Platforms design the form of presentation by which sellers are shown to the buyers. This design not only shapes the buyers' experience but also leads to different market equilibria or dynamics. One component in this design is through the platform's mediation of the search frictions experienced by the buyers for different sellers. We take a model of monopolistic competition and show that, on one hand, when all sellers have the same inspection costs, the market sees no stable price since the sellers always have incentives to undercut each other, and, on the other hand, the platform may stabilize the price by giving prominence to one seller chosen by a carefully designed mechanism. This calls to mind Amazon's Buy Box. We study natural mechanisms for choosing the prominent seller, characterize the range of equilibrium prices implementable by them, and find that in certain scenarios the buyers' surplus improves as the search friction increases.

Maintaining a maximum bipartite matching online while minimizing augmentations is a well studied problem, motivated by content delivery, job scheduling, and hashing. A breakthrough result of Bernstein, Holm, and Rotenberg (SODA 2018) resolved this problem up to a logarithmic factors. However, to model other problems in scheduling and resource allocation, we may need a richer class of combinatorial constraints (e.g., matroid constraints).

A matroid $\mathcal{M}$ on a set $E$ of elements has the $α$-partition property, for some $α>0$, if it is possible to (randomly) construct a partition matroid $\mathcal{P}$ on (a subset of) elements of $\mathcal{M}$ such that every independent set of $\mathcal{P}$ is independent in $\mathcal{M}$ and for any weight function $w:E\to\mathbb{R}_{\geq 0}$, the expected value of the optimum of the matroid secretary problem on $\mathcal{P}$ is at least an $α$-fraction of the optimum on $\mathcal{M}$. We show that the complete binary matroid, ${\cal B}_d$ on $\mathbb{F}_2^d$ does not satisfy the $α$-partition property for any constant $α>0$ (independent of $d$). Furthermore, we refute a recent conjecture of Bérczi, Schwarcz, and Yamaguchi by showing the same matroid is $2^d/d$-colorable but cannot be reduced to an $α2^d/d$-colorable partition matroid for any $α$ that is sublinear in $d$.

In this extended abstract, we show that for some $\epsilon>10^{-36}$ and any metric TSP instance, the max entropy algorithm studied by [1] returns a solution of expected cost at most $\frac{3}{2}-\epsilon$ times the cost of the optimal solution to the subtour elimination LP. This implies that the integrality gap of the subtour LP is at most $\frac{3}{2}-\epsilon$. This analysis also shows that there is a randomized $\frac{3}{2}-\epsilon$ approximation for the 2-edge-connected multi-subgraph problem, improving upon Christofides’ algorithm.

We give a randomized 1+5.06/√k-approximation algorithm for the minimum k-edge connected spanning multi-subgraph problem, k-ECSM.

For some > 10−36 we give a randomized 3/2− approximation algorithm for metric TSP.

We design a 1.49993-approximation algorithm for the metric traveling salesperson problem (TSP) for instances in which an optimal solution to the subtour linear programming relaxation is half-integral. These instances received significant attention over the last decade due to a conjecture of Schalekamp, Williamson and van Zuylen stating that half-integral LP solutions have the largest integrality gap over all fractional solutions. So, if the conjecture of Schalekamp et al. holds true, our result shows that the integrality gap of the subtour polytope is bounded away from 3/2.

Stable matching is a classical combinatorial problem that has been the subject of intense theoretical and empirical study since its introduction in 1962 in a seminal paper by Gale and Shapley. In this paper, we provide a new upper bound on f(n), the maximum number of stable matchings that a stable matching instance with n men and n women can have. It has been a long-standing open problem to understand the asymptotic behavior of f(n) as n→∞, first posed by Donald Knuth in the 1970s. Until now the best lower bound was approximately 2.28n, and the best upper bound was 2nlogn− O(n). In this paper, we show that for all n, f(n) ≤ cn for some universal constant c. This matches the lower bound up to the base of the exponent. Our proof is based on a reduction to counting the number of downsets of a family of posets that we call “mixing”. The latter might be of independent interest.

We consider time-of-use pricing as a technique for matching supply and demand of temporal resources with the goal of maximizing social welfare. Relevant examples include energy, computing resources on a cloud computing platform, and charging stations for electric vehicles, among many others. A client/job in this setting has a window of time during which he needs service, and a particular value for obtaining it. We assume a stochastic model for demand, where each job materializes with some probability via an independent Bernoulli trial. Given a per-time-unit pricing of resources, any realized job will first try to get served by the cheapest available resource in its window and, failing that, will try to find service at the next cheapest available resource, and so on. Thus, the natural stochastic fluctuations in demand have the potential to lead to cascading overload events. Our main result shows that setting prices so as to optimally handle the expected demand works well: with high probability, when the actual demand is instantiated, the system is stable and the expected value of the jobs served is very close to that of the optimal offline algorithm.

