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Boshi Yu

dblp:261/0678 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2022
0009-0002-2694-810XORCID · corroborated

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

Systems, architecture and hardware · 2 · 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 · 54% Memory systems · 23% Distributed systems · 23%

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

TopicWeightPapersLastEvidence papers
Cloud and datacenter computing › virtualization › virtual machine monitor
distributed hypervisor
0.612022
GiantVM: A Novel Distributed Hypervisor for Resource Aggregation with DSM-aware Optimizations · ACM Trans. Archit. Code Optim. 2022
Memory systems › shared memory
distributed shared memory
0.612022
GiantVM: A Novel Distributed Hypervisor for Resource Aggregation with DSM-aware Optimizations · ACM Trans. Archit. Code Optim. 2022
Distributed systems › distributed resource management
resource aggregation
0.612022
GiantVM: A Novel Distributed Hypervisor for Resource Aggregation with DSM-aware Optimizations · ACM Trans. Archit. Code Optim. 2022
Cloud and datacenter computing
virtualization
0.612022
GiantVM: A Novel Distributed Hypervisor for Resource Aggregation with DSM-aware Optimizations · ACM Trans. Archit. Code Optim. 2022
Cloud and datacenter computing › virtualization
virtual machine migration
0.212022
GiantVM: A Novel Distributed Hypervisor for Resource Aggregation with DSM-aware Optimizations · ACM Trans. Archit. Code Optim. 2022

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

distributed CPU and I/O virtualization · 0.6DSM-aware scheduling · 0.6
YearPublicationVenuePosition
2022 GiantVM: A Novel Distributed Hypervisor for Resource Aggregation with DSM-aware Optimizations
abstract
We present GiantVM, 1 an open-source distributed hypervisor that provides the many-to-one virtualization to aggregate resources from multiple physical machines. We propose techniques to enable distributed CPU and I/O virtualization and distributed shared memory (DSM) to achieve memory aggregation. GiantVM is implemented based on the state-of-the-art type-II hypervisor QEMU-KVM, and it can currently host conventional OSes such as Linux. (1) We identify the performance bottleneck of GiantVM to be DSM, through a top-down performance analysis. Although GiantVM offers great opportunities for CPU-intensive applications to enjoy the aggregated CPU resources, memory-intensive applications could suffer from cross-node page sharing, which requires frequent DSM involvement and leads to performance collapse. We design the guest-level thread scheduler, DaS (DSM-aware Scheduler), to overcome the bottleneck. When benchmarking with NAS Parallel Benchmarks, the DaS could achieve a performance boost of up to 3.5×, compared to the default Linux kernel scheduler. (2) While evaluating DaS, we observe the advantage of GiantVM as a resource reallocation facility. Thanks to the SSI abstraction of GiantVM, migration could be done by guest-level scheduling. DSM allows standby pages in the migration destination, which need not be transferred through the network. The saved network bandwidth is 68% on average, compared to VM live migration. Resource reallocation with GiantVM increases the overall CPU utilization by 14.3% in a co-location experiment.
Xingguo Jia, Boshi Yu, Xingyue Qian, Zhengwei Qi, Haibing Guan
ACM Trans. Archit. Code Optim.3
2020 GiantVM: a type-II hypervisor implementing many-to-one virtualization
abstract
In recent years, since scale-up machines are not economical and may not be affordable for small businesses, scale-out has become the standard answer to data analysis, machine learning, and many other fields. However, these frameworks introduce complex programming models that put a burden on developers. Therefore, Single System Image (SSI), which means a cluster of machines that appears to be one single system, has been proposed to hide the complexity of distributed systems. Unfortunately, due to the mature ecosystem of current mainstream Operating Systems (OSes), it might be non-trivial and even unaffordable to modify the current OS to implement SSI. With the wide use of virtualization, we believe that it is appealing to support SSI at the hypervisor, without modifying guest OSes.
Zhuocheng Ding, Yubin Chen, Xingguo Jia, Boshi Yu, Zhengwei Qi, Haibing Guan
VEE5