Realizing sets by hash tables

1995 ◽  
pp. 58-73
Author(s):  
Peter Pepper ◽  
Ralph Betschko ◽  
Sabine Dick ◽  
Klaus Didrich
Keyword(s):  
Hash Tables

scholarly journals An IOTA-Based Service Discovery Framework for Fog Computing

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 844
Author(s):  
Tsung-Yi Tang ◽  
Li-Yuan Hou ◽  
Tyng-Yeu Liang
Keyword(s):  
Service Discovery ◽  
Fog Computing ◽  
Cost Effective ◽  
Public Access ◽  
Hash Tables ◽  
Network Attacks ◽  
Comprehensive Information ◽  
Data Flows ◽  
Multiple Levels ◽  
Discovery Method

With the rise in fog computing, users are no longer restricted to only accessing resources located in central and distant clouds and can request services from neighboring fog nodes distributed over networks. This can effectively reduce the network latency of service responses and the load of data centers. Furthermore, it can prevent the Internet’s bandwidth from being used up due to massive data flows from end users to clouds. However, fog-computing resources are distributed over multiple levels of networks and are managed by different owners. Consequently, the problem of service discovery becomes quite complicated. For resolving this problem, a decentralized service discovery method is required. Accordingly, this research proposes a service discovery framework based on the distributed ledger technology of IOTA. The proposed framework enables clients to directly search for service nodes through any node in the IOTA Mainnet to achieve the goals of public access and high availability and avoid network attacks to distributed hash tables that are popularly used for service discovery. Moreover, clients can obtain more comprehensive information by visiting known nodes and select a fog node able to provide services with the shortest latency. Our experimental results have shown that the proposed framework is cost-effective for distributed service discovery due to the advantages of IOTA. On the other hand, it can indeed enable clients to obtain higher service quality by automatic node selection.

Technical Perspective

2021 ◽  
Vol 50 (1) ◽  
pp. 59-59
Author(s):  
Marcin Zukowski
Keyword(s):  
Query Processing ◽  
Hash Tables ◽  
Database Query ◽  
Query Performance ◽  
Research Opportunities ◽  
Database Query Processing

Hash tables are possibly the single most researched element of the database query processing layers. There are many good reasons for that. They are critical for some key operations like joins and aggregation, and as such are one of the largest contributors to the overall query performance. Their efficiency is heavily impacted by variations of workloads, hardware and implementation, leading to many research opportunities. At the same time, they are sufficiently small and local in scope, allowing a starting researcher, or even a student, to understand them and contribute novel ideas. And benchmark them. . . Oh, the benchmarks. . . :)

scholarly journals Authentication in Stealth Distributed Hash Tables

2006 ◽  
Author(s):  
A. MacQuire ◽  
A. Brampton ◽  
I.A. Rai ◽  
N.J.P. Race ◽  
L. Mathy
Keyword(s):  
Distributed Hash Tables ◽  
Hash Tables

Persistent memory hash indexes

2021 ◽  
Vol 14 (5) ◽  
pp. 785-798
Author(s):  
Daokun Hu ◽  
Zhiwen Chen ◽  
Jianbing Wu ◽  
Jianhua Sun ◽  
Hao Chen
Keyword(s):  
Future Development ◽  
High Performance ◽  
Performance Metrics ◽  
Comprehensive Evaluation ◽  
State Of The Art ◽  
Hash Tables ◽  
Trade Offs ◽  
Depth Analysis ◽  
Persistent Memory ◽  
Memory Modules

Persistent memory (PM) is increasingly being leveraged to build hash-based indexing structures featuring cheap persistence, high performance, and instant recovery, especially with the recent release of Intel Optane DC Persistent Memory Modules. However, most of them are evaluated on DRAM-based emulators with unreal assumptions, or focus on the evaluation of specific metrics with important properties sidestepped. Thus, it is essential to understand how well the proposed hash indexes perform on real PM and how they differentiate from each other if a wider range of performance metrics are considered. To this end, this paper provides a comprehensive evaluation of persistent hash tables. In particular, we focus on the evaluation of six state-of-the-art hash tables including Level hashing, CCEH, Dash, PCLHT, Clevel, and SOFT, with real PM hardware. Our evaluation was conducted using a unified benchmarking framework and representative workloads. Besides characterizing common performance properties, we also explore how hardware configurations (such as PM bandwidth, CPU instructions, and NUMA) affect the performance of PM-based hash tables. With our in-depth analysis, we identify design trade-offs and good paradigms in prior arts, and suggest desirable optimizations and directions for the future development of PM-based hash tables.

Chapter 6: Hash Tables, Maps, and Sets

2016 ◽  
pp. 137-170
Author(s):  
Edmund Weitz
Keyword(s):  
Hash Tables

More Practical and Secure History-Independent Hash Tables

2016 ◽  
pp. 20-38 ◽  
Author(s):  
Michael T. Goodrich ◽  
Evgenios M. Kornaropoulos ◽  
Michael Mitzenmacher ◽  
Roberto Tamassia
Keyword(s):  
Hash Tables

Hash Tables

2013 ◽  
pp. 203-220
Keyword(s):  
Hash Tables
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