LSM-Forest: Planting Workload-optimized LSM-trees
- Title
- LSM-Forest: Planting Workload-optimized LSM-trees
- Author(s)
- Woojin Cho
- DGIST Authors
- Woojin Cho ; Sungjin Lee ; Yongjune Kim
- Advisor
- 이성진
- Co-Advisor(s)
- Yongjune Kim
- Issued Date
- 2022
- Awarded Date
- 2022/02
- Type
- Thesis
- Description
- Big Data, LSM-tree, Database, Computer System, 빅데이터, 데이터베이스, 컴퓨터 시스템
- Table Of Contents
-
I. Introduction 1
II. Background & Related Work 4
2.1 Background 4
2.1.1 LSM Tree Key-Value Store 4
2.1.2 Bloom Filter 5
2.1.3 MyRocks 5
2.1.4 Column Family & Multi Tenant 6
2.2 Related Work 7
2.2.1 Iso-KVSSD 7
2.2.2 Kevin 7
2.2.3 Parameter Tuning Using Deep Learning 8
III. Motivation 9
3.1 Problem of the Existing Shared Global LSM-tree 10
3.2 Problems with Existing Parameter Configuration 11
IV. LSM Forest 12
4.1 How to Planting LSM Tree 12
4.1.1 Target Application 13
4.1.2 Per Table Allocation LSM-tree 14
4.1.3 How to optimally allocate LSM-tree for each workload pattern 15
4.2 How to Gardening LSM Tree 16
4.2.1 Configure parameters according to workload characteristics 16
4.2.2 Resource allocate for each LSM-tree according to workload characteristics 19
V. Evaluation 27
5.1 Experimental Setup 27
5.1.1 Evaluation Setting 27
5.1.2 Comparison of each optimization 28
VI. Conclusions 30
References 31
- URI
-
http://dgist.dcollection.net/common/orgView/200000593310
http://hdl.handle.net/20.500.11750/16301
- Degree
- Master
- Department
- Information and Communication Engineering
- Publisher
- DGIST
- Related Researcher
-
-
Lee, Sungjin
- Research Interests Computer System; System Software; Storage System; Non-volatile Memory; Flash-based SSD; Distributed Storage Systems
-
- Files in This Item:
-
There are no files associated with this item.
- Appears in Collections:
- Department of Electrical Engineering and Computer Science Theses Master
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.