We consider the online carpool fairness problem of [Fagin and Williams, 1983] in which an online algorithm is presented with a sequence of pairs drawn from a group of n potential drivers. The online algorithm must select one driver from each pair, with the objective of partitioning the driving burden as fairly as possible for all drivers. The unfairness of an online algorithm is a measure of the worst-case deviation between the number of times a person has driven and the number of times they would have driven if life was completely fair. We introduce a version of the problem in which drivers only carpool with their neighbors in a given social network graph; this is a generalization of the original problem, which corresponds to the social network of the complete graph. We show that for graphs of degree d, the unfairness of deterministic algorithms against adversarial sequences is exactly d/2. For random sequences of edges from planar graph social networks we give a [deterministic] algorithm with logarithmic unfairness (holds more generally for any bounded-genus graph). This does not follow from previous random sequence results in the original model, as we show that restricting the random sequences to sparse social network graphs may increase the unfairness. A very natural class of randomized online algorithms are so-called static algorithms that preserve the same state distribution over time. Surprisingly, we show that any such algorithm has unfairness ~Theta(sqrt(d)) against oblivious adversaries. This shows that the local random greedy algorithm of [Ajtai et al, 1996] is close to optimal amongst the class of static algorithms. A natural (non-static) algorithm is global random greedy (which acts greedily and breaks ties at random). We improve the lower bound on the competitive ratio from Omega(log^{1/3}(d)) to Omega(log(d)). We also show that the competitive ratio of global random greedy against adaptive adversaries is Omega(d).

Consider the pricing problem faced by FedEx. Each customer has a package to ship, a deadline $d$ by which he needs his package to arrive, and a value $v$ for a guarantee that the package will arrive by his deadline. FedEx can (and does) offer a number of different shipping options in order to extract more revenue from their customers. In this paper, we solve the optimal (revenue-maximizing) auction problem for the single-agent version of this problem. Our paper adds to the relatively short list of multi-parameter settings for which a closed-form solution is known.

We consider a pricing problem where a buyer is interested in purchasing/using a good, such as an app or music or software, repeatedly over time. The consumer discovers his value for the good only as he uses it, and the value evolves with each use. Optimizing for the seller's revenue in such dynamic settings is a complex problem and requires assumptions about how the buyer behaves before learning his future value(s), and in particular, how he reacts to risk. We explore the performance of a class of pricing mechanisms that are extremely simple for both the buyer and the seller to use: the buyer reacts to prices myopically without worrying about how his value evolves in the future; the seller needs to optimize for revenue over a space of only two parameters, and can do so without knowing the buyer's risk profile or fine details of the value evolution process. We present simple-versus-optimal type results, namely that under certain assumptions, simple pricing mechanisms of the above form are approximately optimal regardless of the buyer's risk profile. Our results assume that the buyer's value per usage evolves as a martingale. For our main result, we consider pricing mechanisms in which the seller offers the product for free for a certain number of uses, and then charges an appropriate fixed price per usage. We assume that the buyer responds by buying the product for as long as his value exceeds the fixed price. Importantly, the buyer does not need to know anything about how his future value will evolve, only how much he wants to use the product right now. Regardless of the buyers' initial value, our pricing captures as revenue a constant fraction of the total value that the buyers accumulate in expectation over time.

Consider a scenario in which there are multiple employers competing to hire the best possible employee. How does the competition between the employers affect their hiring strategies or their ability to hire one of the best possible candidates? In this paper, we address this question by studying a generalization of the classical secretary problem from optimal stopping theory: a set of ranked employers compete to hire from the same random stream of employees, and each employer wishes to hire the best candidate in the bunch. We show how to derive subgame-perfect Nash equilibrium strategies in this game and analyze the impact the competition has on the quality of the hires as a function of the rank of the employer. We present numerical results from simulations of these strategies.

We study revenue maximization in settings where agents' values are interdependent: each agent receives a signal drawn from a correlated distribution and agents' values are functions of all of the signals. We introduce a variant of the generalized VCG auction with reserve prices and random admission, and show that this auction gives a constant approximation to the optimal expected revenue in matroid environments. Our results do not require any assumptions on the signal distributions, however, they require the value functions to satisfy a standard single-crossing property and a concavity-type condition.

Surveys conducted by human interviewers are one of the principal means of gathering data from all over the world, but the quality of this data can be threatened by interviewer fabrication. In this paper, we investigate a new approach to detecting interviewer fabrication automatically. We instrument electronic data collection software to record logs of low-level behavioral data and show that supervised classification, when applied to features extracted from these logs, can identify interviewer fabrication with an accuracy of up to 96%. We show that even when interviewers know that our approach is being used, have some knowledge of how it works, and are incentivized to avoid detection, it can still achieve an accuracy of 86%. We also demonstrate the robustness of our approach to a moderate amount of label noise and provide practical recommendations, based on empirical evidence, on how much data is needed for our approach to be effective.

