Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing最新文献

{"title":"The Recoverable Consensus Hierarchy","authors":"W. Golab","doi":"10.1145/3293611.3331574","DOIUrl":"https://doi.org/10.1145/3293611.3331574","url":null,"abstract":"Herlihy's consensus hierarchy ranks the power of various synchronization primitives for solving consensus in a model where asynchronous processes communicate through shared memory, and may fail by halting. This paper revisits the consensus hierarchy in a model with crash-recovery failures, where the specification of consensus, called recoverable consensus in this paper, is weakened by allowing non-terminating executions when a process fails infinitely often. Two variations of this model are considered: with independent process failures, and with simultaneous (i.e., system-wide) process failures. We prove two fundamental results: (i) Test-And-Set is at level 2 of the recoverable consensus hierarchy if failures are simultaneous, and similarly for any primitive at level 2 of the traditional consensus hierarchy; and (ii) Test-And-Set drops to level 1 of the hierarchy if failures are independent, unless the number of such failures is bounded. To our knowledge, this is the first separation between the simultaneous and independent crash-recovery failure models with respect to the computability of consensus.","PeriodicalId":153766,"journal":{"name":"Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123488135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed Minimum Degree Spanning Trees","authors":"M. Dinitz, M. Halldórsson, Taisuke Izumi, Calvin C. Newport","doi":"10.1145/3293611.3331604","DOIUrl":"https://doi.org/10.1145/3293611.3331604","url":null,"abstract":"The minimum degree spanning tree (MDST) problem requires the construction of a spanning tree T for graph G, such that the maximum degree of T is the smallest among all spanning trees of G. Let d be this MDST degree for a given graph. In this paper, we present a randomized distributed approximation algorithm for the MDST problem that constructs a spanning tree with maximum degree in O(d+log n ). With high probability in n, the algorithm runs in O((D + √n) log4 n) rounds, in the broadcast-CONGEST model, where D is the graph diameter and n is the graph size. We then show how to derandomize this algorithm, obtaining the same asymptotic guarantees on degree and time complexity, but now requiring the standard CONGEST model. Although efficient approximation algorithms for the MDST problem have been known in the sequential setting since the 1990's (finding an exact solution is NP-hard), our algorithms are the first efficient distributed solutions. We conclude by proving a lower bound that establishes that any randomized MDST algorithm that guarantees a maximum degree in ∼Ω (d) requires &Ø#8764;Ω (n1/3) rounds, and any deterministic solution requires ∼Ω (n1/2) rounds. These bounds proves our deterministic algorithm to be asymptotically optimal, and eliminates the possibility of significantly more efficient randomized solutions.","PeriodicalId":153766,"journal":{"name":"Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126576660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Classical to Blockchain Consensus: What Are the Exact Algorithms?","authors":"Yanhong A. Liu, S. Stoller","doi":"10.1145/3293611.3338022","DOIUrl":"https://doi.org/10.1145/3293611.3338022","url":null,"abstract":"This tutorial describes well-known algorithms for distributed consensus problems, from classical consensus to blockchain consensus, and discusses exact algorithms that are high-level as in pseudocode and directly executable at the same time. The tutorial consists of five parts: (1) A introduction to different distributed consensus problems, from classical consensus and Byzantine consensus to blockchain consensus. (2) An overview of well-known algorithms, from Paxos for classical consensus to the Bitcoin algorithm for blockchain consensus, including important variants such as Viewstamped Replication and Virtual Synchrony, as well as Proof-of-Stake vs. Proof-of-Work. (3) An overview of a method and language, DistAlgo, for expressing distributed algorithms precisely at a high-level as pseudocode and having them be directly executable at the same time. (4) A study of exact algorithms expressed at a high level for the most extensively studied algorithm variants, including Lamport's Paxos for classical consensus and Nakamoto's Bitcoin algorithm for blockchain consensus. (5) A demo of the direct execution of these exact algorithms by distributed processes.","PeriodicalId":153766,"journal":{"name":"Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing","volume":"45 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133686040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vorpal","authors":"Kunal Korgaonkar, Joseph Izraelevitz, Jishen Zhao, S. Swanson","doi":"10.1145/3293611.3331598","DOIUrl":"https://doi.org/10.1145/3293611.3331598","url":null,"abstract":"In systems with non-volatile main memories (NVMMs), programmers must carefully control the order in which writes become persistent. Otherwise, what will remain in persistence after a crash may be unusable upon recovery. Prior art has already explored semantic models for specifying this persist order, but most enforcement algorithms for the order are not scalable to large server machines because they assume that the machine contains only one or two memory controllers. In this paper, we describe a collection of provably correct algorithms for enforcing the persist-order across writes, generated at many different cores, and persisted across numerous different memory controllers. Relative to existing solutions, our algorithms improve performance by 48% by reducing both traffic and serialization overheads.","PeriodicalId":153766,"journal":{"name":"Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115530396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plain