e-mail: mavronic@ucy.ac.cy

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Algorithmic Game Theory Distributed Computing Networking Theory

Research Statement


I focused on three major areas: Algorithmic Game Theory, Distributed Computing and Networking Theory.


 1.  Algorithmic Game Theory


This is an emerging discipline at the interface between Computer Science and Game Theory, which recently attracted talents and demanded hard work. (See (RM2) for a research monograph on Algorithmic Game Theory, which P. Spirakis and myself are currently preparing.) I got interested in working in this area back in 1999, after I read the (by now seminal) paper of E. Koutsoupias and C. H. Papadimitriou "Worst-Case Equilibria" in STACS 1999, introducing the (now famous) KP model for selfish routing over the anarchical internet. That work introduced the Price of Anarchy as a measure of the worst-case performance loss in a computer system due to the selfish behavior of its users. So, the Price of Anarchy is the worst-case ratio of the system's Social Cost at a Nash equilibrium over the system's Optimum; at a Nash equilibrium, no user can unilaterally deviate to improve its own Individual Cost.


I soon succeeded in sharing my excitement about the new area with my closest colleague Paul Spirakis, with whom I published my first paper (C22) in it, which now appears as (J25) (but see also extensions in (C23)); that was also the first published work on the KP model following the original paper in STACS 1999. Together with Paul, we introduced the fully mixed Nash equilibrium, a mixed equilibrium with all probabilities strictly positive, which seemed amenable to tractable analysis. We provided the first tight bounds on Price of Anarchy for the special case of the fully mixed Nash equilibrium, but also a compendium of techniques for analyzing Nash equilibria in the KP model that influenced much subsequent work - that turned out to be my most cited work.


We went on with Paul in this promising endeavor, and together with D. Fotakis, S. Kontogiannis and E. Koutsoupias, we shortly thereafter presented in (C24) a thorough analysis of the structure and complexity of Nash equilibria for the KP model. Our findings about structure provided the first seed to studying the fully mixed Nash equilibrium as a candidate worst-case equilibrium. Our complexity results provided an efficient algorithm to compute a pure Nash equilibrium (for the KP model), based on the classical LPT scheduling algorithm of R. L. Graham; in contrast, we proved that finding a (pure) Nash equilibrium with certain optimality properties is an NP-complete problem. Further on, Paul and myself together with E. Koutsoupias presented in (C25) (which later appeared as (J16)) the first tight bounds on Price of Anarchy that hold for all Nash equilibria. This work introduced and analyzed a new variant of the classical balls-and-bins problem, and a new probabilistic tool called ball fusion, probably of more general interest and applicability, for the analysis of random processes.


As the interest in Algorithmic Game Theory was growing stronger around 2001, a group of European researchers, including E. Koutsoupias, B. Monien, P. Spirakis and myself, were merged into a consortium and succeeded in attracting European funds through the IST project FLAGS. One of the main topics of FLAGS was Algorithmic Game Theory; with support from FLAGS, we were able to push its frontier much further in the next three years.


In our already first trip under FLAGS, M. Gairing, T. Lucking, B. Monien, P. Spirakis and myself officially formulated in (C32) (which later appeared as (J24)) the Fully Mixed Nash Equilibrium Conjecture, stating that the fully mixed Nash equilibrium is the worst-case one - it maximizes Social Cost. We showed something very close to the conjecture by proving that any arbitrary Nash equilibrium is within 2h(1+ε) of the fully mixed one (with respect to Social Cost), where h is the factor by which the largest user traffic deviates from the average user traffic, and ε > 0 is any arbitrarily small constant. Crucial to our proof has been the use for the first time of techniques from Majorization Theory and Stochastic Orders for the analysis of Nash equilibria. Still there, we introduced Nashification - the process of transforming an arbitrary (pure) strategy profile into a (pure) Nash equilibrium with no increased Social Cost. We presented the first Nashification algorithm to approximate the Social Cost of the best pure Nash equilibrium to any arbitrary accuracy. In sharp contrast, we proved that approximating well the Social Cost of the worst pure Nash equilibrium is an NP-complete problem. These complexity results established the first contrast between best and worst Nash equilibria with respect to their approximability properties. Moreover, Nashification has survived as a natural paradigm for the approximation of optimal Nash equilibria.


