논문 스터디8 [SC'21] DeltaFS: A Scalable No-Ground-Truth Filesystem For Massively-Parallel Computing [SC'21] DeltaFS: A Scalable No-Ground-Truth Filesystem For Massively-Parallel Computing 1. Motivations Global synchronization Today's filesystem clients tend to synchronize too frequently with their servers. The inadequacy of the current state-of-art Today's filesystems map all application jobs to a single filesystem namespace. Filesystem metadata performance is limited by the amount.. 2022. 9. 6. [HotStorage'22] Lifetime-leveling LSM-tree compaction for ZNS SSD [HotStorage'22] Lifetime-leveling LSM-tree compaction for ZNS SSD 1. Motivations Problems from compaction in LSM-based KV stores with ZNS SSD Space amplification (Long-lived SSTs) Write amplification (Short-lived SSTs) 2. LL (Lifetime leveling) compaction algorithm Key principles Allocates dedicated zones for each level (For avoiding long-lived SSTs) Each compaction must involve all the lowe.. 2022. 7. 25. [FAST'19] SLM-DB: Single-Level Key-Value Store with Persistent Memory [FAST'19] SLM-DB: Single-Level Key-Value Store with Persistent Memory 1. Motivations LSM-tree based KV stores are optimized to support write intensive workloads It has high R/W amplification and low read performance Recently, typical workloads has changed to have similar proportions of R/W KV stores need to be optimized for both read and write workloads 2. Optimization point Improve read perfoma.. 2022. 7. 18. [HotStorage'22] Compaction-Aware Zone Allocation for LSM based Key-Value Store on ZNS SSDs [HotStorage'22] Compaction-Aware Zone Allocation for LSM based Key-Value Store on ZNS SSDs 1. Motivations ZNS SSD Space management is moved from device to host Thus, application must perform zone cleaning Write amplification: When cleaning a zone, if valid data remains, this data should be copied to other zone To reduce WA, data with the same lifetime should be written in the same zone LIZA(LIfe.. 2022. 7. 18. [ATC'18] Redesigning LSMs for Nonvolatile Memory with NoveLSM [ATC'18] Redesigning LSMs for Nonvolatile Memory with NoveLSM 1. Critical problems on LSM when we use NVM Different in-memory and storage(persistent) form of the data High (de)serialization cost Only in-memory data can be changed. storage data is immutable. Limited memory capacity leads to frequent compaction, which increases stall time Becuase memory is volatile, updates must be logged. This al.. 2022. 7. 4. [ATC'20] MatrixKV: Reducing Write Stalls and Write Amplification in LSM-tree Based KV Stores with Matrix Container in NVM [ATC'20] MatrixKV: Reducing Write Stalls and Write Amplification in LSM-tree Based KV Stores with Matrix Container in NVM 1. Challenges on LSM-tree based KV stores Write stalls LSM구조에서 L0레벨은 DRAM에서 flush된 Memtable이 그대로 저장되기 때문에 정렬이 되어 있지 않다. Compaction은 merge sort 방식으로 하는데, L0이 정렬된 상태가 아니기 때문에 여기서 오는 overheadr가 크다. Write amplification WA = n*AF (n: # of levels, AF: Amplification factor) 2. Previ.. 2022. 6. 30. [ASPLOS'19] Nimble Page Management for Tiered Memory Systems [ASPLOS'19] Nimble Page Management for Tiered Memory Systems Ref: https://dl.acm.org/doi/10.1145/3297858.3304024 1. Introduction 현대의 processing, memory system에서 hetrogeneity가 많아지는 추세 효율적인 자원의 관리가 필요 hot data는 fast memory, cold data는 slow memory에 배치 이 hot, cold data migration의 오버헤드가 너무 커서 hardware의 banwidth에 한참 미치지 못한다 이 논문에서 제시하는 4가지 optimziation Huge page migration Parallelized data copy Concur.. 2022. 1. 14. [SOSP'21] HeMem: Scalable Tiered Memory Management for Big Data Applications and Real NVM [SOSP'21] HeMem: Scalable Tiered Memory Management for Big Data Applications and Real NVM Ref: https://dl.acm.org/doi/10.1145/3477132.3483550 2.Background 2.1 Application Memory Demands 현대의 application들은 data-intensive ex) web, machine learning, in-memory DB... Requirements large memory capacity, bandwidth low memory access tail-latency 위에서 언급한 application들의 memory lifetime 특성은 allocation size와 .. 2022. 1. 5. 이전 1 다음