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Title:UGLAŠEVANJE ZMOGLJIVOSTI APLIKACIJSKIH STREŽNIKOV JAVA EE Z ALGORITMOM DIFERENCIALNE EVOLUCIJE
Authors:ID Lešnik, Marko (Author)
ID Bošković, Borko (Mentor) More about this mentor... New window
ID Brest, Janez (Comentor)
Files:.pdf MAG_Lesnik_Marko_2012.pdf (2,61 MB)
MD5: FF74E0396FEA7D003B053CFDC612EAFA
PID: 20.500.12556/dkum/e25ecd48-6269-4321-aa89-42340156cd32
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V magistrskem delu predstavimo empirični pristop uglaševanja zmogljivosti aplikacijskih strežnikov (AS) Java EE. Pri tem uporabimo algoritem diferencialne evolucije GDE3 za večkriterijsko optimizacijo izbranih konfiguracijskih parametrov AS po principu črne škatle. V uvodnih poglavjih podamo raziskovalne hipoteze, orišemo proces uglaševanja zmogljivosti, arhitekturo AS Java EE in delovanje algoritma diferencialne evolucije GDE3. Sledi obravnava sorodnih del in podrobna predstavitev predlaganega pristopa, ki ga uporabimo za uglaševanje zmogljivosti AS GlassFish in preizkusne aplikacije Java EE DayTrader. Dosežene rezultate prikažemo tabelarično in grafično ter jih statistično analiziramo, pri tem sproti ovrednotimo zastavljene raziskovalne hipoteze. Na koncu podamo nekatere konceptualne ideje za nadgradnjo predlaganega pristopa, da bi se le-ta bolje vključeval v sodobno industrijsko paradigmo avtonomnega računalništva.
Keywords:proces uglaševanja zmogljivosti, aplikacijski strežniki, Java Enterprise Edition, večkriterijska optimizacija, diferencialna evolucija, empirični pristop, avtonomno računalništvo
Place of publishing:Maribor
Publisher:[M. Lešnik]
Year of publishing:2012
PID:20.500.12556/DKUM-37959 New window
UDC:004.774.021(043.2)
COBISS.SI-ID:16440086 New window
NUK URN:URN:SI:UM:DK:UGUG1WS7
Publication date in DKUM:15.11.2012
Views:2331
Downloads:120
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:PERFORMANCE TUNING OF JAVA EE APPLICATION SERVERS WITH DIFFERENTIAL EVOLUTION ALGORITHM
Abstract:In this MSc thesis we present an empirical approach for performance tuning of Java EE application servers (AS). To achieve this goal we use the differential evolution algorithm GDE3 for multi-objective black-box optimization of selected configuration parameters of AS. In the introductory chapters we define the research hypotheses, outline the performance tuning process, the architecture of Java EE AS and the operation of differential evolution algorithm GDE3. Discussion of related works and a detailed presentation of the proposed approach, which is used for performance tuning of AS GlassFish and Java EE test application DayTrader, follow. The achieved results are shown in tabular and graphical form with accompanying statistical analysis while the research hypotheses are simultaneously evaluated. At the end we propose some conceptual ideas to upgrade the proposed approach so it would be more compatible with the modern industrial paradigm of autonomic computing.
Keywords:performance tuning process, application servers, Java Enterprise Edition, multi-objective optimization, differential evolution, empirical approach, autonomic computing


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