diff --git a/README.md b/README.md
index 9dab2e6..bea343c 100644
--- a/README.md
+++ b/README.md
@@ -56,8 +56,6 @@ A reading list related to storage systems, including data deduplication, erasure
22. *The Dilemma between Deduplication and Locality: Can Both be Achieved?*---FAST'21 ([link](https://www.usenix.org/system/files/fast21-zou.pdf)) [summary](https://yzr95924.github.io/paper_summary/MFDedup-FAST'21.html)
23. *SLIMSTORE: A Cloud-based Deduplication System for Multi-version Backups*----ICDE'21 ([link](http://www.cs.utah.edu/~lifeifei/papers/slimstore-icde21.pdf))
24. *Improving the Performance of Deduplication-Based Backup Systems via Container Utilization Based Hot Fingerprint Entry Distilling*----ACM TOS'21 ([link](https://dl.acm.org/doi/full/10.1145/3459626))
-25. *DedupSearch: Two-Phase Deduplication Aware Keyword Search*----FAST'22 ([link](https://www.usenix.org/system/files/fast22-elias.pdf)) [summary](https://yzr95924.github.io/paper_summary/DedupSearch-FAST'22.html)
-26. *Physical vs. Logical Indexing with IDEA: Inverted Deduplication-Aware Index*----FAST'24 ([link](https://www.usenix.org/system/files/fast24-levi.pdf))
### Restore Performances
@@ -135,7 +133,7 @@ A reading list related to storage systems, including data deduplication, erasure
1. *Data Domain Cloud Tier: Backup here, Backup there, Deduplicated Everywhere!*----USENIX ATC'19 ([link](https://www.usenix.org/system/files/atc19-duggal.pdf)) [summary]( https://yzr95924.github.io/paper_summary/CloudTier-ATC'19.html )
2. *InftyDedup: Scalable and Cost-Effective Cloud Tiering with Deduplication*----FAST'23 ([link](https://www.usenix.org/system/files/fast23-kotlarska.pdf)) [summary](https://yzr95924.github.io/paper_summary/InftyDedup-FAST'23.html)
-### Post-Deduplication: Data Compression and Delta Compression
+### Post-Deduplication: Data Compression, Delta Compression, and Application
1. *Redundancy Elimination Within Large Collections of Files*----USENIX ATC'04 ([link](https://www.usenix.org/legacy/publications/library/proceedings/usenix04/tech/general/full_papers/kulkarni/kulkarni.pdf))
2. *The Design of a Similarity Based Deduplication System*----SYSTOR'09 ([link](https://dl.acm.org/doi/pdf/10.1145/1534530.1534539))
3. *Delta Compressed and Deduplicated Storage Using Stream-Informed Locality*----HotStorage'12 ([link](https://www.usenix.org/system/files/conference/hotstorage12/hotstorage12-final38_0.pdf)) [summary](https://yzr95924.github.io/paper_summary/deltaStore-HotStorage'12.html)
@@ -158,6 +156,8 @@ A reading list related to storage systems, including data deduplication, erasure
20. *Donag: Generating Eficient Patches and Difs for Compressed Archives*----ACM TOS'22 ([link](https://dl.acm.org/doi/pdf/10.1145/3507919))
21. *LoopDelta: Embedding Locality-aware Opportunistic Delta Compression in Inline Deduplication for Highly Efficient Data Reduction*----USENIX ATC'23 ([link](https://www.usenix.org/system/files/atc23-zhang-yucheng.pdf))
22. *Palantir: Hierarchical Similarity Detection for Post-Deduplication Delta Compression*----ASPLOS'24 ([link](https://qiangsu97.github.io/files/asplos24spring-final6.pdf))
+23. *DedupSearch: Two-Phase Deduplication Aware Keyword Search*----FAST'22 ([link](https://www.usenix.org/system/files/fast22-elias.pdf)) [summary](https://yzr95924.github.io/paper_summary/DedupSearch-FAST'22.html)
+24. *Physical vs. Logical Indexing with IDEA: Inverted Deduplication-Aware Index*----FAST'24 ([link](https://www.usenix.org/system/files/fast24-levi.pdf)) [summary](https://yzr95924.github.io/paper_summary/IDEA-FAST'24.html)
### Memory && Block-Layer Deduplication
@@ -517,7 +517,7 @@ A reading list related to storage systems, including data deduplication, erasure
### HPC Storage
1. *GPFS: A Shared-Disk File System for Large Computing Clusters*----FAST'02 ([link](https://www.usenix.org/legacy/publications/library/proceedings/fast02/full_papers/schmuck/schmuck.pdf))
-2. *Efficient Object Storage Journaling in a Distributed Parallel File System*----FAST'10 ([link](Efficient Object Storage Journaling in a Distributed Parallel File System))
+2. *Efficient Object Storage Journaling in a Distributed Parallel File System*----FAST'10 ([link](https://www.usenix.org/legacy/events/fast10/tech/full_papers/oral.pdf))
3. *Taking back control of HPC file systems with Robinhood Policy Engine*----arxiv'15 ([link](https://arxiv.org/abs/1505.01448))
4. *Lustre Lockahead: Early Experience and Performance using Optimized Locking*----CUG'17 ([link](https://cug.org/proceedings/cug2017_proceedings/includes/files/pap141s2-file1.pdf))
