Asymptotic Miss Ratio of LRU Caching with Consistent Hashing

Ji, Kaiyi; Quan, Guocong; Tan, Jian

doi:10.1109/infocom.2018.8485860

Cited by 12 publications

(3 citation statements)

References 42 publications

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“…Ji et al [28] reached similar conclusions to those of Theorem 6. However their model makes more restrictive assumptions and only applies to the case of a system of LRU caches subject to a Zipf-distributed IRM demand with α > 1 and infinite item population.…”

Section: Cache Hit Ratiosupporting

confidence: 62%

Load Imbalance and Caching Performance of Sharded Systems

Saino¹,

Psaras

Leonardi

et al. 2020

IEEE/ACM Trans. Networking

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Sharding is a method for allocating data items to nodes of a distributed caching or storage system based on the result of a hash function computed on the item's identifier. It is ubiquitously used in key-value stores, CDNs and many other applications. Despite considerable work that has focused on the design and implementation of such systems, there is limited understanding of their performance in realistic operational conditions from a theoretical standpoint. In this paper we fill this gap by providing a thorough modeling of sharded caching systems, focusing particularly on load balancing and caching performance aspects. Our analysis provides important insights that can be applied to optimize the design and configuration of sharded caching systems.

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Section: Cache Hit Ratiosupporting

confidence: 62%

Load Imbalance and Caching Performance of Sharded Systems

Saino¹,

Psaras

Leonardi

et al. 2020

IEEE/ACM Trans. Networking

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show abstract

“…erence [58]): To efficiently scale data caching infrastructure to support emerging big data applications, many caching systems rely on consistent hashing to group a large number of servers to form a cooperative cluster. These servers are organized together according to a random hash function.…”

Section: ) Asymptotic Miss Ratio Of Lru Caching With Consistent Hashi...mentioning

confidence: 99%

Bilevel Optimization for Machine Learning: Algorithm Design and Convergence Analysis

2021

Preprint

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Bilevel optimization has become a powerful framework in a variety of machine learning applications including signal processing, meta-learning, hyperparameter optimization, reinforcement learning and network architecture search. There are generally two classes of bilevel optimization formulations for modern machine learning: 1) problem-based bilevel optimization, whose inner-level problem is formulated as finding a minimizer of a given loss function; and 2) algorithm-based bilevel optimization, whose inner-level solution is an output of a fixed algorithm. For the first problem class, two popular types of gradient-based algorithms have been proposed to estimate the gradient of the outer-level objective (hypergradient) via approximate implicit differentiation (AID) and iterative differentiation (ITD). Algorithms for the second problem class include the popular model-agnostic meta-learning (MAML) and almost no inner loop (ANIL). Although bilevel optimization algorithms have been widely used, their convergence rate and fundamental limitations have not been well explored.

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“…Network utility maximization (NUM) has been studied for decades since the seminal work [1] and has been the central analytical framework for the design of fair and distributed resource allocation over the communication networks (e.g., the Internet, 6G networks). Its applications span from network congestion control [1,2,3], power allocation and routing in wireless networks [4,5], load scheduling in cloud computing [6,7,8], to video streaming over dynamic networks [9,10,11,12,13,14], and etc. A comprehensive introduction of the method and its connections to control theory and convex optimization can be found in [15].…”

Section: Introductionmentioning

confidence: 99%

Network Utility Maximization with Unknown Utility Functions: A Distributed, Data-Driven Bilevel Optimization Approach

Ji¹,

Lü²

2023

Preprint

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Fair resource allocation is one of the most important topics in communication networks. Existing solutions almost exclusively assume each user utility function is known and concave. This paper seeks to answer the following question: how to allocate resources when utility functions are unknown, even to the users? This answer has become increasingly important in the next-generation AI-aware communication networks where the user utilities are complex and their closed-forms are hard to obtain. In this paper, we provide a new solution using a distributed and data-driven bilevel optimization approach, where the lower level is a distributed network utility maximization (NUM) algorithm with concave surrogate utility functions, and the upper level is a data-driven learning algorithm to find the best surrogate utility functions that maximize the sum of true network utility. The proposed algorithm learns from data samples (utility values or gradient values) to autotune the surrogate utility functions to maximize the true network utility, so works for unknown utility functions. For the general network, we establish the nonasymptotic convergence rate of the proposed algorithm with nonconcave utility functions. The simulations validate our theoretical results and demonstrate the great effectiveness of the proposed method in a real-world network.

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Asymptotic Miss Ratio of LRU Caching with Consistent Hashing

Cited by 12 publications

References 42 publications

Load Imbalance and Caching Performance of Sharded Systems

Load Imbalance and Caching Performance of Sharded Systems

Bilevel Optimization for Machine Learning: Algorithm Design and Convergence Analysis

Network Utility Maximization with Unknown Utility Functions: A Distributed, Data-Driven Bilevel Optimization Approach

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