SINR is Enough!","authors":"M. Halldórsson, Tigran Tonoyan","doi":"10.1145/3293611.3331602","DOIUrl":"https://doi.org/10.1145/3293611.3331602","url":null,"abstract":"We develop randomized distributed algorithms for many of the most fundamental communication problems in the wireless SINR model, including (multi-message) broadcast, local broadcast, coloring, MIS, and aggregation. The complexity of the algorithms is optimal up to polylogarithmic preprocessing time. It shows -- contrary to expectation -- that the plain vanilla SINR model is just as powerful and fast (modulo the preprocessing) as various extensions studied, including power control, carrier sense, collision detection, free acknowledgements, and GPS location. A key component of the algorithms is an efficient simulation of CONGEST algorithms on a constant-density SINR backbone.","PeriodicalId":153766,"journal":{"name":"Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129570694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2019 Principles of Distributed Computing Doctoral Dissertation Award","authors":"P. Jayanti, N. Lynch, B. Patt-Shamir, U. Schmid","doi":"10.1145/3293611.3341565","DOIUrl":"https://doi.org/10.1145/3293611.3341565","url":null,"abstract":"The winner of the 2019 Principles of Distributed Computing Doctoral Dissertation Award is Dr. Sepehr Assadi for his dissertation Combinatorial Optimization on Massive Datasets: Streaming, Distributed, and Massively Parallel Computation, written under the supervision of Prof. Sanjeev Khanna at the University of Pennsylvania. The thesis resolves a number of long-standing problems in the exciting and still relatively new area of sublinear computation. The area of sublinear computation focuses on design of algorithms that use sublinear space, time, or communication to solve global optimization problems on very large datasets. In addition to addressing a wide range of different problems, comprising graph optimization problems (matching, vertex cover, and connectivity), submodular optimization (set cover and maximum coverage), and resource-constrained optimization (combinatorial auctions and learning), these problems are studied in three different models of computation, namely, streaming algorithms, multiparty communication, and massively parallel computation (MPC). The thesis also reveals interesting relations between these different models, including generic algorithmic and analysis techniques that can be applied in all of them. For many fundamental optimization problems, the thesis gives asymptotically matching algorithmic and intractability results, completely resolving several long-standing problems. This is accomplished by using a broad spectrum of mathematical methods in very detailed and intricate proofs. In addition to a wide variety of classic techniques, ranging from graph theory, combinatorics, probability, linear algebra and calculus, it also makes heavy use of communication complexity and information theory, for example. Sepehr's dissertation work has been published in a remarkably large number of top-conference papers. It received multiple best paper awards and multiple special issue invitations, as well as two invitations to the Highlights of Algorithms (HALG) conference. Due to its contributions to the field of distributed computing and all the merits mentioned above, the award committee unanimously selected this thesis as the winner of the 2019 Principles of Distributed Computing Doctoral Dissertation Award.","PeriodicalId":153766,"journal":{"name":"Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114179992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How to Spread a Rumor: Call Your Neighbors or Take a Walk?","authors":"George Giakkoupis, Frederik Mallmann-Trenn, Hayk Saribekyan","doi":"10.1145/3293611.3331622","DOIUrl":"https://doi.org/10.1145/3293611.3331622","url":null,"abstract":"We study the problem of randomized information dissemination in networks. We compare the now standard PUSH-PULL protocol, with agent-based alternatives where information is disseminated by a collection of agents performing independent random walks. In the VISIT-EXCHANGE protocol, both nodes and agents store information, and each time an agent visits a node, the two exchange all the information they have. In the MEET-EXCHANGE protocol, only the agents store information, and exchange their information with each agent they meet. We consider the broadcast time of a single piece of information in an n-node graph for the above three protocols, assuming a linear number of agents that start from the stationary distribution. We observe that there are graphs on which the agent-based protocols are significantly faster than PUSH-PULL, and graphs where the converse is true. We attribute the good performance of agent-based algorithms to their inherently fair bandwidth utilization, and conclude that, in certain settings, agent-based information dissemination, separately or in combination with PUSH-PULL, can significantly improve the broadcast time. The graphs considered above are highly non-regular. Our main technical result is that on any regular graph of at least logarithmic degree, PUSH-PULL and VISIT-EXCHANGE have the same asymptotic broadcast time. The proof uses a novel coupling argument which relates the random choices of vertices in PUSH-PULL with the random walks in VISIT-EXCHANGE. Further, we show that the broadcast time of MEET-EXCHANGE is asymptotically at least as large as the other two's on all regular graphs, and strictly larger on some regular graphs. As far as we know, this is the first systematic and thorough comparison of the running times of these very natural information dissemination protocols.","PeriodicalId":153766,"journal":{"name":"Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114574820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Constant Amortized