5. *LPCC: Hierarchical Persistent Client Caching for Lustre*----SC'19 ([link](https://dl.acm.org/doi/pdf/10.1145/3295500.3356139)) [slides](https://sc19.supercomputing.org/proceedings/tech_paper/tech_paper_files/pap112s5.pdf)
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diff --git a/storage_paper_note/IDEA-FAST'24.md b/storage_paper_note/IDEA-FAST'24.md
new file mode 100644
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@@ -0,0 +1,195 @@
+---
+typora-copy-images-to: ../paper_figure
+---
+# Physical vs. Logical Indexing with IDEA: Inverted Deduplication-Aware Index
+
+| Venue | Category |
+| :------------------------: | :------------------: |
+| FAST'24 | Deduplicated System Design, Post-Deduplication Management |
+[TOC]
+
+## 1. Summary
+### Motivation of this paper
+
+- motivation
+ - indexing deduplicated data might result in extreme inefficiencies
+ - index size
+ - proportion to the logical data size, **regardless of its deduplication ratio**
+ - each term must point to all the files containing it, **even if the files' content is almost identical**
+ - index creation overhead
+ - random and redundant accesses to the physical chunks
+ - **term indexing** is not supported by any deduplicating storage system
+ - focus on **textual data**
+ - VMware vSphere and Commvault only support file indexing
+ - identifies individual files within a backup based on metadata
+ - Dell-EMC Data Protection Search
+ - support full content indexing
+ - warn: processing the full content of a large number of files can be **time consuming**
+ - recommend performing targeted indexing on **specific backups and file types**
+- challenge
+ - two separate trends
+ - the growing need to process **cold data** (e.g., old backups)
+ - e.g., full-system scans, keyword searches --> deduplication-aware search
+ - the growing application of deduplication on primary storage of hot and warm data
+ - e.g., perform single-term searches for files within deduplicated personal workstation
+ - indexing software on file-system level --> **unaware** of the underlying deduplication at the storage system
+ - index size
+ - increase --> increase the latency of lookups
+ - index time
+ - scan all files in the system --> random IOs, high read amplification
+ - split terms
+ - chunking process will likely split the incoming data into chunks (at **arbitrary position**)
+ - splitting words between adjacent chunks
+
+### IDEA
+
+- 
+
+- key idea
+ - map terms to the unique physical chunks they appear in
+ - instead of the logical documents (disproportionately high)
+ - replace term-to-file mapping with
+ - term-to-chunk map
+ - chunk-to-file map (file ID)
+ - only need to modify chunking process in deduplication system
+ - **white-space aware** --> enforce chunk boundaries only between words
+- white-space aligned chunking
+ - content-defined chunking
+ - **continue scanning** the following characters until a white-space character is encountered
+ - fixed-size chunking
+ - **backward scanning** this chunk until a white-space character is encountered
+ - resulting chunks are always smaller than the fixed size --> can be stored in a single block
+ - can trim the block in memory to chunk boundary
+ - non-textual content
+ - only to chunking of **textual content**
+ - identify textual content by the file extension of the incoming data
+ - .c, .h, and .htm
+ - add a Boolean field to the metadata of each chunk in the file recipe and container
+ - only process chunks marked as textual
+- term-to-chunk mapping
+ - number of documents in the index --> number of physical chunks
+ - might be higher than the number of logical files
+ - chunks are **read sequentially**, each chunk is processed only once
+ - processing chunks is easily parallelizable
+
+ - lookup
+ - return the fingerprints of the chunks this term appears
+
+- chunk-to-file mapping
+ - two complementing maps
+ - chunk-to-file map
+ - chunk fingerprint --> file IDs
+ - file-to-path map
+ - file IDs --> file's full pathname
+ - created from the metadata in the file recipe
+
+- keyword/term lookup
+ - step-1: yield the fingerprints of all the relevant chunks
+ - step-2: a series of lookups in the chunk-to-file map
+ - retrieves the IDs of all files containing these chunks
+ - step-3: a lookup of each file ID in the file-to-path map
+ - returns the final list of file names
+- ranking results
+ - extend IDEA to support document ranking with the TF-IDF metric
+
+### Implementation and Evaluation
+
+- implementation
+ - LucenePlusPlus + Destor
+ - use Lucene term-to-doc map
+ - 
+ - scan all file receipes from Destor
+ - create the list of files containing each chunk using a key-value store
+ - use an SSD for the data structures which are external to Lucene
