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Identifier 000441080
Title Elastic resource allocation for a structural design application
Alternative Title Ελαστική διανομή πόρων για εφαρμογή στατικής ανάλυσης κτιρίων
Author Γιορταμής, Εμμανουήλ
Thesis advisor Πρατικάκης, Πολύβιος
Reviewer Μπίλας, Άγγελος
Μαγκούτης, Κωνσταντίνος
Abstract Cloud computing is the on-demand delivery of computing services such as servers, storage, computing power, databases and networking, through the internet. Rather that owning their own infrastructure, individuals or companies can rent access to highperformant computing resources from a cloud service provider. Cloud providers typically offer pay-as-you-go pricing models which charge users only for the resources they use. However, applications tend to have varying resource demands depending on both incoming traffic rates and incoming workload types. Resource over-allocation leads to wasted resources and thus, money, while under-allocation leads to Service-LevelObjective (SLO) violations. To this end, cloud computing platforms adopt horizontal and vertical elasticity in order to timely scale the application's resources on demand. Horizontal elasticity replicates the application's resources while vertical elasticity resizes them. It came to our attention that both industry and scientific literature focus more on horizontal elasticity than on vertical elasticity. Vertically elastic resource scaling is essential for applications with workload-dependent and spiky resource demands, however. In this thesis we present a vertically elastic resource allocator for fine-grained CPU-time allocation. Our proposed algorithm targets applications with job dependent parallelization spikes and accounts for variable traffic rates. Our example application is a Greek commercial structural design application used by civil engineers, named RAF. Its back-end, RAF::Solver, computes a building's static analyses by solving linear algebra equations and factorizing matrices using parallel Cholesky decomposition. Part of our work was to port the RAF::Solver to Linux, containerize, and deploy it as a cloud service. Then, our methodical profiling and benchmark analysis showed that each RAF::Solver instance has different parallelization speedup margins and thus CPU demands, due to each building's unique properties. Based on these observations we implemented both static and elastic CPU-time allocation schemes. Our evaluation analysis indicates that our fine-grained, vertically elastic CPU-time allocator yields better parallelization exploitation, up to 77% higher resource utilization and up to x10 less SLO violations, compared to the static allocation approaches.
Language English
Subject Cloud computing
Issue date 2021-07-30
Collection   School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
  Type of Work--Post-graduate theses
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