<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-19T05:17:44Z</responseDate><request verb="GetRecord" identifier="oai:dspace.mit.edu:1721.1/150217" metadataPrefix="dim">https://dspace.mit.edu/server/oai/request</request><GetRecord><record><header><identifier>oai:dspace.mit.edu:1721.1/150217</identifier><datestamp>2023-04-01T03:30:12Z</datestamp><setSpec>com_1721.1_7582</setSpec><setSpec>com_1721.1_7581</setSpec><setSpec>col_1721.1_131022</setSpec></header><metadata><dim:dim xmlns:dim="http://www.dspace.org/xmlns/dspace/dim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.dspace.org/schema/dim.xsd">
   <dim:field mdschema="dc" element="contributor" qualifier="advisor">Arvind</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="author">Chung, Chanwoo</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department">Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="accessioned">2023-03-31T14:40:18Z</dim:field>
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   <dim:field mdschema="dc" element="date" qualifier="issued">2023-02</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="submitted">2023-02-28T14:39:37.455Z</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="uri">https://hdl.handle.net/1721.1/150217</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="orcid">0000-0002-2279-1806</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract">Efficient management of storage is a primary concern in all systems dealing with Big Data. In the modern era, flash-based solid-state drives (SSDs) are widely adopted in computer systems, slowly replacing hard disk drives. As many kinds of data generated and collected these days are not well-structured, a key-value store has become one of the most important building blocks widely used in datacenters thanks to its simple interface. Key-value stores are often used as a internal engine for other databases.&#xd;
&#xd;
This thesis explores whether a modern flash-based solid-state drive (SSD) augmented with near-storage computations can be re-designed to provide cheaper and power-efficient solution to maintaining various key-value services in the cloud. The thesis explores a new type of storage device, called a key-value SSD (KV-SSD), that exposes a key-value interface instead of the legacy block interface to the host machine.&#xd;
&#xd;
The two alternative power- and cost-efficient solutions that can replace existing KVS components are based on KV-SSDs, LightStore and PinK. LightStore is a new storage architecture based on a group of network-attached KV-SSDs without storage host servers. LightStore aims to primarily support large-sized objects and emulates other types of data stores using application-side adapters. Compared to existing storage server-based solutions, LightStore is up to 2.3X space- and 7.4X energy-efficient. PinK is a novel design of an LSM-tree for KV-SSDs with software and hardware techniques that provides bounded tail latency and design flexibility. PinK prototype reduces the read and 99th percentile latency by 22% and improves read throughput by 44% compared to LightStore prototype. The PinK prototype showed 42-73% better latency and 37% better throughput compared to commercial hash-based prototype. A proposed future design based on smart SSDs, a block-based SSD with an accelerator, shows how the smart SSDs can help existing software KVS on hosts. We believe these alternatives to running various types of key-value stores in datacenters would reduce storage management cost drastically.</dim:field>
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   <dim:field mdschema="dc" element="publisher">Massachusetts Institute of Technology</dim:field>
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   <dim:field mdschema="dc" element="rights">Copyright MIT</dim:field>
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   <dim:field mdschema="dc" element="title">Implementing accelerated key-value store: From SSDs to datacenter servers</dim:field>
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   	&lt;Title>Implementing accelerated key-value store: From SSDs to datacenter servers&lt;/Title>
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   	&lt;PublicationDate>2023-02&lt;/PublicationDate>
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        	&lt;DisplayName>Chung, Chanwoo&lt;/DisplayName>
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   	&lt;Abstract>Efficient management of storage is a primary concern in all systems dealing with Big Data. In the modern era, flash-based solid-state drives (SSDs) are widely adopted in computer systems, slowly replacing hard disk drives. As many kinds of data generated and collected these days are not well-structured, a key-value store has become one of the most important building blocks widely used in datacenters thanks to its simple interface. Key-value stores are often used as a internal engine for other databases.&#xd;
&#xd;
This thesis explores whether a modern flash-based solid-state drive (SSD) augmented with near-storage computations can be re-designed to provide cheaper and power-efficient solution to maintaining various key-value services in the cloud. The thesis explores a new type of storage device, called a key-value SSD (KV-SSD), that exposes a key-value interface instead of the legacy block interface to the host machine.&#xd;
&#xd;
The two alternative power- and cost-efficient solutions that can replace existing KVS components are based on KV-SSDs, LightStore and PinK. LightStore is a new storage architecture based on a group of network-attached KV-SSDs without storage host servers. LightStore aims to primarily support large-sized objects and emulates other types of data stores using application-side adapters. Compared to existing storage server-based solutions, LightStore is up to 2.3X space- and 7.4X energy-efficient. PinK is a novel design of an LSM-tree for KV-SSDs with software and hardware techniques that provides bounded tail latency and design flexibility. PinK prototype reduces the read and 99th percentile latency by 22% and improves read throughput by 44% compared to LightStore prototype. The PinK prototype showed 42-73% better latency and 37% better throughput compared to commercial hash-based prototype. A proposed future design based on smart SSDs, a block-based SSD with an accelerator, shows how the smart SSDs can help existing software KVS on hosts. We believe these alternatives to running various types of key-value stores in datacenters would reduce storage management cost drastically.&lt;/Abstract>
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