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193
BALANCED ALLOCATIONS: THE HEAVILY LOADED CASE
, 2006
"... We investigate ballsintobins processes allocating m balls into n bins based on the multiplechoice paradigm. In the classical singlechoice variant each ball is placed into a bin selected uniformly at random. In a multiplechoice process each ball can be placed into one out of d ≥ 2 randomly selec ..."
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Cited by 58 (7 self)
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We investigate ballsintobins processes allocating m balls into n bins based on the multiplechoice paradigm. In the classical singlechoice variant each ball is placed into a bin selected uniformly at random. In a multiplechoice process each ball can be placed into one out of d ≥ 2 randomly selected bins. It is known that in many scenarios having more than one choice for each ball can improve the load balance significantly. Formal analyses of this phenomenon prior to this work considered mostly the lightly loaded case, that is, when m ≈ n. In this paper we present the first tight analysis in the heavily loaded case, that is, when m ≫ n rather than m ≈ n. The best previously known results for the multiplechoice processes in the heavily loaded case were obtained using majorization by the singlechoice process. This yields an upper bound of the maximum load of bins of m/n + O ( √ m ln n/n) with high probability. We show, however, that the multiplechoice processes are fundamentally different from the singlechoice variant in that they have “short memory. ” The great consequence of this property is that the deviation of the multiplechoice processes from the optimal allocation (that is, the allocation in which each bin has either ⌊m/n ⌋ or ⌈m/n ⌉ balls) does not increase with the number of balls as in the case of the singlechoice process. In particular, we investigate the allocation obtained by two different multiplechoice allocation schemes,
A stochastic process on the hypercube with applications to peertopeer networks
 Proc. STOC 2003
"... Consider the following stochastic process executed on a graph G = (V, E) whose nodes are initially uncovered. In each step, pick a node at random and if it is uncovered, cover it. Otherwise, if it has an uncovered neighbor, cover a random uncovered neighbor. Else, do nothing. This can be viewed as a ..."
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Cited by 57 (2 self)
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Consider the following stochastic process executed on a graph G = (V, E) whose nodes are initially uncovered. In each step, pick a node at random and if it is uncovered, cover it. Otherwise, if it has an uncovered neighbor, cover a random uncovered neighbor. Else, do nothing. This can be viewed as a structured coupon collector process. We show that for a large family of graphs, O(n) steps suffice to cover all nodes of the graph with high probability, where n is the number of vertices. Among these graphs are dregular graphs with d = Ω(log n log log n), random dregular graphs with d = Ω(log n) and the kdimensional hypercube where n = 2 k. This process arises naturally in answering a question on load balancing in peertopeer networks. We consider a distributed hash table in which keys are partitioned across a set of processors, and we assume that the number of processors
Fast hash table lookup using extended Bloom filter: an aid to network processing
 In ACM SIGCOMM
, 2005
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Parallel Randomized Load Balancing
 In Symposium on Theory of Computing. ACM
, 1995
"... It is well known that after placing n balls independently and uniformly at random into n bins, the fullest bin holds \Theta(log n= log log n) balls with high probability. Recently, Azar et al. analyzed the following: randomly choose d bins for each ball, and then sequentially place each ball in the ..."
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Cited by 56 (8 self)
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It is well known that after placing n balls independently and uniformly at random into n bins, the fullest bin holds \Theta(log n= log log n) balls with high probability. Recently, Azar et al. analyzed the following: randomly choose d bins for each ball, and then sequentially place each ball in the least full of its chosen bins [2]. They show that the fullest bin contains only log log n= log d + \Theta(1) balls with high probability. We explore extensions of this result to parallel and distributed settings. Our results focus on the tradeoff between the amount of communication and the final load. Given r rounds of communication, we provide lower bounds on the maximum load of \Omega\Gamma r p log n= log log n) for a wide class of strategies. Our results extend to the case where the number of rounds is allowed to grow with n. We then demonstrate parallelizations of the sequential strategy presented in Azar et al. that achieve loads within a constant factor of the lower bound for two ...
Approximate Equilibria and Ball Fusion
 Theory of Computing Systems
, 2002
"... We consider sel sh routing over a network consisting of m parallel links through which n sel sh users route their tra c trying to minimize their own expected latency. Westudy the class of mixed strategies in which the expected latency through each link is at most a constant multiple of the optimum m ..."
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Cited by 56 (23 self)
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We consider sel sh routing over a network consisting of m parallel links through which n sel sh users route their tra c trying to minimize their own expected latency. Westudy the class of mixed strategies in which the expected latency through each link is at most a constant multiple of the optimum maximum latency had global regulation been available. For the case of uniform links it is known that all Nash equilibria belong to this class of strategies. We areinterested in bounding the coordination ratio (or price of anarchy) of these strategies de ned as the worstcase ratio of the maximum (over all links) expected latency over the optimum maximum latency. The load balancing aspect of the problem immediately implies a lower bound; lnm ln lnm of the coordination ratio. We give a tight (uptoamultiplicative constant) upper bound. To show the upper bound, we analyze a variant ofthe classical balls and bins problem, in which balls with arbitrary weights are placed into bins according to arbitrary probability distributions. At the heart of our approach is a new probabilistic tool that we call
On the Analysis of Randomized Load Balancing Schemes
 IN PROCEEDINGS OF THE 9TH ANNUAL ACM SYMPOSIUM ON PARALLEL ALGORITHMS AND ARCHITECTURES
, 1998
"... It is well known that simple randomized load balancing schemes can balance load effectively while incurring only a small overhead, making such schemes appealing for practical systems. In this paper, we provide new analyses for several such dynamic randomized load balancing schemes. Our work extends ..."
