Fragment Re-Allocation Strategy Based on Hypergraph for NoSQL Database Systems

2016 ◽  
Vol 8 (3) ◽  
pp. 1-23 ◽  
Author(s):  
Zhikun Chen ◽  
Shuqiang Yang ◽  
Yunfei Shang ◽  
Yong Liu ◽  
Feng Wang ◽  
...  
Keyword(s):  
Database Systems ◽  
Communication Cost ◽  
Allocation Strategy ◽  
Hypergraph Partitioning ◽  
Computing Model ◽  
Operation Pattern ◽  
Nosql Database ◽  
High Scalability ◽  
Weighted Hypergraph ◽  
Allocation Strategies

NoSQL database is famed for the characteristics of high scalability, high availability, and high fault-tolerance. It is used to manage data for a lot of applications. The computing model has been transferred to “computing close to data”. Therefore, the location of fragment directly affects system's performance. Every site's load dynamical changes because of the increasing data and the ever-changing operation pattern. So system has to re-allocate fragment to improve system's performance. The general fragment re-allocation strategies of NoSQL database scatter the related fragments as possible to improve the operations' parallel degree. But those fragments may interact with each other in some application's operations. So the high parallel degree of operation may increase system's communication cost such as data are transferred by network. In this paper, the authors propose a fragment re-allocation strategy based on hypergraph. This strategy uses a weighted hypergraph to represent the fragments' access pattern of operations. A hypergraph partitioning algorithm is used to cluster fragments in the strategy. This strategy can improve system's performance according to reducing the communication cost while guaranteeing the parallel degree of operations. Experimental results confirm that the strategy will effectively contribute in solving fragment re-allocation problem in specific application environment of NoSQL database system, and it can improve system's performance.

A new fragment re-allocation strategy for NoSQL database systems

2014 ◽  
Vol 9 (1) ◽  
pp. 111-127 ◽  
Author(s):  
Zhikun Chen ◽  
Shuqiang Yang ◽  
Shuang Tan ◽  
Li He ◽  
Hong Yin ◽  
...  
Keyword(s):  
Database Systems ◽  
Allocation Strategy ◽  
Nosql Database

scholarly journals Performance Benchmarking of Key-Value Store NoSQL Databases

2018 ◽  
Vol 8 (6) ◽  
pp. 5333
Author(s):  
Omoruyi Osemwegie ◽  
Kennedy Okokpujie ◽  
Nsikan Nkordeh ◽  
Charles Ndujiuba ◽  
Samuel John ◽  
...  
Keyword(s):  
Data Storage ◽  
Web Applications ◽  
Query Language ◽  
Research Work ◽  
Database Systems ◽  
Nosql Databases ◽  
Performance Benchmarking ◽  
Nosql Database ◽  
Structured Query Language ◽  
Web Developers

<p>Increasing requirements for scalability and elasticity of data storage for web applications has made Not Structured Query Language NoSQL databases more invaluable to web developers. One of such NoSQL Database solutions is Redis. A budding alternative to Redis database is the SSDB database, which is also a key-value store but is disk-based. The aim of this research work is to benchmark both databases (Redis and SSDB) using the Yahoo Cloud Serving Benchmark (YCSB). YCSB is a platform that has been used to compare and benchmark similar NoSQL database systems. Both databases were given variable workloads to identify the throughput of all given operations. The results obtained shows that SSDB gives a better throughput for majority of operations to Redis’s performance.</p>

Localization of Data Sets in Distributed Database Systems Using Slope-Based Vertical Fragmentation

2020 ◽  
pp. 36-60
Author(s):  
Ashish Ranjan Mishra ◽  
Neelendra Badal
Keyword(s):  
Database Systems ◽  
Distributed Database ◽  
Communication Cost ◽  
Data Sets ◽  
Distributed Database Systems ◽  
The Third ◽  
Vertical Partitioning ◽  
Vertical Fragmentation ◽  
Partitioning Algorithm ◽  
Better Than

This chapter explains an algorithm that can perform vertical partitioning of database tables dynamically on distributed database systems. After vertical partitioning, a new algorithm is developed to allocate that fragments to the proper sites. To accomplish this, three major tasks are performed in this chapter. The first task is to develop a partitioning algorithm, which can partition the relation in such a way that it would perform better than most of the existing algorithms. The second task is to allocate the fragments to the appropriate sites where allocating the fragments will incur low communication cost with respect to other sites. The third task is to monitor the change in frequency of queries at different sites as well as same site. If the change in frequency of queries at different sites as well as the same site exceeds the threshold, the re-partitioning and re-allocation are performed.

scholarly journals Evaluation of the United States COVID-19 Vaccine Allocation Strategy

2021 ◽  
Author(s):  
Md Rafiul Islam ◽  
Tamer Oraby ◽  
Audrey McCombs ◽  
Mohammad Mihrab Chowdhury ◽  
Mohammad Al-Mamun ◽  
...  
Keyword(s):  
United States ◽  
Disease Model ◽  
Age Groups ◽  
Optimal Strategies ◽  
The United States ◽  
Living Condition ◽  
Years Of Life Lost ◽  
Allocation Strategy ◽  
Optimal Allocations ◽  
Allocation Strategies

Background: Anticipating an initial shortage of vaccines for COVID-19, the Centers for Disease Control (CDC) in the United States developed priority vaccine allocations for specific demographic groups in the population. This study evaluates the performance of the CDC vaccine allocation strategy with respect to multiple potentially competing vaccination goals (minimizing mortality, cases, infections, and years of life lost (YLL)), under the same framework as the CDC allocation: four priority vaccination groups and population demographics stratified by age, comorbidities, occupation and living condition (congested or non-congested). Methods: We developed a compartmental disease model that incorporates key elements of the current pandemic including age-varying susceptibility to infection, age-varying clinical fraction, an active case-count dependent social distancing level, and time-varying infectivity (accounting for the emergence of more infec- tious virus strains). Under this model, the CDC allocation strategy is compared to all other possibly optimal allocations that stagger vaccine roll-out in up to four phases (17.5 million strategies). Results: The CDC allocation strategy performed well in all vaccination goals but never optimally. Under the developed model, the CDC allocation deviated from the optimal allocations by small amounts, with 0.19% more deaths, 4.0% more cases, 4.07% more infections, and 0.97% higher YLL, than the respective optimal strategies. The CDC decision to not prioritize the vaccination of individuals under the age of 16 was optimal, as was the prioritization of health-care workers and other essential workers over non-essential workers. Finally, a higher prioritization of individuals with comorbidities in all age groups improved outcomes compared to the CDC allocation. Interpretation: The developed approach can be used to inform the design of future vaccine allocation strategies in the United States, or adapted for use by other countries seeking to optimize the effectiveness of their vaccine allocation strategies. Funding: The authors received no funding for this work.

scholarly journals Hypergraph partitioning for multiple communication cost metrics: Model and methods

2015 ◽  
Vol 77 ◽  
pp. 69-83 ◽  
Author(s):  
Mehmet Deveci ◽  
Kamer Kaya ◽  
Bora Uçar ◽  
Ümit V. Çatalyürek
Keyword(s):  
Communication Cost ◽  
Hypergraph Partitioning ◽  
Cost Metrics ◽  
Metrics Model
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