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[logseq-plugin-git:commit] 2026-01-12T18:42:12.020Z

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Davide Di Ruscio
2026-01-12 19:42:12 +01:00
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pages/EditoringChairing___SOSYM-25-00005370.md
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@@ -15,6 +15,7 @@ todoist:: https://app.todoist.com/app/task/software-and-systems-modeling-manuscr
- ### [[Comments]] - ### [[Comments]]
- Reviewer 1 / REJECT - Reviewer 1 / REJECT
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- Reviewer 2 / REJECT - Reviewer 2 / REJECT
- Reviewer 3 / REJECT - Reviewer 3 / REJECT
- The paper proposes a model-driven approach to address energy inefficiencies in multi-tenant cloud data centers caused by resource fragmentation and inter-tenant interference. The authors introduce a methodology using Formal Concept Analysis (FCA) to systematically identify semantic dependencies between tenants based on shared application usage, constructing a concept lattice to guide tenant grouping. Two core algorithms, the Algorithm for Placement of Tenants (APT) and the Algorithm for Consolidation of Tenants (ACT), are developed to leverage this lattice for optimizing resource utilization and minimizing network traffic. The approach is evaluated via simulation using CloudSim Plus, with results reporting significant reductions in active servers and energy consumption compared to baselines such as Best-Fit Decreasing and Genetic Algorithms. - The paper proposes a model-driven approach to address energy inefficiencies in multi-tenant cloud data centers caused by resource fragmentation and inter-tenant interference. The authors introduce a methodology using Formal Concept Analysis (FCA) to systematically identify semantic dependencies between tenants based on shared application usage, constructing a concept lattice to guide tenant grouping. Two core algorithms, the Algorithm for Placement of Tenants (APT) and the Algorithm for Consolidation of Tenants (ACT), are developed to leverage this lattice for optimizing resource utilization and minimizing network traffic. The approach is evaluated via simulation using CloudSim Plus, with results reporting significant reductions in active servers and energy consumption compared to baselines such as Best-Fit Decreasing and Genetic Algorithms.