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Tianqiang Xu

dblp:367/6172 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
0009-0004-5483-7110ORCID · reported

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

Systems, architecture and hardware · 1 · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 since 2021

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
1 paper
Cloud and datacenter computing · 77% Parallel and multicore computing · 12% Memory systems · 12%
Software engineering, system software, and programming languages
1 paper
Operating systems · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Operating systems › virtualization
hypervisor scheduling
0.712023
CPS: A Cooperative Para-virtualized Scheduling Framework for Manycore Machines · ASPLOS (4) 2023
Cloud and datacenter computing
virtualization
0.712023
CPS: A Cooperative Para-virtualized Scheduling Framework for Manycore Machines · ASPLOS (4) 2023
Cloud and datacenter computing › virtualization › virtual machine management
virtual machine scheduling
0.712023
CPS: A Cooperative Para-virtualized Scheduling Framework for Manycore Machines · ASPLOS (4) 2023
Parallel and multicore computing
many-core systems
0.212023
CPS: A Cooperative Para-virtualized Scheduling Framework for Manycore Machines · ASPLOS (4) 2023
Memory systems
non-uniform memory access
0.212023
CPS: A Cooperative Para-virtualized Scheduling Framework for Manycore Machines · ASPLOS (4) 2023

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

paravirtualization · 1.3cooperative scheduling · 1.3
YearPublicationVenuePosition
2023 CPS: A Cooperative Para-virtualized Scheduling Framework for Manycore Machines
abstract
Today's cloud platforms offer large virtual machine (VM) instances with multiple virtual CPUs (vCPU) on manycore machines. These machines typically have a deep memory hierarchy to enhance communication between cores. Although previous researches have primarily focused on addressing the performance scalability issues caused by the double scheduling problem in virtualized environments, they mainly concentrated on solving the preemption problem of synchronization primitives and the traditional NUMA architecture. This paper specifically targets a new aspect of scalability issues caused by the absence of runtime hypervisor-internal states (RHS). We demonstrate two typical RHS problems, namely the invisible pCPU (physical CPU) load and dynamic cache group mapping. These RHS problems result in a collapse in VM performance and low CPU utilization because the guest VM lacks visibility into the latest runtime internal states maintained by the hypervisor, such as pCPU load and vCPU-pCPU mappings. Consequently, the guest VM makes inefficient scheduling decisions.
Yuxuan Liu 0019, Tianqiang Xu, Zeyu Mi, Zhichao Hua 0001, Binyu Zang, Haibo Chen 0001
ASPLOS (4)2