that stores that object. Storing an object o in cache i incurs astorage cost of f oi, and assigning client j to cache i incurs an access cost of djcij. The goal is to find aplacement of the data objects to caches respecting the capacity constraints, and an assignment of clients
Tight approximation algorithms for maximum general assignment problems
 Proc. of ACMSIAM SODA
, 2006
"... A separable assignment problem (SAP) is defined by a set of bins and a set of items to pack in each bin; a value, fij, for assigning item j to bin i; and a separate packing constraint for each bin – i.e. for bin i, a family Ii of subsets of items that fit in bin i. The goal is to pack items into bin ..."
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Cited by 63 (7 self)
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A separable assignment problem (SAP) is defined by a set of bins and a set of items to pack in each bin; a value, fij, for assigning item j to bin i; and a separate packing constraint for each bin – i.e. for bin i, a family Ii of subsets of items that fit in bin i. The goal is to pack items into bins to maximize the aggregate value. This class of problems includes the maximum generalized assignment problem (GAP) 1) and a distributed caching problem (DCP) described in this paper. Given a βapproximation algorithm for finding the highest value packing of a single bin, we give 1. A polynomialtime LProunding based ((1 − 1 e)β)approximation algorithm. 2. A simple polynomialtime local search ( β approximation algorithm, for any ɛ> 0. β+1 − ɛ)Therefore, for all examples of SAP that admit an approximation scheme for the singlebin problem, we obtain an LPbased algorithm with (1 − 1 e − ɛ)approximation and a local search algorithm with ( 1 2 −ɛ)approximation guarantee. Furthermore, for cases in which the subproblem admits a fully polynomial approximation scheme (such as for GAP), the LPbased algorithm analysis can be strengthened to give a guarantee of 1 − 1 e. The best previously known approximation algorithm for GAP is a 1 2approximation by Shmoys and Tardos; and Chekuri and Khanna. Our LP algorithm is based on rounding a new linear programming relaxation, with a provably better integrality gap. To complement these results, we show that SAP and DCP cannot be approximated within a factor better than 1 − 1 e unless NP ⊆ DTIME(n O(log log n)), even if there exists a polynomialtime exact algorithm for the singlebin problem.
Results 1  10
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