Our next trip focused on the Fully Mixed Nash Equilibrium Conjecture. Together with T. Lucking, B. Monien, M. Rode, P. Spirakis and I. Vrto, we used combinatorial arguments to establish in (C30) the conjecture for two important special cases (two users or two links). These results still provide state-of-the-art cases for which the Fully Mixed Nash Equilibrium Conjecture holds. (Additional state-of-the-art cases were soon thereafter identified in the M.Sc. Theses of I. Ippolytou and P. Pavlou, which I supervised at University of Cyprus.)


Around 2003, the time was already just right for considering other theoretical models for selfish routing as alternatives to the KP model, and investigate how Price of Anarchy and the complexity of computing Nash equilibria depend on model assumptions. Together with my already closest colleague B. Monien and members of his group M. Gairing, T. Lucking and M. Rode (whose Ph.D. Thesis at University of Paderborn I co-supervised with Burkhard), we introduced and investigated a series of such models in four papers ((C33) (which now appears as (IJS1)), (C35) (a part of which now appears as (J26)), (C36) and (C38) (whose full version appears as (JS7))). These models were formed through varying the definition of either the Social Cost or the Individual Cost, or through restricting each user to a particular set of (allowed) links. We obtained a comprehensive collection of bounds on Price of Anarchy for them; some bounds identified subtle differences between ways of defining Social Cost - for example, they imply that the Price of Anarchy with Social Cost defined as maximum latency (as in the KP model) is radically different than when Social Cost is defined as average latency. Interestingly, some of the bounds demonstrated the first (probably unforeseen) connections of the Price of Anarchy to classical sequences of combinatorial numbers, such as the Stirling numbers of the second kind and the Bell numbers. Some other bounds demonstrated the crucial role of the convexity assumption on the latency functions. Furthermore, we presented in (C35) an algorithm to compute a pure Nash equilibrium for the model of restricted parallel links. This algorithm extends first the classical Preflow-Push algorithm of A. V. Goldberg and R. E. Tarjan to the setting of unsplittable flow; it then employs a novel Nashification algorithm that still relies on techniques from network flows. I believe that the connection to network flows may be the key to identifying new tractable classes for the problem of computing Nash equilibria.


Together with R. Elsasser, M. Gairing, T. Lucking and B. Monien in (C44), we further investigated the model of restricted parallel links in its simplest case where each user is restricted to only two machines. This assumption allows a graph-theoretic representation, called the interaction graph, where users are edges and machines are vertices. Interaction graphs allowed us to pose a new genre of mathematical questions concerning the best-case or worst-case interaction graph (with respect to Social Cost) for a given number of users and machines. We answered some of these questions for the special case of 3-regular interaction graphs; our analysis yielded some deep properties of orientations in 3-regular graphs, which are of independent interest. (Additional results for interaction graphs appear in the recent M.Sc. Thesis of S. Lazarides, which I supervised at University of Cyprus.)


In a latest trip supported by the newly funded IST projects DELIS and AEOLUS, I embarked with P. Panagopoulou and P. Spirakis(in (C43) and (C46)) to studying the impact of pricing mechanisms for charging users assigned to machines on the quality of the achievable Nash equilibria. Congestion games had assumed that the number of users (assigned to a machine) is the determining parameter; weighted congestion games extended these to users with weights. Our idea has been to combine the two and let each machine charge to each user the total weight over the total number of users assigned to it. This scheme induces a new set of Nash equilibria, which we studied. Within this new model, we also defined and evaluated the Diffuse Price of Anarchy, which extends the Price of Anarchy to take into account the probability distribution on inputs. We believe that the Diffuse Price of Anarchy offers a more realistic assessment of the effect of the selfish behavior of users than the (worst-case) Price of Anarchy. We expect that the Diffuse Price of Anarchy will attract much further interest and study in the years to come.


