Mihalis G. Markakis

Scheduling policies for single-hop networks with heavy-tailed traffic

In the first part of the paper, we study the impact of scheduling, in a setting of parallel queues with a mix of heavy-tailed and light-tailed traffic. We analyze queue-length unaware scheduling policies, such as round-robin, randomized, and priority, and characterize their performance. We prove the queue-length instability of Max-Weight scheduling, in the presence of heavy-tailed traffic. Motivated by this, we analyze the performance of Max-Weight-α scheduling, and establish conditions on the α-parameters, under which the system is queue-length stable. We also introduce the Max-Weight-log policy, which provides performance guarantees, without any knowledge of the arriving traffic. In the second part of the paper, we extend the results on Max-Weight and Max-Weight-α scheduling to a single-hop network, with arbitrary topology and scheduling constraints.

Max-Weight Scheduling in Queueing Networks With Heavy-Tailed Traffic

We consider the problem of scheduling in a single-hop switched network with a mix of heavy-tailed and light-tailed traffic and analyze the impact of heavy-tailed traffic on the performance of Max-Weight scheduling. As a performance metric, we use the delay stability of traffic flows: A traffic flow is delay-stable if its expected steady-state delay is finite, and delay-unstable otherwise. First, we show that a heavy-tailed traffic flow is delay-unstable under any scheduling policy. Then, we focus on the celebrated Max-Weight scheduling policy and show that a light-tailed flow that conflicts with a heavy-tailed flow is also delay-unstable. This is true irrespective of the rate or the tail distribution of the light-tailed flow or other scheduling constraints in the network. Surprisingly, we show that a light-tailed flow can become delay-unstable, even when it does not conflict with heavy-tailed traffic. Delay stability in this case may depend on the rate of the light-tailed flow. Finally, we turn our attention to the class of Max-Weight-α scheduling policies. We show that if the α-parameters are chosen suitably, then the sum of the α-moments of the steady-state queue lengths is finite. We provide an explicit upper bound for the latter quantity, from which we derive results related to the delay stability of traffic flows, and the scaling of moments of steady-state queue lengths with traffic intensity.

Nonlinear Modeling of the Dynamic Effects of Infused Insulin on Glucose: Comparison of Compartmental With Volterra Models

This paper presents the results of a computational study that compares simulated compartmental (differential equation) and Volterra models of the dynamic effects of insulin on blood glucose concentration in humans. In the first approach, we employ the widely accepted ldquominimal modelrdquo and an augmented form of it, which incorporates the effect of insulin secretion by the pancreas, in order to represent the actual closed-loop operating conditions of the system, and in the second modeling approach, we employ the general class of Volterra-type models that are estimated from input-output data. We demonstrate both the equivalence between the two approaches analytically and the feasibility of obtaining accurate Volterra models from insulin-glucose data generated from the compartmental models. 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 (e.g., glucose tolerance tests) that are necessary for the compartmental models proposed previously. These prior assumptions may lead to results that are improperly constrained or biased by preconceived (and possibly erroneous) notions-a risk that is avoided when we let the data guide the inductive selection of the appropriate model within the general class of Volterra-type models, as our simulation results suggest.

Nonconcave Utility Maximization in Locally Coupled Systems, With Applications to Wireless and Wireline Networks

Motivated by challenging resource allocation issues arising in large-scale wireless and wireline communication networks, we study distributed network utility maximization problems with a mixture of concave (e.g., best-effort throughputs) and nonconcave (e.g., voice/video streaming rates) utilities. In the first part of the paper, we develop our methodological framework in the context of a locally coupled networked system, where nodes represent agents that control a discrete local state. Each node has a possibly nonconcave local objective function, which depends on the local state of the node and the local states of its neighbors. The goal is to maximize the sum of the local objective functions of all nodes. We devise an iterative randomized algorithm, whose convergence and optimality properties follow from the classical framework of Markov Random Fields and Gibbs Measures via a judiciously selected neighborhood structure. The proposed algorithm is distributed, asynchronous, requires limited computational effort per node/iteration, and yields provable convergence in the limit. In order to demonstrate the scope of the proposed methodological framework, in the second part of the paper we show how the method can be applied to two different problems for which no distributed algorithm with provable convergence and optimality properties is available. Specifically, we describe how the proposed methodology provides a distributed mechanism for solving nonconcave utility maximization problems: 1) arising in OFDMA cellular networks, through power allocation and user assignment; 2) arising in multihop wireline networks, through explicit rate allocation. Several numerical experiments are presented to illustrate the convergence speed and performance of the proposed method.

Computational study of an augmented minimal model for glycaemia control

In this paper we introduce a new model structure for the metabolic effects of intravenous insulin on blood glucose in man and derive its parameter values from the widely used model of Sorensen. The proposed model attempts to combine the advantages of the existing comprehensive and minimal models. Validation of the new model is done through deriving equivalent nonparametric nonlinear models in the form of Principal Dynamic Modes. We show that the new structure can represent the insulin - glucose dynamics of healthy subjects as well as Type 1 and Type 2 diabetics, with appropriate adjustment in its parameters.

