Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Balvansh Heerekar

dblp:346/2780 · DBLP profile ↗
← Back
2ranked-venue papers
1as first author
2since 2021 · last 2024
0009-0005-0696-2694ORCID · corroborated

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

Systems, architecture and hardware · 2 · 1 first-author · 2 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 · 100%
Software engineering, system software, and programming languages
1 paper
Operating systems · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cloud and datacenter computing › resource management
datacenter resource management
0.712023
Aggregate VM: Why Reduce or Evict VM's Resources When You Can Borrow Them From Other Nodes? · EuroSys 2023
Cloud and datacenter computing › virtualization › virtual machine monitor
distributed hypervisor
0.712023
Aggregate VM: Why Reduce or Evict VM's Resources When You Can Borrow Them From Other Nodes? · EuroSys 2023
Cloud and datacenter computing › resource management
resource fragmentation
0.712023
Aggregate VM: Why Reduce or Evict VM's Resources When You Can Borrow Them From Other Nodes? · EuroSys 2023

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

resource-borrowing hypervisor · 1.3distributed VM aggregation · 1.3
YearPublicationVenuePosition
2024 Offloading Datacenter Jobs to RISC-V Hardware for Improved Performance and Power Efficiency
abstract
The end of Moore's Law has brought significant changes in the architecture of servers used in data centers, increasingly incorporating new ISAs beyond x86-64 as well as diverse accelerators. Further, single-board computers have become increasingly efficient and can run certain Linux applications at significantly lower equipment and energy costs compared to traditional servers. Past research has demonstrated that offloading applications at runtime from x86-based servers to ARM-based single-board computers can result in increases in throughput and energy efficiency. The RISC-V architecture has recently gained significant commercial interest, and OS-capable single-board computers with RISC-V cores are increasingly available at the commodity scale.
Balvansh Heerekar, Cesar Philippidis, Ho-Ren Chuang, Pierre Olivier, Antonio Barbalace, Binoy Ravindran
SYSTOR1
2023 Aggregate VM: Why Reduce or Evict VM's Resources When You Can Borrow Them From Other Nodes?
abstract
Hardware resource fragmentation is a common issue in data centers. Traditional solutions based on migration or overcommitment are unacceptably slow, and modern commercial or research solutions like Spot VM may reduce or evict VM's resources anytime. We propose an alternative solution that does not suffer from these drawbacks, the Aggregate VM. We introduce a new distributed hypervisor design, the resource-borrowing hypervisor, which creates Aggregate VMs: distributed VMs that temporarily aggregate fragmented resources belonging to different host machines, which require mobility of virtual CPUs, memory and IO devices. We implement a prototype, FragVisor, which runs guest software transparently. We also propose minimal modifications to the guest OS that can enable significant performance gains. We evaluate FragVisor over a set of microbenchmarks and IaaS-style real applications. Although Aggregate VMs are not a perfect fit for every type of applications, some workloads enjoy significant speedups compared to overcommitted scenarios (up to 3.9x with 4 distributed vCPUs). We further demonstrate that FragVisor is faster than a state-of-the-art competitor, GiantVM (up to 2.5x).
Ho-Ren Chuang, Karim Manaouil, Tong Xing 0002, Antonio Barbalace, Pierre Olivier, Balvansh Heerekar, Binoy Ravindran
EuroSys6