4,024 research outputs found
Generating realistic scaled complex networks
Research on generative models is a central project in the emerging field of
network science, and it studies how statistical patterns found in real networks
could be generated by formal rules. Output from these generative models is then
the basis for designing and evaluating computational methods on networks, and
for verification and simulation studies. During the last two decades, a variety
of models has been proposed with an ultimate goal of achieving comprehensive
realism for the generated networks. In this study, we (a) introduce a new
generator, termed ReCoN; (b) explore how ReCoN and some existing models can be
fitted to an original network to produce a structurally similar replica, (c)
use ReCoN to produce networks much larger than the original exemplar, and
finally (d) discuss open problems and promising research directions. In a
comparative experimental study, we find that ReCoN is often superior to many
other state-of-the-art network generation methods. We argue that ReCoN is a
scalable and effective tool for modeling a given network while preserving
important properties at both micro- and macroscopic scales, and for scaling the
exemplar data by orders of magnitude in size.Comment: 26 pages, 13 figures, extended version, a preliminary version of the
paper was presented at the 5th International Workshop on Complex Networks and
their Application
Scaling Virtualized Smartphone Images in the Cloud
Üks selle Bakalaureuse töö eesmärkidest oli Android-x86 nutitelefoni platvormi juurutamine
pilvekeskkonda ja välja selgitamine, kas valitud instance on piisav virtualiseeritud nutitelefoni
platvormi juurutamiseks ning kui palju koormust see talub. Töös kasutati Amazoni instance'i
M1 Small, mis oli piisav, et juurutada Androidi virtualiseeritud platvormi, kuid jäi kesisemaks
kui mobiiltelefon, millel teste läbi viidi. M1 Medium instance'i tüüp oli sobivam ja näitas
paremaid tulemusi võrreldes telefoniga.
Teostati koormusteste selleks vastava tööriistaga Tsung, et näha, kui palju üheaegseid
kasutajaid instance talub. Testi läbiviimiseks paigaldasime Dalviku instance'ile Tomcat
serveri.
Pärast teste ühe eksemplariga, juurutasime külge Elastic Load Balancing ja
automaatse skaleerimise Amazon Auto Scaling tööriista. Esimene neist jaotas koormust
instance'ide
vahel.
Automaatse
skaleerimise
tööriista
kasutasime,
et
rakendada
horisontaalset skaleerimist meie Android-x86 instance'le. Kui CPU tõusis üle 60% kauemaks
kui üks minut, siis tehti eelmisele identne instance ja koormust saadeti edaspidi sinna. Seda
protseduuri vajadusel korrati maksimum kümne instance'ini. Meie teostusel olid tagasilöögid,
sest Elastic Load Balancer aegus 60 sekundi pärast ning me ei saanud kõikide välja
saadetud päringutele vastuseid. Serverisse saadetud faili kirjutamine ja kompileerimine olid
kulukad tegevused ja seega ei lõppenud kõik 60 sekundi jooksul. Me ei saanud koos Load
Balancer'iga läbiviidud testidest piisavalt andmeid, et teha järeldusi, kas virtualiseeritud
nutitelefoni platvorm Android on hästi või halvasti skaleeruv.In this thesis we deployed a smartphone image in an Amazon EC2 instance and ran stress tests on them to know how much users can one instance bear and how scalable it is. We tested how much time would a method run in a physical Android device and in a cloud instance. We deployed CyanogenMod and Dalvik for a single instance. We used Tsung for stress testing. For those tests we also made a Tomcat server on Dalvik instance that would take the incoming file, the file would be compiled with java and its class file would be wrapped into dex, a Dalvik executable file, that is later executed with Dalvik. Three instances made a Tsung cluster that sent load to a Dalvik Virtual Machine instance. For scaling we used Amazon Auto Scaling tool and Elastic Load Balancer that divided incoming load between the instances
Dimensionality of social networks using motifs and eigenvalues
We consider the dimensionality of social networks, and develop experiments
aimed at predicting that dimension. We find that a social network model with
nodes and links sampled from an -dimensional metric space with power-law
distributed influence regions best fits samples from real-world networks when
scales logarithmically with the number of nodes of the network. This
supports a logarithmic dimension hypothesis, and we provide evidence with two
different social networks, Facebook and LinkedIn. Further, we employ two
different methods for confirming the hypothesis: the first uses the
distribution of motif counts, and the second exploits the eigenvalue
distribution.Comment: 26 page
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