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Cited by 55 (7 self)
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It is well known that simple randomized load balancing schemes can balance load effectively while incurring only a small overhead, making such schemes appealing for practical systems. In this paper, we provide new analyses for several such dynamic randomized load balancing schemes. Our work extends a previous analysis of the supermarket model, a model that abstracts a simple, efficient load balancing scheme in the setting where jobs arrive at a large system of parallel processors. In this model, customers arrive at a system of n servers as a Poisson stream of rate #n, # < 1, with service requirements exponentially distributed with mean 1. Each customer chooses d servers independently and uniformly at random from the n servers, and is served according to the First In First Out (FIFO) protocol at the choice with the fewest customers. For the supermarket model, it has been shown that using d = 2 choices yields an exponential improvement in the expected time a customer spends in the syst...
Fast Concurrent Access to Parallel Disks
 In 11th ACMSIAM Symposium on Discrete Algorithms
, 1999
"... High performance applications involving large data sets require the efficient and flexible use of multiple disks. In an external memory machine with D parallel, independent disks, only one block can be accessed on each disk in one I/O step. This restriction leads to a load balancing problem that is ..."
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Cited by 52 (11 self)
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High performance applications involving large data sets require the efficient and flexible use of multiple disks. In an external memory machine with D parallel, independent disks, only one block can be accessed on each disk in one I/O step. This restriction leads to a load balancing problem that is perhaps the main inhibitor for adapting singledisk external memory algorithms to multiple disks. This paper shows that this problem can be solved efficiently using a combination of randomized placement, redundancy and an optimal scheduling algorithm. A buffer of O(D) blocks suffices to support efficient writing of arbitrary blocks if blocks are distributed uniformly at random to the disks (e.g., by hashing). If two randomly allocated copies of each block exist, N arbitrary blocks can be read within dN=De + 1 I/O steps with high probability. In addition, the redundancy can be reduced from 2 to 1 + 1=r for any integer r. These results can be used to emulate the simple and powerful "singledisk multihead" model of external computing [1] on the physically more realistic independent disk model [33] with small constant overhead. This is faster than a lower bound for deterministic emulation [3].
A generic scheme for building overlay networks in adversarial scenarios
 In Proc. Intl. Parallel and Distributed Processing Symp
, 2003
"... This paper presents a generic scheme for a central, yet untackled issue in overlay dynamic networks: maintaining stability over long life and against malicious adversaries. The generic scheme maintains desirable properties of the underlying structure including low diameter, and efficient routing mec ..."
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Cited by 51 (6 self)
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This paper presents a generic scheme for a central, yet untackled issue in overlay dynamic networks: maintaining stability over long life and against malicious adversaries. The generic scheme maintains desirable properties of the underlying structure including low diameter, and efficient routing mechanism, as well as balanced node dispersal. These desired properties are maintained in a decentralized manner without resorting to global updates or periodic stabilization protocols even against an adaptive adversary that controls the arrival and departure of nodes. 1
The ContentAddressable Network D2B
, 2003
"... A contentaddressable network (CAN) is a distributed lookup table that can be used to implement peertopeer (P2P) systems. A CAN allows the discovery and location of data and/or resources, identi ed by keys, in a distributed network (e.g., Internet), in absence of centralized server or any hier ..."
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Cited by 49 (2 self)
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A contentaddressable network (CAN) is a distributed lookup table that can be used to implement peertopeer (P2P) systems. A CAN allows the discovery and location of data and/or resources, identi ed by keys, in a distributed network (e.g., Internet), in absence of centralized server or any hierarchical organization. Several networks have been recently described in the literature, and some of them have led to the development of experimental systems. We present a new CAN, called d2b. Its main characteristics are: simplicity, provability, and scalability. d2b allows the number of nodes n to vary between 1 and jKj where K is the set of keys managed by the network. In term of performances, any join or leave of a user implies a constant expected number of link modi cations, and, with high probability (w.h.p.), at most O(log n) link modi cations.
On Traveling Salesperson Problems for Dubins' vehicle: stochastic and dynamic environments
 CDC 2005, TO APPEAR
, 2005
"... In this paper we propose some novel planning and routing strategies for Dubins’ vehicle, i.e., for a nonholonomic vehicle moving along paths with bounded curvature, without reversing direction. First, we study a stochastic version of the Traveling Salesperson Problem (TSP): given n targets randomly ..."
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Cited by 42 (13 self)
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In this paper we propose some novel planning and routing strategies for Dubins’ vehicle, i.e., for a nonholonomic vehicle moving along paths with bounded curvature, without reversing direction. First, we study a stochastic version of the Traveling Salesperson Problem (TSP): given n targets randomly sampled from a uniform distribution in a rectangle, what is the shortest Dubins ’ tour through the targets and what is its length? We show that the expected length of such a tour is Ω(n 2/3) and we propose a novel algorithm that generates a tour of length O(n 2/3 log(n) 1/3) with high probability. Second, we study a dynamic version of the TSP (known as “Dynamic Traveling Repairperson Problem” in the Operations Research literature): given a stochastic process that generates targets, is there a policy that allows a Dubins vehicle to stabilize the system, in the sense that the number of unvisited targets does not diverge over time? If such policies exist, what is the minimum expected waiting period between the time a target is generated and the time it is visited? We propose a novel recedinghorizon algorithm whose performance is almost within a constant factor from the optimum.