M Sackman · arXiv (Cornell University) · 2015
Consistent Hashing functions are widely used for load balancing across a variety of applications. However, the original presentation and typical implementations of Consistent Hashing rely on randomised allocation of hash codes to keys which results in a flawed and approximately-uniform allocation of keys to hash codes. We analyse the desired properties and present an algorithm that perfectly achieves them without resorting to any random distributions. The algorithm is simple and adds to our understanding of what is necessary to create a consistent hash function.
Cited by 5Open access
Neil Alldrin · 2002
Consistent hashing is a special kind of hashing which is useful for certain applications. Originally devised by Karger et. al. at MIT[2] for use in distributed
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Ben Appleton, Michael O’Reilly · arXiv (Cornell University) · 2015
We describe a consistent hashing algorithm which performs multiple lookups per key in a hash table of nodes. It requires no additional storage beyond the hash table, and achieves a peak-to-average load ratio of 1 + epsilon with just 1 + 1/epsilon lookups per key.
Cited by 12Open access
D Vimala, I Linda, C Geetha et al. · International Journal of Innovative Technology and Exploring Engineering · 2019
The combination of put in writing flipside cache has studied IPv6, and up to date trends put forward that the construction of hash tables will materialize. Here, we show the amalgamation of Web services, which symbolize the archetypal principles of theory. We motivate a low energy means for exploring the location-identity split, which we call WAVEY.
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Zheng Zhang · 2024
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Erinn Rekurs · 2014
Unified ambimorphic communication have led to many significant advances, including Moore's Law and e-business [1,1,10]. In fact, few experts would disagree with the analysis of model checking, which embodies the robust principles of machine learning. This is crucial to the success of our work. In our research, we use cooperative configurations to disprove that telephony and Boolean logic [8] can interfere to fix this riddle. 1
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Thorsten Schütt, Florian Schintke, Alexander Reinefeld · 2006
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David Karger, Alexsherman Ł, Andy Berkheimer et al. · 2008
A key performance measure for the World Wide Web is the speed with which content is served to users. As traffic on the Web increases, users are faced with increasing delays and failures in data delivery. Web caching is one of the key strategies that has been explored to improve performance. An important issue in many caching systems is how to decide what is cached where at any given time. Solutions have included multicast queries and directory schemes. In this paper, we offer a new Web caching strategy based on consistent hashing. Consistent hashing provides an alternative to multicast and directory schemes, and has several other advantages in load balancing and fault tolerance. Its performance was analyzed theoretically in previous work; in this paper we describe the implementation of a consistent-hashing-based system and experiments that support our thesis that it can provide performance improvements.
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Coluzzi, Massimo, Brocco, Amos, Contu, Patrizio et al. · IEEE DataPort · 2022
This dataset has been used to evaluate different consistent hashing algorithms for non-peer-to-peer contexts. Further information can be found at https://github.com/SUPSI-DTI-ISIN/java-consistent-hashing-algorithms
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Linsu · Zenodo (CERN European Organization for Nuclear Research) · 2020
This is a test for Two-level-Attributed-Consistent-Hashing.
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Haitao Lin, Min Meng, Jigang Wu · 2022 IEEE International Conference on Multimedia and Expo (ICME) · 2022
Most of the existing cross-modal hashing (CMH) methods are trained in a batch-based manner, which is time-consuming and unable to handle streaming data. Recently, online CMH methods have attracted increasing attention. However, existing online CMH methods face two limitations: 1) they usu-ally excavate the common semantic information by learning modality-specific projection matrix for each modality, while ignoring the intrinsic relationship among different modali-ties; 2) they often suffer from learning less discriminative hash codes because of insufficiently exploiting the pairwise similarity. To tackle these challenges, we propose a novel Online Robust Specific and Consistent Hashing (ORSCH) method. Specifically, ORSCH decomposes the projection ma-trices into consistent and modality-specific ones, which ef-fectively exploits intrinsic semantic information of streaming data in different modalities. Furthermore, we utilize both dis-crete and continuous labels to construct affinity matrices to improve the discrimination of hash codes. Experiments on three benchmark datasets show the superiority of ORSCH.
Cited by 3
O Valentino, Jeffrey Hargrave · 2011
Many steganographers would agree that, had it not been for vacuum tubes, the construction of evolutionary programming might never have occurred. After years of practical research into DHCP, we show the improvement of neural networks, which embodies the theoretical principles of hardware and architecture. We explore a virtual tool for investigating courseware (Cub), showing that simulated annealing can be made interposable, collaborative, and optimal.
