dc.contributor.author |
Vemmou, Marina
|
en |
dc.date.accessioned |
2020-01-21T08:28:25Z |
|
dc.date.available |
2020-01-21T08:28:25Z |
|
dc.identifier.uri |
https://dspace.lib.ntua.gr/xmlui/handle/123456789/49684 |
|
dc.identifier.uri |
http://dx.doi.org/10.26240/heal.ntua.17382 |
|
dc.rights |
Default License |
|
dc.subject |
Interference |
en |
dc.subject |
Application profiling |
en |
dc.subject |
Application classification |
en |
dc.subject |
Hardware performance counters |
en |
dc.subject |
Machine learning |
en |
dc.title |
Application classification techniques’ design for interference
mitigation in multiprocessor systems |
en |
heal.type |
bachelorThesis |
|
heal.classification |
Computer systems |
en |
heal.language |
en |
|
heal.access |
free |
|
heal.recordProvider |
ntua |
el |
heal.publicationDate |
2019-07-19 |
|
heal.abstract |
Multiprocessors are the basic building block of all modern computing systems. De-
spite the benefits yielded by the ability to execute applications concurrently, the rivalry
between applications for the chip’s shared resources, such the Last Level Cache and the
memory bandwidth, can be detrimental to performance. Especially in commercial cloud
environments, the provider is obliged to abide by strict performance guarantees required
by certain applications (Quality of Service goals), leading to the isolated execution of the
latter in dedicated servers to avoid interference, and consequently to the system’s under-
utilization.
As a result, extensive research has been conducted on the problem of application in-
terference. This diploma thesis focuses on predicting cases where interference might be
present by utilizing exclusively data by low-level hardware performance counters gathered
during isolated application execution. The main characteristic of our approach is that it
does not require executing an application with co-runners to decide whether it will suffer
from or create contention, making it ideal for cloud environments, where subjecting an
application to artificial interference is prohibitive.
Our final mechanisms consists of two machine learning base multiclass classifiers.
Each classifier receives a s input a specific set of hardware performance counter values
and classifies the application in regards to its ability to cause interference (noise) and its
sensitivity to it. We also showcase how the labels we have assigned each application
can then be utilized by an application scheduler in a datacenter, in order to maximize the
performance of high-priority applications. |
en |
heal.advisorName |
Γκούμας, Γεώργιος |
el |
heal.committeeMemberName |
Γκούμας, Γεώργιος |
el |
heal.committeeMemberName |
Κοζύρης, Νεκτάριος |
el |
heal.committeeMemberName |
Παπασπύρου, Νικόλαος |
el |
heal.academicPublisher |
Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Τεχνολογίας Πληροφορικής και Υπολογιστών |
el |
heal.academicPublisherID |
ntua |
|
heal.numberOfPages |
90 σ. |
|
heal.fullTextAvailability |
true |
|