1,383 research outputs found
An efficient hybrid model and dynamic performance analysis for multihop wireless networks
Multihop wireless networks can be subjected to nonstationary phenomena due to a dynamic network topology and time varying traffic. However, the simulation techniques used to study multihop wireless networks focus on the steady-state performance even though transient or nonstationary periods will often occur. Moreover, the majority of the simulators suffer from poor scalability. In this paper, we develop an efficient performance modeling technique for analyzing the time varying queueing behavior of multihop wireless networks. The one-hop packet transmission (service) time is assumed to be deterministic, which could be achieved by contention-free transmission, or approximated in sparse or lightly loaded multihop wireless networks. Our model is a hybrid of time varying adjacency matrix and fluid flow based differential equations, which represent dynamic topology changes and nonstationary network queues, respectively. Numerical experiments show that the hybrid fluid based model can provide reasonably accurate results much more efficiently than standard simulators. Also an example application of the modeling technique is given showing the nonstationary network performance as a function of node mobility, traffic load and wireless link quality. © 2013 IEEE
Modeling Quality of Service Techniques for Packet-Switched Networks
Quality of service is the ability to provide different priorities to different applications, users or dataflows, or to guarantee a certain level of performance to a dataflow. The chapter uses timed Petri nets to model techniques that provide the quality of service in packet-switched networks and illustrates the behavior of developed models by performance characteristics of simple examples. These performance characteristics are obtained by discrete-event simulation of analyzed models
Applications of stochastic modeling in air traffic management:Methods, challenges and opportunities for solving air traffic problems under uncertainty
In this paper we provide a wide-ranging review of the literature on stochastic modeling applications within aviation, with a particular focus on problems involving demand and capacity management and the mitigation of air traffic congestion. From an operations research perspective, the main techniques of interest include analytical queueing theory, stochastic optimal control, robust optimization and stochastic integer programming. Applications of these techniques include the prediction of operational delays at airports, pre-tactical control of aircraft departure times, dynamic control and allocation of scarce airport resources and various others. We provide a critical review of recent developments in the literature and identify promising research opportunities for stochastic modelers within air traffic management
LETRIS: Staffing service systems by means of simulation
Purpose: This paper introduces a procedure for solving the staffing problem in a service system (i.e., determining the number of servers for each staffing period).
Design/methodology: The proposed algorithm combines the use of queueing theory to find an initial solution with the use of simulation to adjust the number of servers to meet previously specified target non-delay probabilities. The basic idea of the simulation phase of the procedure is to successively fix the number of servers from the first staffing period to the last, without backtracking.
Findings: Under the assumptions that the number of servers is not upper-bounded and there are no abandonments and, therefore, no retrials, the procedure converges in a finite number of iterations, regardless of the distributions of arrivals and services, and requires a reasonable amount of computing time.
Originality / value: The new procedure proposed in this paper is a systematic, robust way to find a good solution to a relevant problem in the field of service management and it is very easy to implement using no more than commonly accessible tools.Peer Reviewe
An optimal transportation routing approach using GIS-based dynamic traffic flows
This paper examines the value of real-time traffic information gathered through Geographic Information Systems for achieving an optimal vehicle routing within a dynamically stochastic transportation network. We present a systematic approach in determining the dynamically varying parameters and implementation attributes that were used for the development of a Web-based transportation routing application integrated with real-time GIS services. We propose and implement an optimal routing algorithm by modifying Dijkstra’s algorithm in order to incorporate stochastically changing traffic flows. We describe the significant features of our Web application in making use of the real-time dynamic traffic flow information from GIS services towards achieving total costs savings and vehicle usage reduction. These features help users and vehicle drivers in improving their service levels and productivity as the Web application enables them to interactively find the optimal path and in identifying destinations effectively
Learning While Scheduling in Multi-Server Systems with Unknown Statistics: MaxWeight with Discounted UCB
Multi-server queueing systems are widely used models for job scheduling in
machine learning, wireless networks, crowdsourcing, and healthcare systems.
This paper considers a multi-server system with multiple servers and multiple
types of jobs, where different job types require different amounts of
processing time at different servers. The goal is to schedule jobs on servers
without knowing the statistics of the processing times. To fully utilize the
processing power of the servers, it is known that one has to at least learn the
service rates of different job types on different servers. Prior works on this
topic decouple the learning and scheduling phases which leads to either
excessive exploration or extremely large job delays. We propose a new
algorithm, which combines the MaxWeight scheduling policy with discounted upper
confidence bound (UCB), to simultaneously learn the statistics and schedule
jobs to servers. We prove that under our algorithm the asymptotic average queue
length is bounded by one divided by the traffic slackness, which is order-wise
optimal. We also obtain an exponentially decaying probability tail bound for
any-time queue length. These results hold for both stationary and nonstationary
service rates. Simulations confirm that the delay performance of our algorithm
is several orders of magnitude better than previously proposed algorithms
Hybrid Satellite-Terrestrial Communication Networks for the Maritime Internet of Things: Key Technologies, Opportunities, and Challenges
With the rapid development of marine activities, there has been an increasing
number of maritime mobile terminals, as well as a growing demand for high-speed
and ultra-reliable maritime communications to keep them connected.
Traditionally, the maritime Internet of Things (IoT) is enabled by maritime
satellites. However, satellites are seriously restricted by their high latency
and relatively low data rate. As an alternative, shore & island-based base
stations (BSs) can be built to extend the coverage of terrestrial networks
using fourth-generation (4G), fifth-generation (5G), and beyond 5G services.
Unmanned aerial vehicles can also be exploited to serve as aerial maritime BSs.
Despite of all these approaches, there are still open issues for an efficient
maritime communication network (MCN). For example, due to the complicated
electromagnetic propagation environment, the limited geometrically available BS
sites, and rigorous service demands from mission-critical applications,
conventional communication and networking theories and methods should be
tailored for maritime scenarios. Towards this end, we provide a survey on the
demand for maritime communications, the state-of-the-art MCNs, and key
technologies for enhancing transmission efficiency, extending network coverage,
and provisioning maritime-specific services. Future challenges in developing an
environment-aware, service-driven, and integrated satellite-air-ground MCN to
be smart enough to utilize external auxiliary information, e.g., sea state and
atmosphere conditions, are also discussed
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