Demo Data from SIGIR 2026 / reviewers in the wild / expert
Mia Primorac
dblp:167/7996
· DBLP profile ↗
4ranked-venue papers
1as first author
1since 2021 · last 2021
0000-0003-1001-1195ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 3Computer networks · 1 · 1 first-author · 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
2 papers |
Cloud and datacenter computing · 71% Performance modeling and evaluation · 14% Energy-efficient computing · 8% | |
| Software engineering, system software, and programming languages
1 paper |
Operating systems · 100% |
Topics — the 3 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cloud and datacenter computing › datacenter services › online service systems › internet services
interactive services |
0.5 | 1 | 2021 | When to Hedge in Interactive Services · NSDI 2021 |
Operating systems › operating system design
dataplane operating system |
0.3 | 1 | 2017 | The IX Operating System: Combining Low Latency, High Throughput, and Efficiency in a Protected Dataplane · ACM Trans. Comput. Syst. 2017 |
Performance modeling and evaluation
queueing models |
0.1 | 1 | 2021 | When to Hedge in Interactive Services · NSDI 2021 |
Methods — techniques the papers use, named apart from their topics
zero-copy API · 0.6hardware virtualization · 0.6batching · 0.6queueing theory · 0.5hedging · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | When to Hedge in Interactive Services
Mia Primorac, Katerina J. Argyraki, Edouard Bugnion |
NSDI | 1 |
| 2017 | The IX Operating System: Combining Low Latency, High Throughput, and Efficiency in a Protected DataplaneabstractThe conventional wisdom is that aggressive networking requirements, such as high packet rates for small messages and μs-scale tail latency, are best addressed outside the kernel, in a user-level networking stack. We present ix , a dataplane operating system that provides high I/O performance and high resource efficiency while maintaining the protection and isolation benefits of existing kernels. ix uses hardware virtualization to separate management and scheduling functions of the kernel (control plane) from network processing (dataplane). The dataplane architecture builds upon a native, zero-copy API and optimizes for both bandwidth and latency by dedicating hardware threads and networking queues to dataplane instances, processing bounded batches of packets to completion, and eliminating coherence traffic and multicore synchronization. The control plane dynamically adjusts core allocations and voltage/frequency settings to meet service-level objectives. We demonstrate that ix outperforms Linux and a user-space network stack significantly in both throughput and end-to-end latency. Moreover, ix improves the throughput of a widely deployed, key-value store by up to 6.4× and reduces tail latency by more than 2× . With three varying load patterns, the control plane saves 46%--54% of processor energy, and it allows background jobs to run at 35%--47% of their standalone throughput. Adam Belay, George Prekas, Mia Primorac, Ana Klimovic, Samuel Grossman, Christoforos E. Kozyrakis, Edouard Bugnion |
ACM Trans. Comput. Syst. | 3 |
| 2017 | Corrigendum to "The IX Operating System: Combining Low Latency, High Throughput and Efficiency in a Protected Dataplane"abstractNo abstract available. Adam Belay, George Prekas, Mia Primorac, Ana Klimovic, Samuel Grossman, Christoforos E. Kozyrakis, Edouard Bugnion |
ACM Trans. Comput. Syst. | 3 |
| 2015 | Energy proportionality and workload consolidation for latency-critical applicationsabstractEnergy proportionality and workload consolidation are important objectives towards increasing efficiency in large-scale datacenters. Our work focuses on achieving these goals in the presence of applications with μs-scale tail latency requirements. Such applications represent a growing subset of datacenter workloads and are typically deployed on dedicated servers, which is the simplest way to ensure low tail latency across all loads. Unfortunately, it also leads to low energy efficiency and low resource utilization during the frequent periods of medium or low load. George Prekas, Mia Primorac, Adam Belay, Christoforos E. Kozyrakis, Edouard Bugnion |
SoCC | 2 |