This doctoral Thesis describes a novel way to select the best computer node out of
a pool of available potentially heterogeneous computing nodes for the execution of
computational tasks. This is a very basic and dificult problem of computer science
and computing centres tried to get around it by using only homogeneous compute
clusters. Usually this fails as like any technical equipment, clusters get extended,
adapted or repaired over time, and you end up with a heterogeneous configuration.
So far, the solution for this, was:
• To leave it to the computer users to choose the right node(s) for execution, or
•To make extensive tests by executing and measuring all tasks on every type
of computing node available in the pool. In the typical case, where a large
number of tasks would need to be tested on many different types of nodes, this
could use a lot of computing resources, sometimes even more than the actual
execution one wants to optimize.
In a specific situation (hierarchical multi-clusters), the situation is worse, as
the configuration of the cluster changes over time, so that the execution tests would
have to be done over and over, every time the configuration of the cluster is changed.
I developed a novel and elegant solution for this problem, named "Performance
Probe", or just "Probe", for short. A probe is a striped-down version of a compu-
tational task which includes all important characteristics of the original task, but
can be executed in a much shorter time (seconds, instead of hours), is much smaller
than the original task (about 5% of the original size in the worst cases), but allows
to predict the execution time of the original within reasonable bounds (around 90%
accuracy).
These results are very important: as scheduling is a basic problem of computer
science, these results cannot only be used in the setting described by the thesis (of
setting the right compute node for tasks in a hierarchical multi-cluster), but can
also be applied in many diferent contexts every time scheduling and/or selection
decisions have to be made: selecting where a computational task would run most
efficiently (which cluster at which centre); picking the right execution nodes in a
large complex (grid, cloud), work
ows and many more.
| Identifer | oai:union.ndltd.org:TDX_UAB/oai:www.tdx.cat:10803/286110 |
| Date | 15 July 2011 |
| Creators | Otto Strube, Alexandre |
| Contributors | Luque, Emilio, Universitat Autònoma de Barcelona. Departament d'Arquitectura de Computadors i Sistemes Operatius |
| Publisher | Universitat Autònoma de Barcelona |
| Source Sets | Universitat Autònoma de Barcelona |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis, info:eu-repo/semantics/publishedVersion |
| Format | 100 p., application/pdf |
| Source | TDX (Tesis Doctorals en Xarxa) |
| Rights | ADVERTIMENT. L'accés als continguts d'aquesta tesi doctoral i la seva utilització ha de respectar els drets de la persona autora. Pot ser utilitzada per a consulta o estudi personal, així com en activitats o materials d'investigació i docència en els termes establerts a l'art. 32 del Text Refós de la Llei de Propietat Intel·lectual (RDL 1/1996). Per altres utilitzacions es requereix l'autorització prèvia i expressa de la persona autora. En qualsevol cas, en la utilització dels seus continguts caldrà indicar de forma clara el nom i cognoms de la persona autora i el títol de la tesi doctoral. No s'autoritza la seva reproducció o altres formes d'explotació efectuades amb finalitats de lucre ni la seva comunicació pública des d'un lloc aliè al servei TDX. Tampoc s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant als continguts de la tesi com als seus resums i índexs., info:eu-repo/semantics/openAccess |
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