Multi-Server Multi-Function Distributed Computation

The work here studies the communication cost for a multi-server multi-task distributed computation framework, as well as for a broad class of functions and data statistics. Considering the framework where a user seeks the computation of multiple complex (conceivably non-linear) tasks from a set of d...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Entropy (Basel, Switzerland) Ročník 26; číslo 6; s. 448
Hlavní autoři: Malak, Derya, Deylam Salehi, Mohammad Reza, Serbetci, Berksan, Elia, Petros
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 26.05.2024
MDPI
Témata:
Artificial intelligence
characteristic graph entropy
Communication
Costs
Data compression
Datasets
distributed computation
Distributed processing
File servers
Fourier transforms
functional compression
Geospatial data
Graphs
Linear functions
linearly separable functions
multi-server
non-linear functions
Task complexity
Upper bounds
non-linear functions
functional compression
skewed statistics
data correlations
multi-function
distributed computation
multi-server
linearly separable functions
characteristic graph entropy
ISSN:1099-4300, 1099-4300
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The work here studies the communication cost for a multi-server multi-task distributed computation framework, as well as for a broad class of functions and data statistics. Considering the framework where a user seeks the computation of multiple complex (conceivably non-linear) tasks from a set of distributed servers, we establish the communication cost upper bounds for a variety of data statistics, function classes, and data placements across the servers. To do so, we proceed to apply, for the first time here, Körner’s characteristic graph approach—which is known to capture the structural properties of data and functions—to the promising framework of multi-server multi-task distributed computing. Going beyond the general expressions, and in order to offer clearer insight, we also consider the well-known scenario of cyclic dataset placement and linearly separable functions over the binary field, in which case, our approach exhibits considerable gains over the state of the art. Similar gains are identified for the case of multi-linear functions.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
This work was conducted when B. Serbetci was a Postdoctoral Researcher at EURECOM.
ISSN:1099-4300
1099-4300
DOI:10.3390/e26060448