Demo Data from EDBT 2026 / reviewers in the wild / expert
Navindra Yadav
dblp:80/4092
· DBLP profile ↗
3ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 first-authorComputer networks · 1Databases, data management, data science and information retrieval · 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 networks
1 paper |
Datacenter networks · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Database system architecture and tuning · 77% Query processing and optimization · 23% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Database system architecture and tuning › self-managing database systems › database diagnosis
root cause analysis |
0.4 | 1 | 2019 | ExplainIt! - A Declarative Root-cause Analysis Engine for Time Series Data · SIGMOD Conference 2019 |
Datacenter networks › load balancing
congestion-aware load balancing |
0.2 | 1 | 2014 | CONGA: distributed congestion-aware load balancing for datacenters · SIGCOMM 2014 |
Datacenter networks
datacenter transport |
0.2 | 1 | 2014 | CONGA: distributed congestion-aware load balancing for datacenters · SIGCOMM 2014 |
Datacenter networks › load balancing
flowlet switching |
0.2 | 1 | 2014 | CONGA: distributed congestion-aware load balancing for datacenters · SIGCOMM 2014 |
Datacenter networks
load balancing |
0.2 | 1 | 2014 | CONGA: distributed congestion-aware load balancing for datacenters · SIGCOMM 2014 |
Methods — techniques the papers use, named apart from their topics
probabilistic graphical model · 0.4flowlet · 0.2distributed congestion feedback · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | ExplainIt! - A Declarative Root-cause Analysis Engine for Time Series DataabstractWe present \sys, a declarative, unsupervised root-cause analysis engine that uses time series monitoring data from large complex systems such as data centres. \sys empowers operators to succinctly specify a large number of causal hypotheses to search for causes of interesting events. \sys then ranks these hypotheses, reducing the number of causal dependencies from hundreds of thousands to a handful for human understanding. We show how a declarative language, such as SQL, can be effective in declaratively enumerating hypotheses that probe the structure of an unknown probabilistic graphical causal model of the underlying system. Our thesis is that databases are in a unique position to enable users to rapidly explore the possible causal mechanisms in data collected from diverse sources. We empirically demonstrate how \sys had helped us resolve over 30~performance issues in a commercial product since late 2014, of which we discuss a few cases in detail. Vimalkumar Jeyakumar, Omid Madani, Ali ParandehGheibi, Ashutosh Kulshreshtha, Weifei Zeng, Navindra Yadav |
SIGMOD Conference | 6 |
| 2014 | CONGA: distributed congestion-aware load balancing for datacentersabstractWe present the design, implementation, and evaluation of CONGA, a network-based distributed congestion-aware load balancing mechanism for datacenters. CONGA exploits recent trends including the use of regular Clos topologies and overlays for network virtualization. It splits TCP flows into flowlets, estimates real-time congestion on fabric paths, and allocates flowlets to paths based on feedback from remote switches. This enables CONGA to efficiently balance load and seamlessly handle asymmetry, without requiring any TCP modifications. CONGA has been implemented in custom ASICs as part of a new datacenter fabric. In testbed experiments, CONGA has 5x better flow completion times than ECMP even with a single link failure and achieves 2-8x better throughput than MPTCP in Incast scenarios. Further, the Price of Anarchy for CONGA is provably small in Leaf-Spine topologies; hence CONGA is nearly as effective as a centralized scheduler while being able to react to congestion in microseconds. Our main thesis is that datacenter fabric load balancing is best done in the network, and requires global schemes such as CONGA to handle asymmetry. Mohammad Alizadeh, Tom Edsall, Sarang Dharmapurikar, Ramanan Vaidyanathan, Kevin Chu, Andy Fingerhut, Vinh The Lam, Francis Matus, Navindra Yadav, George Varghese |
SIGCOMM | 10 |
| 1999 | Parallel Saturating Fractional Arithmetic UnitsabstractThis paper describes the designs of a saturating adder multiplier single MAC unit, and dual MAC unit with single cycle latencies. The dual MAC unit can perform two saturating MAC operations in parallel and accumulate the results with saturation. Specialized saturation logic ensures that the output of the dual MAC unit is identical to the result of the operations performed serially with saturation after each multiplication and each addition. Navindra Yadav, Michael J. Schulte, C. John Glossner |
Great Lakes Symposium on VLSI | 1 |