In my most recent trip to Algorithmic Game Theory and together with my post-doc V. Papadopoulou and M. Gelastou, A. Philippou and P. Spirakis, we introduced and analyzed (in (C42), (C45), (CS1) and (CS2)) a novel, simultaneously game-theoretic and graph-theoretic model for network security; it applies to adversarial networks with harmful procedures called attackers, and protector (or defender) entities cleaning up the network against the harm. We have characterized the Nash equilibria of the associated game. The obtained graph-theoretic characterizations point out connections to milestones in Matching Theory, such as Hall's Theorem. From the characterizations, we derived efficient algorithms to compute (classes of) Nash equilibria for some special cases of the network graph.


In conclusion, my published work on Algorithmic Game Theory has spawned several innovative ideas and brand-new concepts and techniques, such as the fully mixed Nash equilibrium, the Fully Mixed Nash Equilibrium Conjecture, ball fusion, Nashification, the connection of Nash equilibria to network flows, (worst-case and best-case) interaction graphs, the Diffuse Price of Anarchy, to name a few. All these concepts and methodologies have found a position in contemporary Algorithmic Game Theory.


For future work, I will keep studying the Price of Anarchy and the problem of computing equilibria (including Nash, correlated  and market) for other significant classes of games that model modern computer systems.


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 2.  Distributed Computing


My interest in Distributed Computing is traced back to my graduate years at Harvard (but see also (J12) for a survey I wrote later), where my (Ph.D. Thesis) focused on Timing-Based Distributed Computation. This subfield concerns the study of how timing assumptions in a distributed system affect the complexity (and even the possibility) of solving interesting problems. I presented in (C1) (which later appeared as (J2)) together with H. Attiya, and in (J1) the first complexity-theoretic separations between semi-synchronous and asynchronous models of distributed computation; we used the classical session problem as a vehicle for obtaining the separations. I also presented, together with D. Roth in (C2) and (C3) (which were later combined into (J9)) time bounds on implementing Distributed Shared Memory over partially synchronous message-passing; in particular, we presented the first timing-based distributed algorithm for implementing linearizable read-write objects over message-passing. I later generalized in (C20), together with my Undergraduate Diploma student M. Eleftheriou, some of those bounds to models with drifting clocks and different delay assumptions. Also, I obtained in (C4) (almost matching) lower and upper bounds on how closely clocks can be synchronized in a semi-synchronous network.


Since 1993, I have have been studying counting networks (invented by J. Aspnes, M. Herlihy and N. Shavit back in 1991) - some distributed, low-latency and low-contention data structures for implementing shared counters and other primitives in a distributed system. (See (J5) for an early survey I wrote, and (RM1) for a research monograph which C. Busch and myself are currently preparing.) My work on this topic can be partitioned as followed:

  • Combinatorial properties: This reflects the interest that originally attracted me to work on counting networks; it arose back in 1993 out of my supervision of a term project by Costas Busch, then a Master's student at University of Crete, for a graduate class on distributed computing I taught there. Starting up a fruitful, yearly collaboration with Costas, we published in (C6) (which later appeared as (J4)) an extensive mathematical study of the combinatorial properties of counting networks and their closest relatives, smoothing networks and (the much older) sorting networks. Most notably, our study yielded an analog for counting networks of the classical0-1 principle for sorting networks. We further extended this study in (J6) to encompass threshold and weak threshold networks, and in (J3) to produce methodologies for the verification of counting networks.

  • Limitations: Which other concurrent operations can counting networks support? Together with W. Aiello, C. Busch, M. Herlihy, N. Shavit and D. Touitou, we discovered in (BA6) and (C16) (which later appeared as (J11)) that counting networks can support, through the antitoken primitive invented earlier by N. Shavit and D. Touitou, the decrement-by-one operation (concurrently with the increment-by-one). Further on, with C. Busch, N. Demetriou and M. Herlihy, we soon extended this result in (C18) (which later appeared as (J14)) to counting networks supporting threshold counting; eventually, we provided in (C21) (which later appeared as (BC2) and (J10)) a combinatorial characterization of the capabilities and limitations of the antitoken.


