Scalable data processing model of the ALICE experiment in the cloud

Lončar, Petra

doctoral thesis

Scalable data processing model of the ALICE experiment in the cloud

Split: University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, 2023. urn:nbn:hr:179:516445

Lončar, Petra

University of Split
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture
Department of Electronics and Computing

Cite this document

APA 6th Edition

Lončar, P. (2023). Scalable data processing model of the ALICE experiment in the cloud (Doctoral thesis). Split: University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:179:516445

MLA 8th Edition

Lončar, Petra. "Scalable data processing model of the ALICE experiment in the cloud." Doctoral thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, 2023. https://urn.nsk.hr/urn:nbn:hr:179:516445

Chicago 17th Edition

Harvard

Lončar, P. (2023). 'Scalable data processing model of the ALICE experiment in the cloud', Doctoral thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, accessed 26 June 2024, https://urn.nsk.hr/urn:nbn:hr:179:516445

Vancouver

Lončar P. Scalable data processing model of the ALICE experiment in the cloud [Doctoral thesis]. Split: University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture; 2023 [cited 2024 June 26] Available at: https://urn.nsk.hr/urn:nbn:hr:179:516445

IEEE

P. Lončar, "Scalable data processing model of the ALICE experiment in the cloud", Doctoral thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:179:516445

Cite this item: https://urn.nsk.hr/urn:nbn:hr:179:516445

Please login to the repository to save this object to your list.

Metadata

Title	Scalable data processing model of the ALICE experiment in the cloud
Title (croatian)	Model skalabilne obrade podataka ALICE eksperimenta u oblaku
Author	Petra Lončar https://orcid.org/0000-0001-6486-2230
Mentor	Sven Gotovac (mentor)
Committee member	Eugen Mudnić (predsjednik povjerenstva)
Committee member	Josip Knezović (član povjerenstva)
Committee member	Goran Martinović https://orcid.org/0000-0002-7469-6018 (član povjerenstva)
Committee member	Linda Vicković (član povjerenstva)
Committee member	Dunja Božić-Štulić (član povjerenstva)
Granter	University of Split Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture (Department of Electronics and Computing) Split
Defense date and country	2023-06-09, Croatia
Scientific / art field, discipline and subdiscipline	TECHNICAL SCIENCES Computing Information Systems
Universal decimal classification (UDC)	621.3 - Electrical engineering
Abstract	This thesis proposes an optimisation strategy for scalable Big Data processing in a heterogeneous Cloud. The resource needs of A Large Ion Collider Experiment (ALICE) at the European Organization for Nuclear Research (CERN) are reviewed as a motivating example. The thesis examines how to efficiently process and optimise the processing of resourceintensive tasks on a heterogeneous Cloud infrastructure distributed in five data centres to meet the needs of the ALICE experiment at the Tier 2 level. The objective was to perform research on a much larger number of tasks and resources of a significantly larger capacity than prior studies, which focused on a smaller number of tasks and resources with a lower capacity. The proposed and developed processing model for ALICE Monte Carlo production is based on a centralised software-defined management approach for the use of heterogeneous resources. Algorithms for assigning tasks to heterogeneous virtual resources have been analysed and proposed. The proposed algorithms are based on the selected Evolution Strategies metaheuristic that has not yet been used in this domain, namely Evolution Strategies algorithm, Evolution Strategies algorithm with Longest Job First broker policy, and Evolution Strategies algorithm with Shortest Job First broker policy. The Cloud system model is implemented using the open-source CloudSim simulator. ALICE Monte Carlo production job requirements are imported into the simulation model as a workload created in Standard Workload Format (SWF) adapted for the Cloud simulator. The results of the simulation performance of the reference implementation under different loads were analysed and compared with the Genetic Algorithm from the same group of algorithms. The obtained results show multiple improvements. The proposed data processing model enables centralised software management of heterogeneous Cloud infrastructure, optimises measured metrics, improves resource usage, and achieves the system's scalability.
Abstract (croatian)	Ova disertacija predlaže strategiju optimizacije za skalabilnu obradu velikih podataka u heterogenom oblaku (engl. Cloud). U radu se kao motivirajući primjer razmatraju potrebe za resursima Eksperimenta na velikom ionskom sudaraču (engl. A Large Ion Collider Experiment, ALICE) na Europskoj organizaciji za nuklearna istraživanja (engl. European Organization for Nuclear Research, CERN). Rad istražuje kako učinkovito obraditi i optimizirati obradu resursno-intenzivnih zadataka na heterogenoj infrastrukturi u oblaku raspoređenoj u pet podatkovnih centara kako bi se zadovoljile potrebe na Tier 2 razini ALICE eksperimenta. Cilj je bio provesti istraživanje za višestruko veći broj zadataka i resurse znatno većeg kapaciteta u odnosu na dosadašnja istraživanja koja su provođena na manjem broju zadataka i resursima manjih kapaciteta. Predloženi i razvijeni model obrade za ALICE Monte Carlo produkciju temelji se na centraliziranom softverski definiranom pristupu upravljanja korištenjem heterogenih resursa. Analizirani su i predloženi algoritmi za dodjelu zadataka heterogenim virtualnim resursima. Predloženi algoritmi temelje se na odabranoj metaheuristici evolucijskih strategija (engl. Evolution Strategies) koja dosad nije korištena u ovoj domeni, a to su algoritam evolucijskih strategija, algoritam evolucijskih strategija s broker politikom kod koje prioritet izvođenja imaju najdulji zadaci (engl. Longest Job First) i algoritam evolucijskih strategija s broker politikom koja prioritet izvođenja daje najkraćim zadacima (engl. Shortest Job First). Model računalnog oblaka implementiran je pomoću CloudSim simulatora otvorenog koda. Zahtjevi ALICE Monte Carlo produkcijskih poslova uneseni su u simulaciju modela u obliku kreiranog radnog opterećenja u standardnom formatu radnog opterećenja (engl. Standard Workload Format, SWF) prilagođenom za rad u odabranom simulatoru. Analizirani su rezultati izvedbe simulacije referentne implementacije pod različitim opterećenjima i uspoređeni su s genetskim algoritmom (engl. Genetic Algorithm) iz iste skupine algoritama. Dobiveni rezultati pokazuju višestruka poboljšanja. Ovdje predložen model obrade podataka omogućava centralizirano softversko upravljanje heterogenom infrastrukturom u oblaku, optimizira mjerene metrike, poboljšava korištenje resursa i postiže skalabilnost sustava.
Keywords
Keywords (croatian)
Language	english
URN:NBN	urn:nbn:hr:179:516445
Promotion	2023
Study programme	Title: Electrical Engineering and Information Technology Study programme type: university Study level: postgraduate Academic / professional title: doktor/doktorica znanosti, područje tehničkih znanosti, polje računarstvo (doktor/doktorica znanosti, područje tehničkih znanosti, polje računarstvo)
Type of resource	Text
Extent	115 str.
File origin	Born digital
Access conditions	Open access
Terms of use
Repository	Repository of the Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture
Created on	2023-07-05 12:46:46