Throughput Optimal Scheduling Over Time-Varying Channels in the Presence of Heavy-Tailed Traffic

We study the problem of scheduling over time varying links in a network that serves both heavy-tailed and light tailed traffic. We consider a system consisting of two parallel queues, served by a single server. One of the queues receives heavy-tailed traffic (the heavy queue), and the other receives light-tailed traffic (the light queue). The queues are connected to the server through time-varying ON/OFF links, which model fading wireless channels. We first show that the policy that gives complete priority to the light-tailed traffic guarantees the best possible tail behavior of both queue backlog distributions, whenever the queues are stable. However, the priority policy is not throughput maximizing, and can cause undesirable instability effects in the heavy queue. Next, we study the class of throughput optimal max-weight-α scheduling policies. We discover a threshold phenomenon, and show that the steady state light queue backlog distribution is heavy-tailed for arrival rates above a threshold value, and light-tailed otherwise. We also obtain the exact tail coefficient of the light queue backlog distribution under max-weight-α scheduling. Finally, we study a log-max-weight scheduling policy, which is throughput optimal, and ensures that the light queue backlog distribution is light-tailed.

Queue length asymptotics for generalized max-weight scheduling in the presence of heavy-tailed traffic

We investigate the asymptotic behavior of the steady-state queue length distribution under generalized max-weight scheduling in the presence of heavy-tailed traffic. We consider a system consisting of two parallel queues, served by a single server. One of the queues receives heavy-tailed traffic, and the other receives light-tailed traffic. We study the class of throughput optimal max-weight-α scheduling policies, and derive an exact asymptotic characterization of the steady-state queue length distributions. In particular, we show that the tail of the light queue distribution is heavier than a power-law curve, whose tail coefficient we obtain explicitly. Our asymptotic characterization also shows that the celebrated max-weight scheduling policy leads to the worst possible tail of the light queue distribution, among all non-idling policies. Motivated by the above ‘negative’ result regarding the max-weight-α policy, we analyze a log-max-weight (LMW) scheduling policy. We show that the LMW policy guarantees an exponentially decaying light queue tail, while still being throughput optimal.

Inventory Pooling Under Heavy-Tailed Demand

Risk pooling has been studied extensively in the operations management literature as the basic driver behind strategies such as transshipment, manufacturing flexibility, component commonality, and drop shipping. This paper explores the benefit of risk pooling in the context of inventory management using the canonical model first studied in Eppen [Eppen GD (1979) Effects of centralization on expected costs in a multi-location newsboy problem. Management Sci. 25(5):498–501]. Specifically, we consider a single-period, multilocation newsvendor model, where n different locations face independent and identically distributed demands and linear holding and backorder costs. We show that Eppen’s celebrated result, i.e., that the expected cost savings from centralized inventory management scale with the square root of the number of locations, depends critically on the “light-tailed” nature of the demand uncertainty. In particular, we establish that the benefit from pooling relative to the decentralized case, in term...

Model Predictive Control of blood glucose in Type 1 diabetes: The Principal Dynamic Modes approach

This computational study demonstrates the efficacy of regulating blood glucose in Type 1 diabetics with a Model Predictive Control strategy, utilizing a nonparametric / Principal Dynamic Modes model. For this purpose, a stochastic glucose disturbance signal is introduced and a simple methodology for predicting its future values is developed. The results of our simulations confirm that the proposed algorithm achieves very good performance, is computationally efficient and avoids hypoglycaemic events.

Delay analysis of the Max-Weight policy under heavy-tailed traffic via fluid approximations

We consider a single-hop switched queueing network with a mix of heavy-tailed (i.e., arrival processes with infinite variance) and light-tailed traffic, and study the delay performance of the Max-Weight policy, known for its throughput optimality and asymptotic delay optimality properties. Classical results in queueing theory imply that heavy-tailed queues are delay unstable, i.e., they experience infinite expected delays in steady state. Thus, we focus on the impact of heavy-tailed traffic on the light-tailed queues, using delay stability as performance metric. Recent work has shown that this impact may come in the form of subtle rate-dependent phenomena, the stochastic analysis of which is quite cumbersome. Our goal is to show how fluid approximations can facilitate the delay analysis of the Max-Weight policy under heavy-tailed traffic. More specifically, we show how fluid approximations can be combined with renewal theory in order to prove delay instability results. Furthermore, we show how fluid approximations can be combined with stochastic Lyapunov theory in order to prove delay stability results. We illustrate the benefits of the proposed approach in two ways: (i) analytically, by providing a sharp characterization of the delay stability regions of networks with disjoint schedules, significantly generalizing previous results; (ii) computationally, through a Bottleneck Identification algorithm, which identifies (some) delay unstable queues by solving the fluid model of the network from certain initial conditions.