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Online robust optimization framework for QoS guarantees in distributed soft real-time systems
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Autores
Orientador(es)
Resumo(s)
In distributed soft real-time systems, maximizing the aggregate
quality-of-service (QoS) is a typical system-wide goal, and addressing
the problem through distributed optimization is challenging.
Subtasks are subject to unpredictable failures in many practical
environments, and this makes the problem much harder. In
this paper, we present a robust optimization framework for maximizing
the aggregate QoS in the presence of random failures. We
introduce the notion of K-failure to bound the effect of random
failures on schedulability. Using this notion we define the concept
of K-robustness that quantifies the degree of robustness on QoS
guarantee in a probabilistic sense. The parameter K helps to tradeoff
achievable QoS versus robustness. The proposed robust framework
produces optimal solutions through distributed computations
on the basis of Lagrangian duality, and we present some implementation
techniques. Our simulation results show that the proposed
framework can probabilistically guarantee sub-optimal QoS which
remains feasible even in the presence of random failures.
Descrição
Palavras-chave
Soft real-time systems Robust optimization QoS guarantee
Contexto Educativo
Citação
Editora
ACM