EDBT 2026 Demo / reviewers in the wild / expert
Feng Qi 0003
dblp:42/1054-3
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0002-0759-5268ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 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
1 paper |
Cloud and datacenter computing · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cloud and datacenter computing › datacenter operations
datacenter resource utilization |
0.4 | 1 | 2019 | Pliant: Leveraging Approximation to Improve Datacenter Resource Efficiency · HPCA 2019 |
Cloud and datacenter computing › multi-tenancy
multi-tenant cloud |
0.1 | 1 | 2019 | Pliant: Leveraging Approximation to Improve Datacenter Resource Efficiency · HPCA 2019 |
Cloud and datacenter computing
quality of service |
0.1 | 1 | 2019 | Pliant: Leveraging Approximation to Improve Datacenter Resource Efficiency · HPCA 2019 |
Methods — techniques the papers use, named apart from their topics
interference-aware approximation · 0.4incremental approximation · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Pliant: Leveraging Approximation to Improve Datacenter Resource EfficiencyabstractCloud multi-tenancy is typically constrained to a single interactive service colocated with one or more batch, low-priority services, whose performance can be sacrificed when deemed necessary. Approximate computing applications offer the opportunity to enable tighter colocation among multiple applications whose performance is important. We present Pliant, a lightweight cloud runtime that leverages the ability of approximate computing applications to tolerate some loss in their output quality to boost the utilization of shared servers. During periods of high resource contention, Pliant employs incremental and interference-aware approximation to reduce contention in shared resources, and prevent QoS violations for co-scheduled interactive, latency-critical services. We evaluate Pliant across different interactive and approximate computing applications, and show that it preserves QoS for all co-scheduled workloads, while incurring a 2.1% loss in output quality, on average. Neeraj Kulkarni, Feng Qi 0003, Christina Delimitrou |
HPCA | 2 |