EDBT 2026 Demo / reviewers in the wild / expert
Fernando G. Palacios
dblp:50/253
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1975
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 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 |
Performance modeling and evaluation · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Performance modeling and evaluation › queueing models › queueing network model
multiclass queueing networks |
0.0 | 1 | 1975 | Open, Closed, and Mixed Networks of Queues with Different Classes of Customers · J. ACM 1975 |
Performance modeling and evaluation › queueing models › product-form queueing networks
product-form equilibrium distribution |
0.0 | 1 | 1975 | Open, Closed, and Mixed Networks of Queues with Different Classes of Customers · J. ACM 1975 |
Performance modeling and evaluation
queueing models |
0.0 | 1 | 1975 | Open, Closed, and Mixed Networks of Queues with Different Classes of Customers · J. ACM 1975 |
Performance modeling and evaluation › queueing models
queueing network model |
0.0 | 1 | 1975 | Open, Closed, and Mixed Networks of Queues with Different Classes of Customers · J. ACM 1975 |
Methods — techniques the papers use, named apart from their topics
markov chain analysis · 0.0generating functions · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1975 | Open, Closed, and Mixed Networks of Queues with Different Classes of CustomersabstractThe joint equihbrmm distribution of queue sizes in a network of queues containing N service centers and R classes of customers m derived The equilibrium state probabillUes have the general form P(S) = Cd(S) fl(xl)f2(x2) .fN(x~), where S is the state of the system, x, is the configuration of customers at the ~th service center, d(S) is a function of the state of the model, f, is a function that depends on the type of the zth service center, and C is a normalizing constant It is assumed that the eqmhbrlum probabfl~tles exmt and are unique Four types of service centers to model central processors, data channels, terminals, and routing delays are considered The queuemg dlSclphnes associated with these service centers include first-come-first-served, processor sharing, no queueing, and last-come-first-served Each customer belongs to a single class of customers while awaiting or receiving serwce at a service center, but may change classes and service centers according to fixed probabditms at the completion of a service request For open networks, state dependent arrival processes are considered Closed networks are those with no exogenous arrivals A network may be closed with respect to some classes of customers and open with respect to other classes of customers At three of the four types of serwce centers, the service times of customers are governed by probablhty dmtrlbutions hawng ratmnM Laplace transforms, different classes of customers hawng different distributions At first-come-first-served-type service centers, the service time distribution must be identical and exponentml for all classes of customers.Examples show how different classes of customers can affect models of computer systems. Forest Baskett, K. Mani Chandy, Richard R. Muntz, Fernando G. Palacios |
J. ACM | 4 |