We introduce and study strongly truthful mechanisms and their applications. We use strongly truthful mechanisms as a tool for implementation in undominated strategies for several problems, including the design of externality resistant auctions and a variant of multi-dimensional scheduling.

In many real-world auctions, a bidder does not know her exact value for an item, but can perform a costly deliberation to reduce her uncertainty. Relatively little is known about such deliberative environments, which are fundamentally different from classical auction environments. In this paper, we propose a new approach that allows us to leverage classical revenue-maximization results in deliberative environments. In particular, we use Myerson (1981) to construct the first non-trivial (i.e., dependent on deliberation costs) upper bound on revenue in deliberative auctions. This bound allows us to apply existing results in the classical environment to a deliberative environment. In addition, we show that in many deliberative environments the only optimal dominant-strategy mechanisms take the form of sequential posted-price auctions.

We study the power of ascending auctions in a scenario in which a seller is selling a collection of identical items to anonymous unit'demand bidders. We show that even with full knowledge of the set of bidders' private valuations for the items, if the bidders are ex-ante identical, no ascending auction can extract more than a constant. times the revenue of the best fixed-price scheme. This problem is equivalent to the problem of coming up with an optimal strategy for blowing up indistinguishable balloons with known capacities in order to maximize the amount of contained, air. We show that the algorithm which simply inflates all balloons to a fixed volume is close to optimal in this setting.

How should players bid in keyword auctions such as those used by Google, Yahoo! and MSN?allWe consider greedy bidding strategies for a repeated auction on a single keyword, where in each round, each player chooses some optimal bid for the next round, assuming that the other players merely repeat their previous bid. We study the revenue, convergence and robustness properties of such strategies. Most interesting among these is a strategy we call the balanced bidding strategy (BB): it is known that BB has a unique fixed point with payments identical to those of the VCG mechanism. We show that if all players use the BB strategy and update each round, BB converges when the number of slots is at most 2, but does not always converge for 3 or more slots. On the other hand, we present a simple variant which is guaranteed to converge to the same fixed point for any number of slots. In a model in which only one randomly chosen player updates each round according to the BB strategy, we prove that convergence occurs with probability 1.We complement our theoretical results with empirical studies.

We study truthful mechanisms for auctions in which the auctioneer is trying to hire a team of agents to perform a complex task, and paying them for their work. As common in the field of mechanism design, we assume that the agents are selfish and will act in such a way as to maximize their profit, which in particular may include misrepresenting their true incurred cost. Our first contribution is a new and natural definition of the frugality ratio of a mechanism, measuring the amount by which a mechanism "overpays ", and extending previous definitions to all monopoly-free set systems. After reexamining several known results in light of this new definition, we proceed to study in detail shortest path auctions and 'r-out-of-k sets" auctions. We show that when individual set systems (e.g., graphs) are considered instead of worst cases over all instances, these problems exhibit a rich structure, and the performance of mechanisms may be vastly different. In particular, we show that the well-known VCG mechanism may be far from optimal in these settings, and we propose and analyze a mechanism that is always within a constant factor of optimal.

We describe mechanisms for auctions that are simultaneously truthful (alternately known as strategy-proof or incentive compatible) and guarantee high "net" profit. We make use of appropriate variants of competitive analysis of algorithms in designing and analyzing our mechanisms. Thus, we do not require any probabilistic assumptions on bids.We present two new concepts regarding auctions, that of a cancellable auction and that of a generalized auction. We use cancellable auctions in the design of generalized auctions, but they are of independent interest as well. Cancellable auctions have the property that if the revenue collected does not meet certain predetermined criteria, then the auction can be cancelled and the resulting auction is still truthful. The trivial approach (run a truthful auction and cancel if needed) yields an auction that is not necessarily truthfu.Generalized auctions can be used to model many problems previously considered in the literature, as well as numerous new problems. In particular, we give the first truthful profit-maximizing auctions for problems such as conditional financing and multicast.

We present a model for web search that captures in a unified manner three critical components of the problem: how the link structure of the web is generated, how the content of a web document is generated, and how a human searcher generates a query. The key to this unification lies in capturing the correlations between these components in terms of proximity in a shared latent semantic space. Given such a combined model, the correct answer to a search query is well defined, and thus it becomes possible to evaluate web search algorithms rigorously. We present a new web search algorithm, based on spectral techniques, and prove that it is guaranteed to produce an approximately correct answer in our model. The algorithm assumes no knowledge of the model, and is well-defined regardless of the model's accuracy.

