Optimizing hyperspace hashing via analytical modelling and adaptation

Diegues, Nuno; Orazov, Muhammet; Paiva, J.P. Sucena; Rodrigues, L.; Romano, Paolo

doi:10.1145/2656864.2656866

Cited by 5 publications

(4 citation statements)

References 27 publications

(15 reference statements)

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“…But hyperspace hashing devised in HyperDex [1] can better balance the maintenance cost and query efficiency as an embedded secondary index. Nuno Diegues et al [4] explore a performance model and propose a method for adaptively selecting best solutions based on changing workloads. But it is also designed for the distributed implementation.…”

Section: Hyperspace Hasingmentioning

confidence: 99%

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GPU-Based Dynamic Hyperspace Hash with Full Concurrency

Ren

et al. 2021

Data Sci. Eng.

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Hyperspace hashing which is often applied to NoSQL data-bases builds indexes by mapping objects with multiple attributes to a multidimensional space. It can accelerate processing queries of some secondary attributes in addition to just primary keys. In recent years, the rich computing resources of GPU provide opportunities for implementing high-performance HyperSpace Hash. In this study, we construct a fully concurrent dynamic hyperspace hash table for GPU. By using atomic operations instead of locking, we make our approach highly parallel and lock-free. We propose a special concurrency control strategy that ensures wait-free read operations. Our data structure is designed considering GPU specific hardware characteristics. We also propose a warp-level pre-combinations data sharing strategy to obtain high parallel acceleration. Experiments on an Nvidia RTX2080Ti GPU suggest that GHSH performs about 20-100X faster than its counterpart on CPU. Specifically, GHSH performs updates with up to 396 M updates/s and processes search queries with up to 995 M queries/s. Compared to other GPU hashes that cannot conduct queries on non-key attributes, GHSH demonstrates comparable building and retrieval performance.

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Section: Hyperspace Hasingmentioning

confidence: 99%

“…So one can find "John Smith" by looking up the number of John Smith in the hash bucket that intersects this line. Nuno Diegues et al [4] explore a performance model and propose a method for adaptively selecting the best solutions based on changing workloads. But it is also designed for the distributed implementation.…”

Section: Introductionmentioning

confidence: 99%

GPU-Based Dynamic Hyperspace Hash with Full Concurrency

Ren

et al. 2021

Data Sci. Eng.

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“…Few studies consider vertical scaling, e.g. [6,18], and configuration tuning [6,12,22,28]. While Horizontal scaling, vertical scaling and configutation tuning approaches are somentime mixed, optimal placement (e.g.…”

Section: Related Workmentioning

confidence: 99%

“…Their goal is to minimize the cost of replicas in term of overall latency. In [12] the authors propose a multidimensional indexing techniques for supporting complex queries using multiple object attributes. Such technique requires a complex system configuration and the authors propose a model and techniques to automatically and dynamically reconfigure the system in dynamic workload environments.…”

Section: Related Workmentioning

confidence: 99%

Energy-aware auto-scaling algorithms for Cassandra virtual data centers

2017

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Apache Cassandra is an highly scalable and available NoSql datastore, largely used by enterprises of each size and for application areas that range from entertainment to big data analytics. Managed Cassandra service providers are emerging to hide the complexity of the installation, fine tuning and operation of Cassandra virtual data centers (VDCs). This paper address the problem of energy efficient autoscaling of Cassandra VDC in managed Cassandra data centers. We propose three energy-aware autoscaling algorithms: Opt, LocalOpt and LocalOpt-H. The first provides the optimal scaling decision orchestrating horizontal and vertical scaling and optimal placement. The other two are heuristics and provide sub-optimal solutions. Both orchestrate horizontal scaling and optimal placement. LocalOpt consider also vertical scaling. In this paper: we provide an analysis of the computational complexity of the optimal and of the heuristic auto-scaling algorithms; we discuss the issues in auto-scaling Cassandra VDC and we provide best practice for using auto-scaling algorithms; we evaluate the performance of the proposed algorithms under programmed SLA variation, surge of throughput (unexpected) and failures of physical nodes. We also compare the performance of energyaware auto-scaling algorithms with the performance of two energy-blind auto-scaling algorithms, namely BestFit and BestFit-H. at reducing the energy consumption or resource usage in general can heavily reduce the reliability of Cassandra in term of the consistency level offered. Horizontal scaling of Cassandra is very slow and make hard to manage surge of throughput. Vertical scaling is a valid alternative, but it is not supported by all the cloud infrastructures.

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GHSH: Dynamic Hyperspace Hashing on GPU

Ren

et al. 2020

Web and Big Data

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Optimizing hyperspace hashing via analytical modelling and adaptation

Cited by 5 publications

References 27 publications

GPU-Based Dynamic Hyperspace Hash with Full Concurrency

GPU-Based Dynamic Hyperspace Hash with Full Concurrency

Energy-aware auto-scaling algorithms for Cassandra virtual data centers

GHSH: Dynamic Hyperspace Hashing on GPU

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