Optimizing resource allocation in next-generation optical access networks

To meet rapidly increasing traffic demands caused by the popularization of Internet and the spouting of bandwidth-demanding applications, Passive Optical Networks (PONs) exploit the potential capacities of optical fibers, and are becoming promising future-proof access network technologies. On the other hand, for a broader coverage area and higher data rate, integrated optical and wireless access is becoming a future trend for wireless access. This thesis investigates three next-generation access networks: Time Division Multiplexing (TDM) PONs, Wavelength Division Multiplexing (WDM) PONs, and WDM Radio-Over-Fiber (RoF) Picocellular networks. To address resource allocation problems in these three networks, this thesis first investigates respective characteristics of these networks, and then presents solutions to address respective challenging problems in these networks. In particular, three main problems are addressed: arbitrating time allocation among different applications to guarantee user quality of experience (QoE) in TDM PONs, scheduling wavelengths optimally in WDM PONs, and jointly allocating fiber and radio resources in WDM RoF Picocellular networks. In-depth theoretical analysis and extensive simulations have been performed in evaluating and demonstrating the performances of the proposed schemes

LIPIcs, Volume 244, ESA 2022, Complete Volume

LIPIcs, Volume 244, ESA 2022, Complete Volum

Evolutionary Solutions and Internet Applications for Algorithmic Game Theory

The growing pervasiveness of the internet has created a new class of algorithmic problems: those in which the strategic interaction of autonomous, self-interested entities must be accounted for. So motivated, we seek to (1) use game theoretic models and techniques to study practical problems in load balancing, data streams and internet traffic congestion, and (2) demonstrate the usefulness of evolutionary game theory's adaptive learning model as an analytical and evaluative tool.First we consider the evolutionary game theory concept of stochastic stability, and propose the price of stochastic anarchy as an alternative to the price of anarchy for quantifying the cost of having no central authority. Unlike Nash equilibria, stochastically stable states are the result of natural dynamics of large populations of computationally bounded agents, and are resilient to small perturbations from ideal play. To illustrate the utility of stochastic stability, we study the load balancing game on related machines, which has an unbounded price of anarchy, even in the case of two jobs and two machines. We show that in contrast, even in the general case, the price of stochastic anarchy is bounded.Next, we propose auction-based mechanisms for admission control of continuous queries to a Data Stream Management System. When submitting a query, each user also submits a bid: how much she is willing to pay for her query to run. Our mechanisms must admit queries and set payments in a way that maximizes system revenue while incentivizing customers to use the system honestly. We propose several manipulation-resistant payment mechanisms and prove that one guarantees a profit close to a standard profit benchmark, and the others perform well experimentally.Finally, we study the long standing problem of congestion control at bottleneck routers on the internet. We examine the effectiveness of commonly-used queuing policies when each network endpoint is self-interested and has no information about the other endpoints' actions or preferences. By employing evolutionary game theory, we find that while bottleneck routers face heavy congestion at stochastically stable states under policies being currently deployed, a practical policy that was recently proposed yields fair and efficient conditions with no congestion

Algorithms for Scheduling Problems

This edited book presents new results in the area of algorithm development for different types of scheduling problems. In eleven chapters, algorithms for single machine problems, flow-shop and job-shop scheduling problems (including their hybrid (flexible) variants), the resource-constrained project scheduling problem, scheduling problems in complex manufacturing systems and supply chains, and workflow scheduling problems are given. The chapters address such subjects as insertion heuristics for energy-efficient scheduling, the re-scheduling of train traffic in real time, control algorithms for short-term scheduling in manufacturing systems, bi-objective optimization of tortilla production, scheduling problems with uncertain (interval) processing times, workflow scheduling for digital signal processor (DSP) clusters, and many more

Nearly-optimal scheduling of users with Markovian time-varying transmission rates

We address the problem of developing a well-performing and implementable scheduler of users with wireless connections to the central controller, which arise in areas such as mobile data networks, heterogeneous networks, or vehicular communications systems. The main feature of such systems is that the connection quality of each user is time-varying, resulting in time-varying transmission rate corresponding to available channel states. We assume that this evolution is Markovian, relaxing the common but unrealistic assumption of stationary channels. We first focus on the three-state channel and study the optimal policy, showing that threshold policies (of giving higher priority to users with higher transmission rate) are not necessarily optimal. For the general channel we design a scheduler which generalizes the recently proposed Potential Improvement (PI) scheduler, and propose its two practical approximations, whose performance is analyzed and compared to existing alternative schedulers in a variety of simulation scenarios. We suggest and give evidence that the variant of PI which only relies on the steady-state distribution of the channel, performs extremely well, and therefore should be used for practical implementation