Experimental evidence suggests that spectral techniques are valuable for a wide range of applications. A partial list of such applications include (i) semantic analysis of documents used to cluster documents into areas of interest, (ii) collaborative filtering --- the reconstruction of missing data items, and (iii) determining the relative importance of documents based on citation/link structure. Intuitive arguments can explain some of the phenomena that has been observed but little theoretical study has been done. In this paper we present a model for framing data mining tasks and a unified approach to solving the resulting data mining problems using spectral analysis. These results give strong justification to the use of spectral techniques for latent semantic indexing, collaborative filtering, and web site ranking.

We present the first optimal randomized online algorithms for the TCP acknowledgment problem [5] and the Bahncard problem [7]. These problems are well-known to be generalizations of the classical online ski rental problem, however, they appeared to be harder. In this paper, we demonstrate that a number of online algorithms which have optimal competitive ratios of e/(e-1), including these, are fundamentally no more complex than ski rental. Our results also suggest a clear paradigm for solving ski rental-like problems.

This paper describes a technique for tracing anonymous packet flooding attacks in the Internet back toward their source. This work is motivated by the increased frequency and sophistication of denial-of-service attacks and by the difficulty in tracing packets with incorrect, or "spoofed," source addresses. We describe a general purpose traceback mechanism based on probabilistic packet marking in the network. Our approach allows a victim to identify the network path(s) traversed by attack traffic without requiring interactive operational support from Internet service providers (ISPs). Moreover, this traceback can be performed "post mortem"-after an attack has completed. We present an implementation of this technology that is incrementally deployable, (mostly) backward compatible, and can be efficiently implemented using conventional technology.

This paper describes a technique for tracing anonymous packet flooding attacks in the Internet back towards their source. This work is motivated by the increased frequency and sophistication of denial-of-service attacks and by the difficulty in tracing packets with incorrect, or ``spoofed'', source addresses. In this paper we describe a general purpose traceback mechanism based on probabilistic packet marking in the network. Our approach allows a victim to identify the network path(s) traversed by attack traffic without requiring interactive operational support from Internet Service Providers (ISPs). Moreover, this traceback can be performed ``post-mortem'' -- after an attack has completed. We present an implementation of this technology that is incrementally deployable, (mostly) backwards compatible and can be efficiently implemented using conventional technology.

We introduce backoff processes, an idealized stochastic model of browsing on the world-wide web, which incorporates both hyperlink traversals and use of the “back button. ” With some probability the next state is generated by a distribution over out-edges from the current state, as in a traditional Markov chain. With the remaining probability, however, the next state is generated by clicking on the back button, and returning to the state from which the current state was entered by a “forward move”. Repeated clicks on the back button require access to increasingly distant history. We show that this process has fascinating similarities to and differences from Markov chains. In particular, we prove that like Markov chains, backoff processes always have a limit distribution, and we give algorithms to compute this distribution. Unlike Markov chains, the limit distribution may depend on the start state.

This paper considers the problem of paging under the assumption that the sequence of pages accessed is generated by a Markov chain. We use this model to study the fault-rate of paging algorithms. We first draw on the theory of Markov decision processes to characterize the paging algorithm that achieves optimal fault-rate on any Markov chain. Next, we address the problem of devising a paging strategy with low fault-rate for a given Markov chain. We show that a number of intuitive approaches fail. Our main result is a polynomial-time procedure that, on any Markov chain, will give a paging algorithm with fault-rate at most a constant times optimal. Our techniques show also that some algorithms that do poorly in practice fail in the Markov setting, despite known (good) performance guarantees when the requests are generated independently from a probability distribution.

Recently there has been a great deal of interest in the operating systems research community in prefetching and caching data from parallel disks, as a technique for enabling serial applications to improve input--output (I/O) performance. In this paper, algorithms are considered for integrated prefetching and caching in a model with a fixed-size cache and any number of backing storage devices (disks). The integration of caching and prefetching with a single disk was previously considered by Cao, Felten, Karlin, and Li. Here, it is shown that the natural extension of their aggressive algorithm to the parallel disk case is suboptimal by a factor near the number of disks in the worst case. The main result is a new algorithm, reverse aggressive, with near-optimal performance for integrated prefetching and caching in the presence of multiple disks.

We examine the application of offline algorithms for determining the optical sequence of loads and superloads (a load of multiple consecutive cache lines) for direct-mapped caches. We evaluate potential gains in terms of miss rate and bandwidth and find that in many cases optimal superloading can noticeably reduce the miss rate without appreciably increasing bandwidth. Then we examine how this performance potential might be realized. We examine the effectiveness of a dynamic online algorithm and of static analysis (profiling) for superloading and compare these to next-line prefetching. Experimental results show improvements comparable to those of the optimal algorithm in terms of miss rates.