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Ike Antkare · 2009
Experts agree that secure symmetries are an interesting new topic in the field of hardware and architecture, and hackers worldwide concur. In this work, we confirm the development of I/O automata. Here, we present a “smart” tool for evaluating local-area networks (Off), which we use to prove that architecture can be made large-scale, stable, and stable. 1
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Eric Leu · arXiv (Cornell University) · 2023
Consistent hashing is a technique that can minimize key remapping when the number of hash buckets changes. The paper proposes a fast consistent hash algorithm (called power consistent hash) that has $O(1)$ expected time for key lookup, independent of the number of buckets. Hash values are computed in real time. No search data structure is constructed to store bucket ranges or key mappings. The algorithm has a lightweight design using $O(1)$ space with superior scalability. In particular, it uses two auxiliary hash functions to achieve distribution uniformity and $O(1)$ expected time for key lookup. Furthermore, it performs consistent hashing such that only a minimal number of keys are remapped when the number of buckets changes. Consistent hashing has a wide range of use cases, including load balancing, distributed caching, and distributed key-value stores. The proposed algorithm is faster than well-known consistent hash algorithms with $O(\log n)$ lookup time.
Cited by 1Open access
John Chen, Benjamin Coleman, Anshumali Shrivastava · arXiv (Cornell University) · 2019
Dynamic load balancing lies at the heart of distributed caching. Here, the goal is to assign objects (load) to servers (computing nodes) in a way that provides load balancing while at the same time dynamically adjusts to the addition or removal of servers. One essential requirement is that the addition or removal of small servers should not require us to recompute the complete assignment. A popular and widely adopted solution is the two-decade-old Consistent Hashing (CH). Recently, an elegant extension was provided to account for server bounds. In this paper, we identify that existing methodologies for CH and its variants suffer from cascaded overflow, leading to poor load balancing. This cascading effect leads to decreasing performance of the hashing procedure with increasing load. To overcome the cascading effect, we propose a simple solution to CH based on recent advances in fast minwise hashing. We show, both theoretically and empirically, that our proposed solution is significantly superior for load balancing and is optimal in many senses. On the AOL search dataset and Indiana University Clicks dataset with real user activity, our proposed solution reduces cache misses by several magnitudes.
Cited by 0Open access
Association for Artificial Intelligence 2021, Chen, John, Coleman, Benjamin et al. · Underline Science Inc. · 2021
Dynamic load balancing lies at the heart of distributed caching. Here, the goal is to assign objects (load) to servers (computing nodes) in a way that provides load balancing while at the same time dynamically adjusts to the addition or removal of servers. Load balancing is a critical topic in many areas including cloud systems, distributed databases, and distributed and data-parallel machine learning. A popular and widely adopted solution to dynamic load balancing is the two-decade-old Consistent Hashing (CH). Recently, an elegant extension was provided to account for server bounds. In this paper, we identify that existing methodologies for CH and its variants suffer from cascaded overflow, leading to poor load balancing. This cascading effect leads to decreasing performance of the hashing procedure with increasing load. To overcome the cascading effect, we propose a simple solution to CH based on recent advances in fast minwise hashing. We show, both theoretically and empirically, that our proposed solution is significantly superior for load balancing and is optimal in many senses. On the AOL search dataset and Indiana University Clicks dataset with real user activity, our proposed solution reduces cache misses by several magnitudes.
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Yanzhuo Li, Leilei Yu, Jiasheng Yuan et al. · 2025
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Write-up Cathrin Weiß, Prof Weikum · 2011
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Wei Xie, Yong Chen · 2017
Elastic distributed storage systems have been increasingly studied in recent years because power consumption has become a major problem in data centers. Much progress has been made in improving the agility of resizing small- and large-scale distributed storage systems. However, most of these studies focus on metadata based distributed storage systems. On the other hand, emerging consistent hashing based distributed storage systems are considered to allow better scalability and are highly attractive. We identify challenges in achieving elasticity in consistent hashing based distributed storage. These challenges cannot be easily solved by techniques used in current studies. In this paper, we propose an elastic consistent hashing based distributed storage to solve two problems. First, in order to allow a distributed storage to resize quickly, we modify the data placement algorithm using a primary server design and achieve an equal-work data layout. Second, we propose a selective data re-integration technique to reduce the performance impact when resizing a cluster. Our experimental and trace analysis results confirm that our proposed elastic consistent hashing works effectively and allows significantly better elasticity.
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Tomasz Madejski, Rob Shakir · 2017
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