Simulation framework for carbon-aware workload shifting in the cloud

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.advisorFodor, Viktoria
dc.contributor.advisorDán, György
dc.contributor.authorAbinaov, Murali Amudha
dc.contributor.schoolPerustieteiden korkeakoulufi
dc.contributor.schoolSchool of Scienceen
dc.contributor.supervisorPremsankar, Gopika
dc.date.accessioned2024-12-16T18:02:27Z
dc.date.available2024-12-16T18:02:27Z
dc.date.issued2024-10-28
dc.description.abstractCarbon-aware workload shifting algorithms are a method to utilize the global scale and flexibility of computing resources offered by cloud computing concepts to minimize the greenhouse gases emitted from the computation tasks. Specifically, the same computational work can produce more or less emissions depending on the geographic location or time when the work is done. Some workload shifting algorithms have already been implemented by major cloud providers like Google, but they are continually iterated upon and improved. This results in a myriad of workload shifting algorithms with different performances and specialties evaluated in custom simulators with different network layouts. In our thesis, we design and develop a simulation framework which can test and compare carbon-aware workload shifting algorithms to aid their development and to determine what scenarios a given algorithm may perform well in. The performance of the algorithms are measured via the amount of carbon dioxide equivalent greenhouse gases produced during a simulation. Some tools already exist to compare algorithms, but they often are highly specialized or put a significant emphasis on the specific resources available in simulated data centers. A core aspect of the framework is the interchangeable carbon intensity data and workloads. The interchangeability of these components save time during the development of workload shifting algorithms by enabling experiments using carbon intensity data from different sources and different workloads. There are common formats for the carbon intensity data and the workloads, a data frame column structure for the former, and a custom API data frame extension for the latter. The common formats ease the compatibility between a given algorithm and the workloads or carbon intensity data not custom designed specifically for the given algorithm. Additionally, this enables two or more algorithms to utilize the same workload without the workload needing to be custom implemented for each algorithm, which in turn supports comparing the algorithms during development.en
dc.format.extent58
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/132328
dc.identifier.urnURN:NBN:fi:aalto-202412167806
dc.language.isoenen
dc.programmeMaster's Programme in Security and Cloud Computingen
dc.programme.majorSecurity and Cloud Computingen
dc.subject.keywordCarbon-aware workload shifting algorithmsen
dc.subject.keywordcloud computingen
dc.subject.keywordgreen computingen
dc.subject.keyworddata centersen
dc.subject.keywordsimulation frameworken
dc.subject.keywordworkload shiftingen
dc.titleSimulation framework for carbon-aware workload shifting in the clouden
dc.typeG2 Pro gradu, diplomityöfi
dc.type.ontasotMaster's thesisen
dc.type.ontasotDiplomityöfi
local.aalto.electroniconlyyes
local.aalto.openaccessyes

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