No abstract available.

While algorithms for cooperative proxy caching have been widely studied, little is understood about cooperativecaching performance in the large-scale World Wide Web environment. This paper uses both trace-based analysis and analytic modelling to show the potential advantages and drawbacks of inter-proxy cooperation. With our traces, we evaluate quantitatively the performance-improvement potential of cooperation between 200 small-organization proxies within a university environment, and between two largeorganization proxies handling 23,000 and 60,000 clients, respectively. With our model, we extend beyond these populations to project cooperative caching behavior in regions with millions of clients. Overall, we demonstrate that cooperative caching has performance benefits only within limited population bounds. We also use our model to examine the implications of future trends in Web-access behavior and traffic. 1 Introduction Cooperative caching -- the sharing and coordination of cache...

Suppose that we sequentially place n balls into n boxes by putting each ball into a randomly chosen box. It is well known that when we are done, the fullest box has with high probability (1 + o(1))ln n/ln ln n balls in it. Suppose instead that for each ball we choose two boxes at random and place the ball into the one which is less full at the time of placement. We show that with high probability, the fullest box contains only ln ln n/ln 2 + O(1) balls---exponentially less than before. Furthermore, we show that a similar gap exists in the infinite process, where at each step one ball, chosen uniformly at random, is deleted, and one ball is added in the manner above. We discuss consequences of this and related theorems for dynamic resource allocation, hashing, and on-line load balancing.

This paper presents cooperative prefetching and caching --- the use of network-wide global resources (memories, CPUs, and disks) to support prefetching and caching in the presence of hints of future demands. Cooperative prefetching and caching effectively unites disk-latency reduction techniques from three lines of research: prefetching algorithms, cluster-wide memory management, and parallel I/O. When used together, these techniques greatly increase the power of prefetching relative to a conventional (non-global-memory) system. We have designed and implemented PGMS, a cooperative prefetching and caching system, under the Digital Unix operating system running on a 1.28 Gb/sec Myrinet-connected cluster of DEC Alpha workstations. Our measurements and analysis show that by using available global resources, cooperative prefetching can obtain significant speedups for I/O-bound programs. For example, for a graphics rendering application, our system achieves a speedup of 4.9 over a non-prefetching version of the same program, and a 3.1-fold improvement over that program using local-disk prefetching alone.

New high-speed networks greatly encourage the use of network memory as a cache for virtual memory and file pages, thereby reducing the need for disk access. Because pages are the fundamental transfer and access units in remote memory systems, page size is a key performance factor. Recently, page sizes of modern processors have been increasing in order to provide more TLB coverage and amortize disk access costs. Unfortunately, for high-speed networks, small transfers are needed to provide low latency. This trend in page size is thus at odds with the use of network memory on high-speed networks.This paper studies the use of subpages as a means of reducing transfer size and latency in a remote-memory environment. Using trace-driven simulation, we show how and why subpages reduce latency and improve performance of programs using network memory. Our results show that memory-intensive applications execute up to 1.8 times faster when executing with 1K-byte subpages, when compared to the same applications using full 8K-byte pages in the global memory system. Those same applications using 1K-byte subpages execute up to 4 times faster than they would using the disk for backing store. Using a prototype implementation on the DEC Alpha and AN2 network, we demonstrate how subpages can reduce remote-memory fault time; e.g., our prototype is able to satisfy a fault on a 1K subpage stored in remote memory in 0.5 milliseconds, one third the time of a full page.

The authors consider algorithms for integrated prefetching and caching in a model with a fixed-size cache and any number of backing storage devices (disks). Previously, the single disk case was considered by Cao et al. (1995). They show that the natural extension of their aggressive algorithm to the parallel disk case is suboptimal by a factor near the number of disks in the worst case. The main result is a new algorithm, reverse aggressive, with near-optimal performance in the presence of multiple disks.

We present and evaluate adaptive, hybrid cache coherence protocols for bus-based, shared-memory multiprocessors. Such protocols are motivated by the observation that sharing patterns vary substantially between different programs and even cache blocks within the same program. Performance measurements across a range of parallel applications indicate that the adaptive protocols we present perform well compared to both write-invalidate and write-update protocols.

No abstract available.

No abstract available.

What is the best paging algorithm if one has partial information about the possible sequences of page requests? We give a partial answer to this question by presenting the analysis of strongly competitive paging algorithms in the access graph model. This model restricts page requests so that they conform to a notion of locality of reference given by an arbitrary access graph We first consider optimal algorithms for undirected access graphs. Borodin et al. [Proc. 23rd ACM Symposium on Theory of Computing, 1991, pp. 249–259] define an algorithm, called FAR, and prove that it is within a logarithmic factor of the optimal online algorithm. We prove that FAR is in fact strongly competitive, i.e, within a constant factor of the optimum. For directed access graphs, we present an algorithm that is strongly competitive on structured program graphs—graphs that model a subset of the request sequences of structured programs.

