Demo Data from EDBT 2026 / reviewers in the wild / expert
Jorge Andrale Ortiz
dblp:55/4797
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2006
—ORCID · none
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 |
Distributed systems · 44% Cloud and datacenter computing · 44% High-performance computing · 13% | |
| Computer networks
1 paper |
Network optimization and economics · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Network optimization and economics › resource allocation
market-based resource allocation |
0.1 | 1 | 2006 | Market-Based Resource Allocation using Price Prediction in a High Performance Computing Grid for Scientific Applications · HPDC 2006 |
Distributed systems
grid computing |
0.1 | 1 | 2006 | Market-Based Resource Allocation using Price Prediction in a High Performance Computing Grid for Scientific Applications · HPDC 2006 |
Cloud and datacenter computing
resource allocation |
0.1 | 1 | 2006 | Market-Based Resource Allocation using Price Prediction in a High Performance Computing Grid for Scientific Applications · HPDC 2006 |
High-performance computing
scientific computing systems |
0.0 | 1 | 2006 | Market-Based Resource Allocation using Price Prediction in a High Performance Computing Grid for Scientific Applications · HPDC 2006 |
Methods — techniques the papers use, named apart from their topics
signed money-transfer tokens · 0.1market-based allocation · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2006 | Market-Based Resource Allocation using Price Prediction in a High Performance Computing Grid for Scientific ApplicationsabstractWe present the implementation and analysis of a market-based resource allocation system for computational grids. Although grids provide a way to share resources and take advantage of statistical multiplexing, a variety of challenges remain. One is the economically efficient allocation of resources to users from disparate organizations who have their own and sometimes conflicting requirements for both the quantity and quality of services. Another is secure and scalable authorization despite rapidly changing allocations. Our solution to both of these challenges is to use a market-based resource allocation system. This system allows users to express diverse quantity- and quality-of-service requirements, yet prevents them from denying service to other users. It does this by providing tools to the user to predict and tradeoff risk and expected return in the computational market. In addition, the system enables secure and scalable authorization by using signed money-transfer tokens instead of identity-based authorization. This removes the overhead of maintaining and updating access control lists, while restricting usage based on the amount of money transferred. We examine the performance of the system by running a bioinformatics application on a fully operational implementation of an integrated grid market Thomas Sandholm, Jorge Andrale Ortiz, Jacob Odeberg |
HPDC | 3 |