O Passado Também Importa: Um Mecanismo de Alocação Justa de Múltiplos Tipos de Recursos ao Longo do Tempo
ResumoShared computing systems are composed by different resource types, such as CPU and memory, and hold users with different resource constraints. While some users execute short workloads in which fast allocation is essential, others execute long workloads that require more resources. Among the different proposals to allocate resources in this scenario, Dominant Resource Fairness (DRF) is notable for satisfying some desirable properties, such as truthfulness and Pareto efficiency. However, these proposals focus only on instantaneous fairness, ignoring users heterogeneity. This paper proposes DRF with state (SDRF). SDRF satisfies the fundamental properties of DRF, besides enforcing a new notion of fairness that look at past resource allocations. We verify SDRF with both theoretical analysis and simulations using Google cluster traces. Results show that SDRF reduces users' average waiting time and improves fairness by increasing the number of completed tasks for users with lower demand with low impact on high-demand users.
SADOK, Hugo; CAMPISTA, Miguel Elias M.; COSTA, Luís Henrique M. K.. O Passado Também Importa: Um Mecanismo de Alocação Justa de Múltiplos Tipos de Recursos ao Longo do Tempo. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 36. , 2018, Campos do Jordão. Anais do XXXVI Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Porto Alegre: Sociedade Brasileira de Computação, may 2018 . ISSN 2177-9384.