As the performance gap between disks and micropocessors continues to increase, effective utilization of the file cache becomes increasingly immportant. Application-controlled file caching and prefetching can apply application-specific knowledge to improve file cache management. However, supporting application-controlled file caching and prefetching is nontrivial because caching and prefetching need to be integrated carefully, and the kernel needs to allocate cache blocks among processes appropriately. This article presents the design, implementation, and performance of a file system that integrates application-controlled caching, prefetching, and disk scheduling. We use a two-level cache management strategy. The kernel uses the LRU-SP (Least-Recently-Used with Swapping and Placeholders) policy to allocate blocks to processes, and each process integrates application-specific caching and prefetching based on thecontrolled-aggressivepolicy, an algorithm previously shown in a theoretical sense to be nearly optimal. Each process also improves its disk access latency by submittint its prefetches in batches so that the requests can be scheduled to optimize disk access performance. Our measurements show that this combination of techniques greatly improves the performance of the file system. We measured that the running time is reduced by 3% to 49% (average 26%) for single-process workloads and by 5% to 76% (average 32%) for multiprocess workloads.

Modern microprocessors contain small TLBs that maintain a cache of recently used translations. A TLB's coverage is the sum of the number of bytes mapped by each entry. Applications with working sets larger than the TLB coverage will perform poorly due to high TLB miss rates. Superpages have been proposed as a mechanism for increasing TLB coverage. A superpageis a virtual memory page with size and alignment that are a power of two multiple of the system's base page size. In this paper, we describe online policies for superpage management that monitor TLB miss traffic to decide when a superpage should be constructed. Our policies take into account both the benefit of a superpage promotion (potential for preventing future misses) and the cost (page copying). Although our approach increases the cost of each TLB miss, the net effect is to improve total execution time by eliminating a large number of misses without significantly increasing memory usage, thereby improving system performance.

Prefetching and caching are effective techniques for improving the performance of file systems, but they have not been studied in an integrated fashion. This paper proposes four properties that optimal integrated strategies for prefetching and caching must satisfy, and then presents and studies two such integrated strategies, called aggressive and conservative. We prove that the performance of the conservative approach is within a factor of two of optimal and that the performance of the aggressive strategy is a factor significantly less than twice that of the optimal case. We have evaluated these two approaches by trace-driven simulation with a collection of file access traces. Our results show that the two integrated prefetching and caching strategies are indeed close to optimal and that these strategies can reduce the running time of applications by up to 50%.

Advances in network and processor technology have greatly changed the communication and computational power of local-area workstation clusters.However, operating systems still treat workstation clusters as a collection of loosely-connected processors,where each workstation acts as an autonomous and independent agent.This operating system structure makes it difficult to exploit the characteristics of current clusters, such as low-latency communication, huge primary memories, and high-speed processors, in order to improve the performance of cluster applications.This paper describes the design and implementation of global memory management in a workstation cluster.Our objective is to use a single, unified, but distributed memory management algorithm at the lowest level of the operating system.By managing memory globally at this level, all system-and higher-level software, including VM, file systems, transaction systems, and user applications, can benefit from available cluster memory.We have implemented our algorithm in the OSF/1 operating system running on an ATM-connected cluster of DEC Alpha workstations.Our measurements show that on a suite of memory-intensive programs, our system improves performance by a factor of 1.5 to 3.5.We also show that our algorithm has a performance advantage over others that have been proposed in the past.

Article Balanced allocations (extended abstract) Share on Authors: Yossi Azar Tel Aviv University, Israel Tel Aviv University, IsraelView Profile , Andrei Z. Broder Digital Systems Research Center, 130 Lytton Avenue, Palo Alto, CA Digital Systems Research Center, 130 Lytton Avenue, Palo Alto, CAView Profile , Anna R. Karlin Digital Systems Research Center, 130 Lytton Avenue, Palo Alto, CA Digital Systems Research Center, 130 Lytton Avenue, Palo Alto, CAView Profile , Eli Upfal IBM Almaden Research Center, San Jose, CA and Department of Applied Mathematics, The Weizmann Institute of Science, Rehovot, Israel IBM Almaden Research Center, San Jose, CA and Department of Applied Mathematics, The Weizmann Institute of Science, Rehovot, IsraelView Profile Authors Info & Claims STOC '94: Proceedings of the twenty-sixth annual ACM symposium on Theory of ComputingMay 1994 Pages 593–602https://doi.org/10.1145/195058.195412Online:23 May 1994Publication History 82citation901DownloadsMetricsTotal Citations82Total Downloads901Last 12 Months122Last 6 weeks9 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

