Mihalis G. Markakis
University of Navarra
35 Papers
317 Citations
Mihalis G. Markakis is an academic researcher from University of Navarra. The author has contributed to research in topics: Queue & Scheduling (computing). The author has an hindex of 14, co-authored 30 publications. Previous affiliations of Mihalis G. Markakis include Pompeu Fabra University & Massachusetts Institute of Technology.
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Papers
Queue-length asymptotics for generalized max-weight scheduling in the presence of heavy-tailed traffic
TL;DR: This work investigates the asymptotic behavior of the steady-state queue-length distribution under generalized max-weight scheduling in the presence of heavy-tailed traffic and shows that the tail of the light queue distribution is at least as heavy as a power-law curve.
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Scheduling policies for single-hop networks with heavy-tailed traffic
Mihalis G. Markakis,Eytan Modiano,John N. Tsitsiklis +2 more
- 30 Sep 2009
TL;DR: The queue-length instability of Max-Weight scheduling is proved, in the presence of heavy-tailed traffic, and the Max- Weight-log policy is introduced, which provides performance guarantees, without any knowledge of the arriving traffic.
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Max-Weight Scheduling in Queueing Networks with Heavy-Tailed Traffic
TL;DR: In this paper, the authors consider the problem of packet scheduling in single-hop queueing networks, and analyze the impact of heavy-tailed traffic on the performance of Max-Weight scheduling.
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Distributed power allocation and user assignment in OFDMA cellular networks
Sem Borst,Mihalis G. Markakis,Iraj Saniee +2 more
- 01 Sep 2011
TL;DR: A randomized algorithm is devised for the Network Utility Maximization problem, whose proof of asymptotic optimality is derived from the classical framework of interacting particle systems, via a judiciously selected neighborhood structure, and yields provable convergence in the limit.
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Nonlinear Modeling of the Dynamic Effects of Infused Insulin on Glucose: Comparison of Compartmental With Volterra Models
TL;DR: The results corroborate the proposition that it may be preferable to obtain data-driven (i.e., inductive) models in a more general and realistic operating context, without resorting to the restrictive prior assumptions and simplifications regarding model structure and/or experimental protocols that are necessary for the compartmental models proposed previously.
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