Optimized self-designing key-value storage engine
Abstract
Embodiments of the invention utilize an optimized key-value storage engine to strike the optimal balance between cloud-cost and performance and supports queries, including updates, lookups, range queries, inserts, and read-modify-writes. Cloud cost is manifested in purchasing both storage and processing resources. The improved approach has the ability to self-design and instantiate holistic configurations given a workload, a cloud budget, and optionally performance goals and a set of Service Level Agreement (SLA) specifications. A configuration reflects an optimized storage engine design in terms of, for example, the individual data structures design (in-memory and on-disk) in the engine as well as their algorithms and interactions, a cloud provider, and the exact virtual machines to be used.
- Inventors:
- Issue Date:
- Research Org.:
- Harvard Univ., Cambridge, MA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1987074
- Patent Number(s):
- 11563803
- Application Number:
- 17/523,112
- Assignee:
- President and Fellows of Harvard College (Cambridge, MA)
- DOE Contract Number:
- SC0020200
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 11/10/2021
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Idreos, Stratos, Chatterjee, Subarna, Jagadeesan, Meena, and Qin, Wilson. Optimized self-designing key-value storage engine. United States: N. p., 2023.
Web.
Idreos, Stratos, Chatterjee, Subarna, Jagadeesan, Meena, & Qin, Wilson. Optimized self-designing key-value storage engine. United States.
Idreos, Stratos, Chatterjee, Subarna, Jagadeesan, Meena, and Qin, Wilson. Tue .
"Optimized self-designing key-value storage engine". United States. https://www.osti.gov/servlets/purl/1987074.
@article{osti_1987074,
title = {Optimized self-designing key-value storage engine},
author = {Idreos, Stratos and Chatterjee, Subarna and Jagadeesan, Meena and Qin, Wilson},
abstractNote = {Embodiments of the invention utilize an optimized key-value storage engine to strike the optimal balance between cloud-cost and performance and supports queries, including updates, lookups, range queries, inserts, and read-modify-writes. Cloud cost is manifested in purchasing both storage and processing resources. The improved approach has the ability to self-design and instantiate holistic configurations given a workload, a cloud budget, and optionally performance goals and a set of Service Level Agreement (SLA) specifications. A configuration reflects an optimized storage engine design in terms of, for example, the individual data structures design (in-memory and on-disk) in the engine as well as their algorithms and interactions, a cloud provider, and the exact virtual machines to be used.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2023},
month = {1}
}
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