    In (C28) (appearing later as (J19)), C. Busch, P. Spirakis and myself solved a long-standing open problem about the possibility of extending counting networks, while keeping them finite-sized, high-concurrency and low-contention, to support operations more complex than increment-by-one but yet as simple as adding. We introduced a novel group-theoretic framework (in detail, a new class of algebraic groups called monotone groups) to model monotone Read&Modify&Write operations, within which we proved the long-seeked impossibility result: there is no such finite-sized network, even if the tokens realizing the operations can carry and exchange information with the network, and even if the network is made up of arbitrary switches. Key to our proof is a Monotone Linearizability Lemma, providing the first instance where linearizability is an inherent (as opposed to required) property that builds up an efficiency bottleneck. (For further extensions and generalizations, see (C41) and (J18).)

  • Consistency properties: It had been known that there is no (non-trivial) linearizable, asynchronous counting network. Together with M. Papatriantafilou and Ph. Tsigas, we investigated in (C8)  timing conditions for linearizablecounting networks. Together with M. Merritt and G. Taubenfeld, we later provided in (C17) the first comparison of sequential consistency and linearizability for timing-based counting networks.

Motivated by my constant interest in the impact of timing assumptions in a distributed system on time and space complexities, I looked at the connection management problem from the timing point of view. How do timing assumptions affect the delivery and quiescence times for a long-lived connection between a sender and a receiver? (For the general question, see (J8) for an early survey I wrote.) Together with my Undergraduate Diploma student N. Papadakis, we presented in (C11) (which later appeared as (J15)) a comprehensive collection of trade-offs between delivery and quiescence times under various assumptions on the sender and receiver clocks.


In a distinct direction with a strong combinatorial flavor, I studied together with P. Spirakis and my Undergraduate Diploma student S. Georgiades in (BA9) and (C19), a distributed decision-making model introduced by C. H. Papadimitriou and M. Yannakakis in PODC 1992. We focused a system that suffers long periods with no communication among the agents. We provided a geometric analysis of high-dimensional polytopes to derive a new formula for the volume of the intersection between a simplex and an orthogonal parallelepiped (in any dimension). In turn, we optimized the formula to derive optimal decision-making protocols for a certain load balancing problem. For 3 agents, our results settle the 0.545 Conjecture of C. H. Papadimitriou and M. Yannakakis.


The lattice agreement decision problem is a weakening of traditional consensus. In (JS1), I presented the first early-stopping algorithm for it in the synchronous message-passing model where processors fail by crashing.


For future work, I am planning to continue studying foundational problems of distributed computing. I am also interested in distributed algorithms for modern settings like ad-hoc wireless and mobile networks.


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 3.  Networking Theory


I worked on four major topics: Flow Control, Packet-Switching/Routing, Queueing and Sensor  Networks.

  • Flow Control: I have worked on this topic together with P. Spirakis and P. Fatourou (then a Ph.D. student at University of Patras, whom I co-supervised with Paul). Specifically, we looked at bottleneck flow control, which converges to max-min fairness; there, we investigated rate-based flow control, where max-min fairness is reached through a sequence of (local) adjustments to the rates of sessions. We studied in (C10) (which later appeared as (J22)) trade-offs between the speed at which the system converges to max-min fairness and the locality of the schedulers (choosing the sequence of adjustments). Our study has yielded the first (tight)lower bound on the convergence complexity of oblivious schedulers (ones who "see" neither the network nor the rates). We went on with P. Spirakis and P. Fatourou to extend in (C15) (which later appeared as (J21)) the classical theory of max-min fair flow control to settings of heterogeneous networks where sessions get different priorities to bandwidth. We also investigated in (BA8) and (C13) the effect of local computations on the total efficiency of distributed, rate-based flow control algorithms. An invited survey of our work appears in (C9).