RMR Abortable Mutex for CC and DSM","authors":"P. Jayanti, S. Jayanti","doi":"10.1145/3293611.3331592","DOIUrl":"https://doi.org/10.1145/3293611.3331592","url":null,"abstract":"The Abortable mutual exclusion problem, proposed by Scott and Scherer in response to the needs in real time systems and databases, is a variant of mutual exclusion that allows processes to abort from their attempt to acquire the lock. Worst-case constant remote memory reference (RMR) algorithms for mutual exclusion using hardware instructions such as Fetch&Add or Fetch&Store have long existed for both Cache Coherent (CC) and Distributed Shared Memory (DSM) multiprocessors, but no such algorithms are known for abortable mutual exclusion. Even relaxing the worst-case requirement to amortized, algorithms are only known for the CC model. In this paper, we improve this state-of-the-art by designing a deterministic algorithm that uses Fetch&Store (FAS) to achieve amortized O(1) RMR in both the CC and DSM models. Our algorithm supports Fast Abort (a process aborts within six steps of receiving the abort signal), and has the following additional desirable properties: it supports an arbitrary number of processes of arbitrary names, requires only O(1) space per process, and satisfies a novel fairness condition that we call \"Airline FCFS''. Our algorithm is short and practical with fewer than a dozen lines of code.","PeriodicalId":153766,"journal":{"name":"Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125992416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Massively Parallel Computation of Matching and MIS in Sparse Graphs","authors":"Soheil Behnezhad, S. Brandt, M. Derakhshan, Manuela Fischer, M. Hajiaghayi, R. Karp, Jara Uitto","doi":"10.1145/3293611.3331609","DOIUrl":"https://doi.org/10.1145/3293611.3331609","url":null,"abstract":"The Massively Parallel Computation (MPC) model serves as a common abstraction of many modern large-scale parallel computation frameworks and has recently gained a lot of importance, especially in the context of classic graph problems. In this work, we mainly consider maximal matching and maximal independent set problems in the MPC model. These problems are known to admit efficient MPC algorithms if the space available per machine is near-linear in the number n of nodes. This is not only often significantly more than what we can afford, but also allows for easy if not trivial solutions for sparse graphs---which are common in real-world large-scale graphs. We are, therefore, interested in the low-memory MPC model, where the space per machine is restricted to be strongly sublinear, that is, nδ for any constant 0 < δ < 1. We parametrize our algorithms by the arboricity λ of the input graph. Our key ingredient is a degree reduction technique that reduces these problems in graphs with arboricity λ to the corresponding problems in graphs with maximum degree poly(λ, log n) in O(log2 log n) rounds, giving rise to O(√ log λ ⋅ log log λ + log 2 log n)-round algorithms. Our result is particularly interesting for graphs with poly log n arboricity as for such graphs, we get O(log 2 log n)-round algorithms. This covers most natural families of sparse graphs and almost exponentially improves over previous algorithms that all required log Ω(1) n rounds in this regime of MPC. Finally, our maximal matching algorithm can be employed to obtain a (1+ε)-approximate maximum cardinality matching, a (2+ε)-approximate maximum weighted matching, as well as a 2-approximate minimum vertex cover in essentially the same number of rounds.","PeriodicalId":153766,"journal":{"name":"Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115221896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can Distributed Uniformity Testing Be Local?","authors":"Uri Meir, Dor Minzer, R. Oshman","doi":"10.1145/3293611.3331613","DOIUrl":"https://doi.org/10.1145/3293611.3331613","url":null,"abstract":"In the distributed uniformity testing problem, k servers draw samples from some unknown distribution, and the goal is to determine whether the unknown distribution is uniform or whether it is ε-far from uniform, where ε is a proximity parameter. Each server decides whether to accept or reject, and these decisions are sent to a referee, who makes a final decision based on the servers' local decisions. Uniformity testing is a particularly useful building-block, because it is complete for the problem of testing identity to any fixed distribution. It was recently shown that distributing the task of uniformity testing allows each server to draw fewer samples than are needed in the centralized case, but so far the number of samples required for distributed uniformity testing has not been well understood. In this paper we settle this question, and also investigate the cost of using local decision rules, such as rejecting iff at least one server wants to reject (the usual decision rule used in local distributed decision). To answer these questions, we develop a new Fourier-based technique for proving lower bounds on the sample complexity of distribution testing, which lends itself particularly well to the distributed case. Using our technique, we tightly characterize the number of samples required for uniformity testing when the referee can apply any decision function to the servers' local decisions. We also show that if the network rejects whenever one server wants to reject, then the cost of uniformity testing is much higher, and in fact we do not gain compared to the centralized case unless the number of servers is exponential in Ω (1/ε). Finally, we apply our lower bound technique to the case where the referee applies a threshold decision rule, and also generalize a lower bound from[1] for learning an unknown input distribution.","PeriodicalId":153766,"journal":{"name":"Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116179577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}