Evaluation and Analysis of In-Memory Key-Value Systems

Cao, Wenqi; Sahin, Semih; Liu, Ling; Bao, Xianqiang

doi:10.1109/bigdatacongress.2016.13

Cited by 12 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Performance refers to the latency to serve memory requests. As existing benchmarks do not support data structures besides to String [25], we extend the memtier_benchmark tool to support them.…”

Section: Performance and Energy Resultsmentioning

confidence: 99%

Application-Oriented Data Migration to Accelerate In-Memory Database on Hybrid Memory

Zhao

Zhang

et al. 2021

Micromachines

View full text Add to dashboard Cite

With the advantage of faster data access than traditional disks, in-memory database systems, such as Redis and Memcached, have been widely applied in data centers and embedded systems. The performance of in-memory database greatly depends on the access speed of memory. With the requirement of high bandwidth and low energy, die-stacked memory (e.g., High Bandwidth Memory (HBM)) has been developed to extend the channel number and width. However, the capacity of die-stacked memory is limited due to the interposer challenge. Thus, hybrid memory system with traditional Dynamic Random Access Memory (DRAM) and die-stacked memory emerges. Existing works have proposed to place and manage data on hybrid memory architecture in the view of hardware. This paper considers to manage in-memory database data in hybrid memory in the view of application. We first perform a preliminary study on the hotness distribution of client requests on Redis. From the results, we observe that most requests happen on a small portion of data objects in in-memory database. Then, we propose the Application-oriented Data Migration called ADM to accelerate in-memory database on hybrid memory. We design a hotness management method and two migration policies to migrate data into or out of HBM. We take Redis under comprehensive benchmarks as a case study for the proposed method. Through the experimental results, it is verified that our proposed method can effectively gain performance improvement and reduce energy consumption compared with existing Redis database.

show abstract

Section: Performance and Energy Resultsmentioning

confidence: 99%

Application-Oriented Data Migration to Accelerate In-Memory Database on Hybrid Memory

Zhao

Zhang

et al. 2021

Micromachines

View full text Add to dashboard Cite

show abstract

“…Cao et al assessed the key‐value store performance analysis too, but they used Memcached and Redis unlike Hazelcast. They focus only on the resource overhead unlike our proposal of service modeling and performance optimization schemes.…”

Section: Related Workmentioning

confidence: 99%

KeyValueServe^†: Design and performance analysis of a multi‐tenant data grid as a cloud service

Das

Iyengar

Mueller

2018

Concurrency and Computation

View full text Add to dashboard Cite

Distributed key-value stores have become indispensable for large-scale cluster applications.Many cloud services have deployed in-memory data grids for their enterprise infrastructures and support multi-tenancy services. However, most services do not offer fine-grained multi-tenant resource sharing. To this front, we present KeyValueServe, a low overhead cloud service with features aiding resource management. Results based on Hazelcast, a popular open source data grid, indicate that KeyValueServe can efficiently provide services to tenants without degrading performance. Providing consistent performance to all tenants for fluctuating workloads is still difficult.Performance problems occur at scale with diverse tenant requirements. To address this, the paper provides insights to contention and performance bottlenecks. Through experimental analysis, we uncover scenarios of performance degradation and demonstrate optimized performance via coalescing multiple clients' requests. Our work indicates that a Hazelcast cluster can get congested with multiple concurrent connections when processing client requests, resulting in poor performance. KeyValueServe can reduce the number of parallel connections maintained for client requests, resulting in improved performance.

show abstract

“…Cache memories are small indexes-smaller than the local index stored in each search nodecontaining processed queries with its top-k results. There are some caching tools like Memcached 1 and Redis, 2 , but these tools are not designed for metric spaces [12,31]. These are NoSQL databases that use key-value data structures in RAM.…”

Section: Introductionmentioning

confidence: 99%

A graph-based cache for large-scale similarity search engines

2017

View full text Add to dashboard Cite

Large-scale similarity search engines are complex systems devised to process unstructured data like images and videos. These systems are deployed on clusters of distributed processors communicated through high-speed networks. To process a new query, a distance function is evaluated between the query and the objects stored in the database. This process relays on a metric space index distributed among the processors. In this paper, we propose a cache-based strategy devised to reduce the number of computations required to retrieve the top-k object results for user queries by using pre-computed information. Our proposal executes an approximate similarity search algorithm, which takes advantage of the links between objects stored in the cache memory. Those links form a graph of similarity among pre-computed queries. Compared to the previous methods in the literature, the proposed approach reduces the number of distance evaluations up to 60%.

show abstract

Evaluation and Analysis of In-Memory Key-Value Systems

Cited by 12 publications

References 2 publications

Application-Oriented Data Migration to Accelerate In-Memory Database on Hybrid Memory

Application-Oriented Data Migration to Accelerate In-Memory Database on Hybrid Memory

KeyValueServe^†: Design and performance analysis of a multi‐tenant data grid as a cloud service

A graph-based cache for large-scale similarity search engines

Contact Info

Product

Resources

About

Evaluation and Analysis of In-Memory Key-Value Systems

Cited by 12 publications

References 2 publications

Application-Oriented Data Migration to Accelerate In-Memory Database on Hybrid Memory

Application-Oriented Data Migration to Accelerate In-Memory Database on Hybrid Memory

KeyValueServe†: Design and performance analysis of a multi‐tenant data grid as a cloud service

A graph-based cache for large-scale similarity search engines

Contact Info

Product

Resources

About

KeyValueServe^†: Design and performance analysis of a multi‐tenant data grid as a cloud service