50,153 research outputs found
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
This report documents the program and the outcomes of GI-Dagstuhl Seminar
16394 "Software Performance Engineering in the DevOps World".
The seminar addressed the problem of performance-aware DevOps. Both, DevOps
and performance engineering have been growing trends over the past one to two
years, in no small part due to the rise in importance of identifying
performance anomalies in the operations (Ops) of cloud and big data systems and
feeding these back to the development (Dev). However, so far, the research
community has treated software engineering, performance engineering, and cloud
computing mostly as individual research areas. We aimed to identify
cross-community collaboration, and to set the path for long-lasting
collaborations towards performance-aware DevOps.
The main goal of the seminar was to bring together young researchers (PhD
students in a later stage of their PhD, as well as PostDocs or Junior
Professors) in the areas of (i) software engineering, (ii) performance
engineering, and (iii) cloud computing and big data to present their current
research projects, to exchange experience and expertise, to discuss research
challenges, and to develop ideas for future collaborations
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
Algorithms for advance bandwidth reservation in media production networks
Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results
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