118,742 research outputs found
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
Hygiene and Sanitation Software: An Overview of Approaches
A review of the state of the art in methods and techniques for sanitation and hygiene behaviour change, and other non-hardware aspects of sanitation programming. Includes introductory text and detailed entries on more than 20 approaches and techniques, with key references, summary information on effectiveness and implementation and an assessment of when different approaches should be used
Classification hardness for supervised learners on 20 years of intrusion detection data
This article consolidates analysis of established (NSL-KDD) and new intrusion detection datasets (ISCXIDS2012, CICIDS2017, CICIDS2018) through the use of supervised machine learning (ML) algorithms. The uniformity in analysis procedure opens up the option to compare the obtained results. It also provides a stronger foundation for the conclusions about the efficacy of supervised learners on the main classification task in network security. This research is motivated in part to address the lack of adoption of these modern datasets. Starting with a broad scope that includes classification by algorithms from different families on both established and new datasets has been done to expand the existing foundation and reveal the most opportune avenues for further inquiry. After obtaining baseline results, the classification task was increased in difficulty, by reducing the available data to learn from, both horizontally and vertically. The data reduction has been included as a stress-test to verify if the very high baseline results hold up under increasingly harsh constraints. Ultimately, this work contains the most comprehensive set of results on the topic of intrusion detection through supervised machine learning. Researchers working on algorithmic improvements can compare their results to this collection, knowing that all results reported here were gathered through a uniform framework. This work's main contributions are the outstanding classification results on the current state of the art datasets for intrusion detection and the conclusion that these methods show remarkable resilience in classification performance even when aggressively reducing the amount of data to learn from
MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME
Computational experiments using spatial stochastic simulations have led to
important new biological insights, but they require specialized tools, a
complex software stack, as well as large and scalable compute and data analysis
resources due to the large computational cost associated with Monte Carlo
computational workflows. The complexity of setting up and managing a
large-scale distributed computation environment to support productive and
reproducible modeling can be prohibitive for practitioners in systems biology.
This results in a barrier to the adoption of spatial stochastic simulation
tools, effectively limiting the type of biological questions addressed by
quantitative modeling. In this paper, we present PyURDME, a new, user-friendly
spatial modeling and simulation package, and MOLNs, a cloud computing appliance
for distributed simulation of stochastic reaction-diffusion models. MOLNs is
based on IPython and provides an interactive programming platform for
development of sharable and reproducible distributed parallel computational
experiments
ENORM: A Framework For Edge NOde Resource Management
Current computing techniques using the cloud as a centralised server will
become untenable as billions of devices get connected to the Internet. This
raises the need for fog computing, which leverages computing at the edge of the
network on nodes, such as routers, base stations and switches, along with the
cloud. However, to realise fog computing the challenge of managing edge nodes
will need to be addressed. This paper is motivated to address the resource
management challenge. We develop the first framework to manage edge nodes,
namely the Edge NOde Resource Management (ENORM) framework. Mechanisms for
provisioning and auto-scaling edge node resources are proposed. The feasibility
of the framework is demonstrated on a PokeMon Go-like online game use-case. The
benefits of using ENORM are observed by reduced application latency between 20%
- 80% and reduced data transfer and communication frequency between the edge
node and the cloud by up to 95\%. These results highlight the potential of fog
computing for improving the quality of service and experience.Comment: 14 pages; accepted to IEEE Transactions on Services Computing on 12
September 201
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