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Michael Hao Tong

dblp:197/7183 · DBLP profile ↗
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5ranked-venue papers
1as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 4 · 1 first-author · 1 since 2021Databases, data management, data science and information retrieval · 2Software engineering, systems software and programming languages · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
5 papers
Storage systems · 57% Cloud and datacenter computing · 32% Electronic design automation · 9%
Software engineering, system software, and programming languages
1 paper
Operating systems · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 11 heaviest of 13, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cloud and datacenter computing › quality of service
tail latency
0.732017
MittOS: Supporting Millisecond Tail Tolerance with Fast Rejecting SLO-Aware OS Interface · SOSP 2017
Tiny-Tail Flash: Near-Perfect Elimination of Garbage Collection Tail Latencies in NAND SSDs · FAST 2017
Tiny-Tail Flash: Near-Perfect Elimination of Garbage Collection Tail Latencies in NAND SSDs · ACM Trans. Storage 2017
Storage systems › flash and SSD › flash memory management
garbage collection
0.622017
Tiny-Tail Flash: Near-Perfect Elimination of Garbage Collection Tail Latencies in NAND SSDs · ACM Trans. Storage 2017
Tiny-Tail Flash: Near-Perfect Elimination of Garbage Collection Tail Latencies in NAND SSDs · FAST 2017
Storage systems › flash and SSD › flash memory
flash storage
0.312018
The CASE of FEMU: Cheap, Accurate, Scalable and Extensible Flash Emulator · FAST 2018
Electronic design automation › hardware verification and test › functional verification › emulation
storage emulation
0.312018
The CASE of FEMU: Cheap, Accurate, Scalable and Extensible Flash Emulator · FAST 2018
Operating systems › i/o › i/o subsystem
i/o scheduling
0.312017
MittOS: Supporting Millisecond Tail Tolerance with Fast Rejecting SLO-Aware OS Interface · SOSP 2017
Storage systems
flash and SSD
0.312017
Tiny-Tail Flash: Near-Perfect Elimination of Garbage Collection Tail Latencies in NAND SSDs · FAST 2017
Storage systems › flash and SSD › flash memory management
flash translation layer
0.312017
Tiny-Tail Flash: Near-Perfect Elimination of Garbage Collection Tail Latencies in NAND SSDs · ACM Trans. Storage 2017
Storage systems › i/o architecture › i/o subsystem
i/o stack
0.312017
MittOS: Supporting Millisecond Tail Tolerance with Fast Rejecting SLO-Aware OS Interface · SOSP 2017
Storage systems › flash and SSD
solid-state drive
0.312017
Tiny-Tail Flash: Near-Perfect Elimination of Garbage Collection Tail Latencies in NAND SSDs · ACM Trans. Storage 2017
Bioinformatics and computational biology
genomics
0.112021
Experiences in Managing the Performance and Reliability of a Large-Scale Genomics Cloud Platform · USENIX ATC 2021
Parallel and multicore computing › data parallelism
data-parallel applications
0.112017
MittOS: Supporting Millisecond Tail Tolerance with Fast Rejecting SLO-Aware OS Interface · SOSP 2017

Methods — techniques the papers use, named apart from their topics

fast rejection · 0.6SLO prediction · 0.6rotating GC · 0.3plane-blocking GC · 0.3GC-tolerant read · 0.3GC-tolerant flush · 0.3
YearPublicationVenuePosition
2021 Experiences in Managing the Performance and Reliability of a Large-Scale Genomics Cloud Platform
Michael Hao Tong, Robert L. Grossman, Haryadi S. Gunawi
USENIX ATC1
2018 The CASE of FEMU: Cheap, Accurate, Scalable and Extensible Flash Emulator
Huaicheng Li, Mingzhe Hao, Michael Hao Tong, Swaminathan Sundararaman, Matias Bjørling, Haryadi S. Gunawi
FAST3
2017 Tiny-Tail Flash: Near-Perfect Elimination of Garbage Collection Tail Latencies in NAND SSDs
Shiqin Yan, Huaicheng Li, Mingzhe Hao, Michael Hao Tong, Swaminathan Sundararaman, Andrew A. Chien, Haryadi S. Gunawi
FAST4
2017 MittOS: Supporting Millisecond Tail Tolerance with Fast Rejecting SLO-Aware OS Interface
abstract
MittOS provides operating system support to cut millisecond-level tail latencies for data-parallel applications. In MittOS, we advocate a new principle that operating system should quickly reject IOs that cannot be promptly served. To achieve this, MittOS exposes a fast rejecting SLO-aware interface wherein applications can provide their SLOs (e.g., IO deadlines). If MittOS predicts that the IO SLOs cannot be met, MittOS will promptly return EBUSY signal, allowing the application to failover (retry) to another less-busy node without waiting. We build MittOS within the storage stack (disk, SSD, and OS cache managements), but the principle is extensible to CPU and runtime memory managements as well. MittOS' no-wait approach helps reduce IO completion time up to 35% compared to wait-then-speculate approaches.
Mingzhe Hao, Huaicheng Li, Michael Hao Tong, Chrisma Pakha, Riza O. Suminto, Cesar A. Stuardo, Andrew A. Chien, Haryadi S. Gunawi
SOSP3
2017 Tiny-Tail Flash: Near-Perfect Elimination of Garbage Collection Tail Latencies in NAND SSDs
abstract
Flash storage has become the mainstream destination for storage users. However, SSDs do not always deliver the performance that users expect. The core culprit of flash performance instability is the well-known garbage collection (GC) process, which causes long delays as the SSD cannot serve (blocks) incoming I/Os, which then induces the long tail latency problem. We present tt F lash as a solution to this problem. tt F lash is a “tiny-tail” flash drive (SSD) that eliminates GC-induced tail latencies by circumventing GC-blocked I/Os with four novel strategies: plane-blocking GC, rotating GC, GC-tolerant read, and GC-tolerant flush. These four strategies leverage the timely combination of modern SSD internal technologies such as powerful controllers, parity-based redundancies, and capacitor-backed RAM. Our strategies are dependent on the use of intra-plane copyback operations. Through an extensive evaluation, we show that tt F lash comes significantly close to a “no-GC” scenario. Specifically, between the 99 and 99.99th percentiles, tt F lash is only 1.0 to 2.6× slower than the no-GC case, while a base approach suffers from 5–138× GC-induced slowdowns.
Shiqin Yan, Huaicheng Li, Mingzhe Hao, Michael Hao Tong, Swaminathan Sundararaman, Andrew A. Chien, Haryadi S. Gunawi
ACM Trans. Storage4