2020
DOI: 10.1109/tnet.2019.2957075
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Load Imbalance and Caching Performance of Sharded Systems

Abstract: 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 thoro… Show more

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Cited by 4 publications
(3 citation statements)
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“…Xiong et al in [165] adopt ADMM algorithm to search the optimal solution for the benefit of miners and cloud/edge providers. In terms of sharding cache system optimization, Lorenzo et al in [166] focus on two important metrics, load balancing and caching performance, in a sharding system. This work first studies the factors that cause load imbalance in a sharding system and then analyzes how sharding affects cache hit performance.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Xiong et al in [165] adopt ADMM algorithm to search the optimal solution for the benefit of miners and cloud/edge providers. In terms of sharding cache system optimization, Lorenzo et al in [166] focus on two important metrics, load balancing and caching performance, in a sharding system. This work first studies the factors that cause load imbalance in a sharding system and then analyzes how sharding affects cache hit performance.…”
Section: Related Workmentioning
confidence: 99%
“…mining pool security strategy [135] --Iterative game mining pool management [129] [130] --Auction -resource management in MEC [138] [139] -Others mining pool management [128] performance analysis [131] mining competition analysis [132] security condition acquisition [136] [226] security in edge networks [140] resource allocation [145] -Optimization theory Convex optimization -security in D2D communication [152] -Geometric programming -resource allocation in IoT [163] -Stochastic programming -optimal algorithm and strategy design in mobile edge network [169] -Lyapunov Optimization DDoS attack avoidance [159] resource allocation in mobile device cloud [164] -Others analytical framework modeling for PBFT [157] sharding security [160] performance analysis [166] [165] security [158] [161], resource allocation in MEC [162], optimal algorithm and strategy design in payment channel network [167] optimal algorithm and strategy design in electric taxi charging scenarios [168] Machine Learning Supervised learning majority-attack avoidance [190] --Unsupervised learning performance optimization [211] security in Bitcoin [204] [205] [206] energy trading [222] data privacy [223] Federated learning -privacy and security in centralized machine learning [193] intrusion identification [208] privacy protection [218] [213] -Deep learning identify malicious nodes [203] application in IoT [217], application in smart grid [221] Reinforcement learning -resource management in IoT …”
Section: A Cryptographymentioning
confidence: 99%
“…File popularity in coded caching, and the problem of symmetry: Before recalling how non-uniform file popularity has been used in coded-caching, let us quickly recall that exploiting file popularity has been a key concept from the early works of Content Delivery Network systems [21], [22], Content-Centric and Information-Centric Networks [23], [24], multi-tier networks [25], as well as in wireless edge caching works [26] that followed the femto-caching ideas of [27]. Such works generally focus on exploiting caches to 'prefetch' content, and have little to do with using caches to handle network interference.…”
Section: Introductionmentioning
confidence: 99%