  • Queueing: I have worked on this topic together with P. Spirakis, S. Nikoletseas and D. Koukopoulos(then a Ph.D. student at University of Patras, whom I co-supervised with Paul); see (J13) and (BC1) for an early survey I wrote. Specifically, we looked at the popular framework of Adversarial Queueing Theory, which replaces the stochastic assumptions made by traditional Queueing Theory on the arrival patterns of inputs to a communication network with worst-case (adversarial) ones. The natural question then concerns stability: how and when will all queues at the systemís servers remain bounded? We have addressed this fundamental question in a variety of settings. For greedy queueing protocols (ones that always forward a packet if they can), we discovered in (C27) (which appeared later as (J23)) that the network structure plays a crucial role for stability. For heterogeneous networks (ones that combine different queueing protocols for queues at different servers), we found out in (C26) that stability is very sensitive to the precise combination of the different protocols. We also discovered in (C29) (but see also (JS2) for a complete exposition) that stability may be seriously harmed when different servers use different rates (also called capacities) for forwarding packets out of their (out-going) buffers; in fact, an adversary against stability that uses just two different levels of rates is as powerful as a general adversary. Furthermore, we proved in(TR1) that the popular FIFO queueing protocol can be made unstable by such an adversary even if the rate of injecting messages is arbitrarily low, and even if the network is restricted to remain planar. On the positive side, we provided in (C34) (but see also (JS3) for a complete exposition) tight bounds on delivery times for certain specific combinations of networks and queueing protocols that are known to guarantee stability.

  • Packet-Switching/Routing: A long-standing open problem in packet-switching concerns the existence of a universal packet-switching algorithm with near-optimal performance guarantees for the class of bufferless networks, where the buffer size for packets in transit is zero. This problem has remained open since the seminal work of T. Leighton, B. Maggs and S. Rao in 1988, which showed the existence of a (near-optimal) universal, store-and-forward packet-switching algorithm. Together with C. Busch, and M. Magdon-Ismail, we gave in (C40) a positive answer to this question. (See (JS6) for a full exposition.) At the heart of our proof is a new technique for hand-crafting a (universal) bufferless algorithm via emulating a (universal) store-and-forward algorithm. The emulation builds on our novel idea of replacing packet buffering by packet circulation; we then prove that circulation converges fast. We expect that this idea will find further applications, in the years to come.


    To evaluate the universality price paid by our universal, bufferless algorithm, we looked together with C. Busch, M. Magdon-Ismail and R. Wattenhofer in (C37) (but see also (JS5) for a complete exposition) at bufferless algorithms for specific topologies, such as trees and leveled networks. We presented both centralized and distributed hot-potato algorithms for them, which improved upon all previously known corresponding algorithms.


    A more stringent (than hot-potato) class of bufferless packet-switching algorithms is that of direct routing, where each packet must, once injected, be delivered to its destination without collisions with other packets. Together with C. Busch, M. Magdon-Ismail and P. Spirakis, we presented in (C39) (which later appears as (J20)) a comprehensive treatment of direct routing. Our analysis includes algorithms to compute efficient direct schedules for the packets; hardness results (more precisely, NP-completeness and inapproximability results) for computing the optimal direct schedules; analysis of the buffering requirements for such optimal schedules.


  • Sensor Networks:Smart dust is made of a vast number of ultra-small, fully autonomous computing devices of restricted energy and computing capabilities, cooperating towards a large sensing task. Together with I. Chatzigiannakis, T. Dimitriou, S. Nikoletseas and P. Spirakis, we implemented and experimentally evaluated in (C31) (which later appeared as (J17)) a number of protocols for local detection and propagation in smart dust networks.

For future work, I will keep studying efficiency and stability issues for fundamental problems like routing, packet switching, flow control and queueing in modern networks like ad-hoc wireless and mobile networks.


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