We study the robustness of the butterfly network against random static faults. Suppose that each edge of the butterfly is present independently of other edges with probability p. Our main result is that there is a 0-1 law on the existence of a linearsized component. More formally, there is a critical probability p such that for p above p, the faulted butterfly almost surely contains a linear-sized component, whereas for p below p, the faulted butterfly almost surely does not contain a linear-sized component. 1 Introduction Given a graph G, let G=p denote the random subgraph obtained by considering each edge independently and including it in the subgraph with probability p, excluding it with probability 1 \\Gamma p. 1 We call G=p a faulted version of G. A long list of theorems illustrate the basic fact that small changes in p can lead to dramatic changes in the connectivity of G=p. In this paper we add to this list a new theorem that shows the degree of fault-tolerance of the butter...

Aleliunas et al. [20th Annual Symposium on Foundations of Computer Science, IEEE Computer Society Press, Los Alamitos, CA, 1979, pp. 218–223] posed the following question: “The reachability problem for undirected graphs can be solved in log space and $O(mn)$ time [m is the number of edges and n is the number of vertices] by a probabilistic algorithm that simulates a random walk, or in linear time and space by a conventional deterministic graph traversal algorithm. Is there a spectrum of time-space trade-offs between these extremes?” This question is answered in the affirmative for sparse graphs by presentation of an algorithm that is faster than the random walk by a factor essentially proportional to the size of its workspace. For denser graphs, this algorithm is faster than the random walk but the speed-up factor is smaller.

The setup for the authors' problem consists of n servers that must complete a set of tasks. Each task can be handled only by a subset of the servers, requires a different level of service, and once assigned can not be re-assigned. They make the natural assumption that the level of service is known at arrival time, but that the duration of service is not. The on-line load balancing problem is to assign each task to an appropriate server in such a way that the maximum load on the servers is minimized. The authors derive matching upper and lower bounds for the competitive ratio of the on-line greedy algorithm for this problem, namely /sup (3n)2/3///sub 2/(1+o(1)), and derive a lower bound, Omega ( square root n), for any other deterministic or randomized on-line algorithm.>

This paper considers the problem of paging under the assumption that the sequence of pages accessed is generated by a Markov chain. The authors use this model to study the fault-rate of paging algorithms, a quantity of interest to practitioners. They first draw on the theory of Markov decision processes to characterize the paging algorithm that achieves optimal fault-rate on any Markov chain. They address the problem of efficiently devising a paging strategy with low fault-rate for a given Markov chain. They show that a number of intuitively good approaches fail. Their main result is an efficient procedure that, on any Markov chain, will give a paging algorithm with fault-rate at most a constant times optimal. Their techniques also show that some algorithms that do poorly in practice fail in the Markov setting, despite known (good) performance guarantees when the requests are generated independently from a probability distribution.>

AN1 (formerly known as Autonet) is a local area network composed of crossbar switches interconnected by 100Mbit/second, full-duplex links. In this paper, we evaluate the performance impact of certain choices in the AN1 design. These include the use of FIFO input buffering in the crossbar switch, the deadlock-avoidance mechanism, cut-through routing, back-pressure for flow control, and multi-path routing. AN1's performance goals were to provide low latency and high bandwidth in a lightly loaded network. In this it is successful. Under heavy load, the most serious impediment to good performance is the use of FIFO input buffers. The deadlock-avoidance technique has an adverse effect on the performance of some topologies, but it seems to be the best alternative, given the goals and constraints of the AN1 design. Cut-through switching performs well relative to store-and-forward switching, even under heavy load. Back-pressure deals adequately with congestion in a lightly-loaded network; under moderate load, performance is acceptable when coupled with end-to-end flow control for bursts. Multi-path routing successfully exploits redundant paths between hosts to improve performance in the face of congestion.

How much can an imperfect source of randomness affect an algorithm? We examine several simple questions of this type concerning the long-term behavior of a random walk on a finite graph. In our setup, each step of the random walk a “controller” can, with a certain small probability, fix the next step, thus introducing a bias. We analyze the extent to which the bias can affect the limit behavior of the walk. The controller is assumed to associate a real, nonnegative, “benefit” with each state, and to strive to maximize the long-term expected benefit. We derive tight bounds on the maximum of this objective function over all controller's strategies, and present polynomial time algorithms for computing the optimal controller strategy.

