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Record W112982782

Storage management for large scale systems

2004· article· en· W112982782 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueUniversity Library - University of Saskatchewan (University of Saskatchewan) · 2004
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Storage Technologies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsComputer scienceCacheDisk bufferWrite bufferPage cacheCache algorithmsCache-oblivious algorithmPage faultCache coloringCache pollutionLock (firearm)Overhead (engineering)Cache invalidationParallel computingOperating systemCPU cacheDistributed computingMemory managementVirtual memory
DOInot available

Abstract

fetched live from OpenAlex

Because of the slow access time of disk storage, storage management is crucial to the performance of many large scale computer systems.This thesis studies performance issues in buffer cache management and disk layout management, two important components of storage management.The buffer cache stores popular disk pages in memory to speed up the access to them.Buffer cache management algorithms used in real systems often have many parameters that require careful hand-tuning to get good performance.A self-tuning algorithm is proposed to automatically tune the page cleaning activity in the buffer cache management algorithm by monitoring the I/O activities of the buffer cache.This algorithm achieves performance comparable to the best manually tuned system.The global data structure used by the buffer cache management algorithm is protected by a lock.Access to this lock can cause contention which can significantly reduce system throughput in multi-processor systems.Current solutions to eliminate lock contention decrease the hit ratio of the buffer cache, which causes poor performance when the system is I/O-bound.A new approach, called the multi-region cache, is proposed.This approach eliminates lock contention, maintains the hit ratio of the buffer cache, and incurs little overhead.Moreover, this approach can be applied to most buffer cache management algorithms.Disk layout management arranges the layout of pages on disks to improve the disk I/O efficiency.The typical disk layout approach, called Overwrite, is optimized for sequential I/Os from a single file.Interleaved writes from multiple users can significantly decrease system throughput in large scale systems using Overwrite.Although the Log-structured File System (LFS) is optimized for such workloads, its garbage collection overhead can be expensive.In modern and future disks, because of the much faster improvement of disk transfer bandwidth over disk positioning time, LFS performs much better than Overwrite in most workloads, unless the disk is close to full.A new disk layout approach, called HyLog, is proposed.HyLog achieves performance comparable to the best of existing disk layout approaches in most cases.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.543
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.005
Open science0.0050.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.170
Teacher spread0.163 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it