+- experimental setup
+ - trace
+ - 
+
+ - hardware
+ - maps of all index alternatives were stored on a separate HDD
+ - chunk-to-file and file-to-path maps of IDEA were stored on a SSD
+
+- evaluation
+ - baseline
+ - traditional deduplication-oblivious indexing (Naive)
+
+ - indexing time
+ - the reduction is proportional to the **deduplication ratio**
+ - recipe-processing time is negligible compared to the chunk-processing time
+
+ - indexing time of IDEA is shorter than that of Naive by 49% to 76%
+
+ - index size
+ - Naive must record more files for all the terms include in them
+ - IDEA additional information is recorded per chunk, not per term
+
+ - lookup times
+ - is faster than Naive by up to 82%
+ - smaller size of its term-to-doc map
+ - incur shorter lookup latency
+
+ - IDEA overhead
+ - IDEA has no advantage when compared to deduplication-oblivious indexing
+ - additional layer of indirection incurs **non-negligible overheads are masked** where the deduplication ratio is sufficiently high
+
+
+## 2. Strength (Contributions of the paper)
+
+- first design of a deduplication-aware term index
+- implementation of IDEA on Lucene
+ - open-source single-node inverted index used by the Elasticsearch
+- extensive evaluation
+
+## 3. Weakness (Limitations of the paper)
+
+- trace is not very large
+- files containing compressed text (.pdf, .docx)
+ - their textual content can only be processed after the file is opened by a suitable application or converted by a dedicated tool
+ - individual chunks cannot be processed during offline index creation
+
+## 4. Some Insights (Future work)
+
+- deduplication scenarios
+ - backup and archival systems
+ - log-structured manner: chunk --> containers
+ - content-defined chunking
+ - primary (non-backup) storage system and appliances
+ - support direct access to individual chunks
+ - fixed-sized chunking
+ - align the deduplicated chunks with the storage interface
+- deduplication data management
+ - implicit sharing of content between files, complicates the followings: transforms logically-sequential data accesses to random IOs in the underlying physical media
+ - GC
+ - load balancing between volumes
+ - caching
+ - charge-back
+- term indexing: **term-to-file** indexing (map)
+ - 
+ - return the files containing **a keyword** or **term**
+ - search engines, data analytics
+ - searched data might be deduplicated
+ - e.g. Elasticsearch
+ - built on top of the single-node Apache Lucene
+ - based on a hierarchy of skip-lists
+ - other variations
+ - Amazon OpenSearch, IBM Watson
+ - keyword: any searchable strings (natural language words)
+ - query
+ - the list of files containing this keyword
+ - optional: byte offsets in which the term appears
+ - indexing creation
+ - collect the documents
+ - identify the terms within each document
+ - normalize the terms
+ - create the list of documents, and optionally offsets, containing each term
+ - result ranking
+ - using a **scoring formula** on each result
+ - TF-IDF
+ - 
+- deduplication basic
+ - file recipe
+ - a list of chunks' fingerprints, their sizes
+ - restore: locate the chunk by searching in the fingerprint map or cache of its entries
+ - pack the **compressed data** into containers
+- standard storage functionality
+ - can be made more efficient by taking advantage of deduplicated state
diff --git a/storage_paper_note/deduplication/secure_dedup/DUPEFS-FAST'22.md b/storage_paper_note/deduplication/secure_dedup/DUPEFS-FAST'22.md
index 2a8771b..b932811 100644
--- a/storage_paper_note/deduplication/secure_dedup/DUPEFS-FAST'22.md
+++ b/storage_paper_note/deduplication/secure_dedup/DUPEFS-FAST'22.md
@@ -3,8 +3,8 @@ typora-copy-images-to: ../paper_figure
---
DUPEFS: Leaking Data Over the Network With Filesystem Deduplication Side Channels
------------------------------------------
-| Venue | Category |
-| :------------------------: | :------------------: |
+| Venue | Category |
+| :-----: | :------------------: |
| FAST'22 | Secure Deduplication |
[TOC]
diff --git a/storage_paper_note/template.md b/storage_paper_note/template.md
index 1a2857f..93e806b 100644
--- a/storage_paper_note/template.md
+++ b/storage_paper_note/template.md
@@ -1,11 +1,11 @@
---
typora-copy-images-to: ../paper_figure
---
-# Light-Dedup: A Light-weight Inline Deduplication Framework for Non-Volatile Memory File Systems
+# Physical vs. Logical Indexing with IDEA: Inverted Deduplication-Aware Index
| Venue | Category |
| :------------------------: | :------------------: |
-| ATC'18 | LSM+PM |
+| FAST'24 | Deduplicated System Design, post-deduplication application |
[TOC]
## 1. Summary