A common operation in multiprocessor programs is acquiring a lock to protect access to shared data. Typically, the requesting thread is blocked if the lock it needs is held by another thread. The cost of blocking one thread and activating another can be a substantial part of program execution time. Alternatively, the thread could spin until the lock is free, or spin for a while and then block. This may avoid context-switch overhead, but processor cycles may be wasted in unproductive spinning. This paper studies seven strategies for determining whether and how long to spin before blocking. Of particular interest are competitive strategies, for which the performance can be shown to be no worse than some constant factor times an optimal off-line strategy. The performance of five competitive strategies is compared with that of always blocking, always spinning, or using the optimal off-line algorithm. Measurements of lock-waiting time distributions for five parallel programs were used to compare the cost of synchronization under all the strategies. Additional measurements of elapsed time for some of the programs and strategies allowed assessment of the impact of synchronization strategy on overall program performance. Both types of measurements indicate that the standard blocking strategy performs poorly compared to mixed strategies. Among the mixed strategies studied, adaptive algorithms perform better than non-adaptive ones.

The computational power of 2-D and 3-D processor arrays that contain a potentially large number of faults is analyzed. Both a random and a worst-case fault model are considered, and it is proved that in either scenario low-dimensional arrays are surprisingly fault tolerant. It is also shown how to route, sort, and perform systolic algorithms for problems such as matrix multiplication in optimal time on faulty arrays. In many cases, the running time is the same as if there were no faults in the array (up to constant factors). On the negative side, it is shown that any constant congestion embedding of an n*n fault-free array on an n*n array with Theta (n/sup 2/) random faults (or Theta (log n) worst-case faults) requires dilation Theta (log n). For 3-D arrays, knot theory is used to prove that the required dilation is Omega ( square root log n).>

Aleliunas et al. [1] posed the following question: “The reachability problem for undirected graphs can be solved in logspace and O(mn) time [m is the number of edges and n is the number of vertices] by a probabilistic algorithm that simulates a random walk, or in linear time and space by a conventional deterministic graph traversal algorithm. Is there a spectrum of time-space trade-offs between these extremes?” We answer this question in the affirmative for linear-sized graphs by presenting an algorithm which is faster than the random walk by a factor essentially proportional to the size of its workspace. For denser graphs, the algorithm is faster than the random walk but the speed-up factor is smaller.

A particle that moves on a connected unidirected graph G with n vertices is considered. At each step the particle goes from the current vertex to one of its neighbors, chosen uniformly at random. The cover time is the first time when the particle has visited all the vertices in the graph, starting from a given vertex. Upper and lower bounds are presented that relate the expected cover time for a graph to the eigenvalues of the Markov chain that describes the above random walk. An interesting consequence is that regular expander graphs have expected cover time theta (n log n).>

A randomized algorithm is given for the dictionary problem with O(1) worst-case time for lookup and O(1) amortized expected time for insertion and deletion. An Omega (log n) lower bound is proved for the amortized worst-case time complexity of any deterministic algorithm in a class of algorithms encompassing realistic hashing-based schemes. If the worst-case lookup time is restricted to k, then the lower bound for insertion becomes Omega (kn/sup 1/k/).>

A central issue in the theory of parallel computation is the gap between the ideal models that utilize shared memory and the feasible models that consist of a bounded-degree network of processors sharing no common memory.This problem has been widely studied.Here a tight bound for the probabilistic complexity of this problem is established.The solution in this paper is based on a probabilistic scheme for implementing shared memory on a bounded-degree network of processors.This scheme, which we term parallel has/zing, enables n processors to store and retrieve an arbitrary set of n data items in O(logn) parallel steps.The items' locations are specified by a function chosen randomly from a small class of universal hash functions.A hash function in this class has a small description and can therefore be efficiently distributed among the processors.A deterministic lower bound for the point-to-point communication model is also presented.

In a snoopy cache multiprocessor system, each processor has a cache in which it stores blocks of data. Each cache is connected to a bus used to communicate with the other caches and with main memory. For several of the proposed models of snoopy caching, we present new on-line algorithms which decide, for each cache, which blocks to retain and which to drop in order to minimize communication over the bus. We prove that, for any sequence of operations, our algorithms' communication costs are within a constant factor of the minimum required for that sequence; for some of our algorithms we prove that no on-line algorithm has this property with a smaller constant.

Article Free Access Share on Parallel hashing—an efficient implementation of shared memory Authors: A R Karlin Computer Science Department, Stanford University Computer Science Department, Stanford UniversityView Profile , E Upfal IBM Almaden Research Center, Almaden, California IBM Almaden Research Center, Almaden, CaliforniaView Profile Authors Info & Claims STOC '86: Proceedings of the eighteenth annual ACM symposium on Theory of computingNovember 1986Pages 160–168https://doi.org/10.1145/12130.12146Published:01 November 1986Publication History 64citation415DownloadsMetricsTotal Citations64Total Downloads415Last 12 Months17Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF