An analytical packet/flow-level modelling approach for wireless LANs with Quality-of-Service support

We present an analytical packet/flow-level modelling approach for the performance analysis of IEEE 802.11e WLAN, where we explicitly take into account QoS differentiation mechanisms based on minimum contention window size values and Arbitration InterFrame Space (AIFS) values, as included in the Enhanced Distributed Channel Access (EDCA) protocol of the 802.11e standard. We first enhance the packet-level approach previously used for best-effort WLANs to include traffic classes with different QoS requirements. The packet-level model approach yields service weights that discriminate among traffic classes. From these observations, the packet/flow-level model for 802.11e is the \textit{generalized} discriminatory processor-sharing (GDPS) queueing model where the state-dependent system capacity is distributed among active traffic classes according to state-dependent priority weights. Extensive simulations show that the discriminatory processor-sharing model closely represents the flow behavior of 802.11e

A fluid system with coupled input and output, and its application to bottlenecks in ad hoc networks.

This paper studies a fluid queue with coupled input and output. Flows arrive according to a Poisson process, and when n flows are present, each of them transmits traffic into the queue at a rate c/(n + 1), where the remaining c/(n + 1) is used to serve the queue. We assume exponentially distributed flow sizes, so that the queue under consideration can be regarded as a system with Markov fluid input. The rationale behind studying this queue lies in ad hoc networks: bottleneck links have roughly this type of sharing policy. We consider four performance metrics: (i) the stationary workload of the queue, (ii) the queueing delay, i.e., the delay of

Burstiness predictions based on rough network traffic measurements

To dimension network links, such that they will not become QoS bottle- necks, the peak rate on these links should be known. To measure these peaks on sufficiently small time scales, special measurement tools are needed. Such tools can be quite expensive and complex. Therefore network operators often rely on more cheap, standard tools, like MRTG, which were designed to measure average traffic rates (m) on time scales such as 5 minutes. For estimating the peak traffic rate (p), operators often use simple rules, such as p = α · m. In this paper we describe measurements that we have performed to investigate how well this rule describes the relation between peak and average traffic rate. In addition, we pro- pose some more advanced rules, and compare these to the simple rule mentioned above. The analyses of our measurements, which have been performed on differ- ent kinds of networks, show that our advanced rules more adequately describe the relation between peak and average traffic rate

Performance analysis of differentiated resource-sharing in a wireless ad-hoc network

In this paper we model and analyze a relay node in a wireless ad-hoc network; the capacity available at this node is used to both transmit traffic from the source nodes (towards the relay node), and to serve traffic at the relay node (so that it can be forwarded to successor nodes). Clearly, when a specific node is used more heavily than others, it is prone to becoming a performance bottleneck. In this paper we consider the situation that the relay node obtains a share of the capacity that is m times as large as the share that each source node receives. The main performance metrics considered are the workload at the relay node and the average overall flow transfer time, i.e., the average time required to transmit a flow from a source node via the relay node to the destination. Our aim is to find expressions for these performance metrics for a general resource-sharing ratio m, as well as a general flow-size distribution. The analysis consists of the following steps. First, for the special case of exponential flow sizes we analyze the source-node dynamics, as well as the workload at the relay node by a fluid-flow queueing model. Then we observe from extensive numerical experimentation over a broad set of parameter values that the distribution of the number of active source nodes is actually insensitive to the flow-size distribution. Using this remarkable (empirical) result as an approximation assumption, we obtain explicit expressions for both the mean workload at the relay node and the overall flow transfer time, both for general flow-size distributions

Performance analysis of wireless LANs: an integrated packet/flow level approach

In this paper we present an integrated packet/flow level modelling approach for analysing flow throughputs and transfer times in IEEE 802.11 WLANs. The packet level model captures the statistical characteristics of the transmission of individual packets at the MAC layer, while the flow level model takes into account the system dynamics due to the initiation and completion of data flow transfers. The latter model is a processor sharing type of queueing model reflecting the IEEE 802.11 MAC design principle of distributing the transmission capacity fairly among the active flows. The resulting integrated packet/flow level model is analytically tractable and yields a simple approximation for the throughput and flow transfer time. Extensive simulations show that the approximation is very accurate for a wide range of parameter settings. In addition, the simulation study confirms the attractive property following from our approximation that the expected flow transfer delay is insensitive to the flow size distribution (apart from its mean)

Performance modeling of a bottleneck node in an IEEE 802.11 ad-hoc network

This paper presents a performance analysis of wireless ad-hoc networks, with IEEE 802.11 as the underlying Wireless LAN technology. WLAN has, due to the fair radio resource sharing at the MAC-layer, the tendency to share the capacity equally amongst the active nodes, irrespective of their loads. An inherent drawback of this sharing policy is that a node that serves as a relay-node for multiple flows is likely to become a bottleneck. This paper proposes to model such a bottleneck by a fluid-flow model. Importantly, this is a model at the flow-level: flows arrive at the bottleneck node, and are served according to the sharing policy mentioned above. Assuming Poisson initiations of new flow transfers, we obtain insightful, robust, and explicit expressions for characteristics related to the overall flow transfer time, the buffer occupancy, and the packet delay at the bottleneck node. The analysis is enabled by a translation of the buffer dynamics at the bottleneck node in terms of an M/G/1 queueing model. We conclude the paper by an assessment of the impact of alternative sharing policies (which can be obtained by the IEEE 802.11E version), in order to improve the performance of the bottleneck

Distributional Multi-Objective Decision Making

For effective decision support in scenarios with conflicting objectives, sets of potentially optimal solutions can be presented to the decision maker. We explore both what policies these sets should contain and how such sets can be computed efficiently. With this in mind, we take a distributional approach and introduce a novel dominance criterion relating return distributions of policies directly. Based on this criterion, we present the distributional undominated set and show that it contains optimal policies otherwise ignored by the Pareto front. In addition, we propose the convex distributional undominated set and prove that it comprises all policies that maximise expected utility for multivariate risk-averse decision makers. We propose a novel algorithm to learn the distributional undominated set and further contribute pruning operators to reduce the set to the convex distributional undominated set. Through experiments, we demonstrate the feasibility and effectiveness of these methods, making this a valuable new approach for decision support in real-world problems.Comment: Accepted at IJCAI 202

A Practical Guide to Multi-Objective Reinforcement Learning and Planning

Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems

LNCS

We study turn-based stochastic zero-sum games with lexicographic preferences over reachability and safety objectives. Stochastic games are standard models in control, verification, and synthesis of stochastic reactive systems that exhibit both randomness as well as angelic and demonic non-determinism. Lexicographic order allows to consider multiple objectives with a strict preference order over the satisfaction of the objectives. To the best of our knowledge, stochastic games with lexicographic objectives have not been studied before. We establish determinacy of such games and present strategy and computational complexity results. For strategy complexity, we show that lexicographically optimal strategies exist that are deterministic and memory is only required to remember the already satisfied and violated objectives. For a constant number of objectives, we show that the relevant decision problem is in NP∩coNP , matching the current known bound for single objectives; and in general the decision problem is PSPACE -hard and can be solved in NEXPTIME∩coNEXPTIME . We present an algorithm that computes the lexicographically optimal strategies via a reduction to computation of optimal strategies in a sequence of single-objectives games. We have implemented our algorithm and report experimental results on various case studies