Routing Games with Progressive Filling

Max-min fairness (MMF) is a widely known approach to a fair allocation of bandwidth to each of the users in a network. This allocation can be computed by uniformly raising the bandwidths of all users without violating capacity constraints. We consider an extension of these allocations by raising the bandwidth with arbitrary and not necessarily uniform time-depending velocities (allocation rates). These allocations are used in a game-theoretic context for routing choices, which we formalize in progressive filling games (PFGs). We present a variety of results for equilibria in PFGs. We show that these games possess pure Nash and strong equilibria. While computation in general is NP-hard, there are polynomial-time algorithms for prominent classes of Max-Min-Fair Games (MMFG), including the case when all users have the same source-destination pair. We characterize prices of anarchy and stability for pure Nash and strong equilibria in PFGs and MMFGs when players have different or the same source-destination pairs. In addition, we show that when a designer can adjust allocation rates, it is possible to design games with optimal strong equilibria. Some initial results on polynomial-time algorithms in this direction are also derived

Fair Resource Allocation in Macroscopic Evacuation Planning Using Mathematical Programming: Modeling and Optimization

Evacuation is essential in the case of natural and manmade disasters such as hurricanes, nuclear disasters, fire accidents, and terrorism epidemics. Random evacuation plans can increase risks and incur more losses. Hence, numerous simulation and mathematical programming models have been developed over the past few decades to help transportation planners make decisions to reduce costs and protect lives. However, the dynamic transportation process is inherently complex. Thus, modeling this process can be challenging and computationally demanding. The objective of this dissertation is to build a balanced model that reflects the realism of the dynamic transportation process and still be computationally tractable to be implemented in reality by the decision-makers. On the other hand, the users of the transportation network require reasonable travel time within the network to reach their destinations. This dissertation introduces a novel framework in the fields of fairness in network optimization and evacuation to provide better insight into the evacuation process and assist with decision making. The user of the transportation network is a critical element in this research. Thus, fairness and efficiency are the two primary objectives addressed in the work by considering the limited capacity of roads of the transportation network. Specifically, an approximation approach to the max-min fairness (MMF) problem is presented that provides lower computational time and high-quality output compared to the original algorithm. In addition, a new algorithm is developed to find the MMF resource allocation output in nonconvex structure problems. MMF is the fairness policy used in this research since it considers fairness and efficiency and gives priority to fairness. In addition, a new dynamic evacuation modeling approach is introduced that is capable of reporting more information about the evacuees compared to the conventional evacuation models such as their travel time, evacuation time, and departure time. Thus, the contribution of this dissertation is in the two areas of fairness and evacuation. The first part of the contribution of this dissertation is in the field of fairness. The objective in MMF is to allocate resources fairly among multiple demands given limited resources while utilizing the resources for higher efficiency. Fairness and efficiency are contradicting objectives, so they are translated into a bi-objective mathematical programming model and solved using the ϵ-constraint method, introduced by Vira and Haimes (1983). Although the solution is an approximation to the MMF, the model produces quality solutions, when ϵ is properly selected, in less computational time compared to the progressive-filling algorithm (PFA). In addition, a new algorithm is developed in this research called the θ progressive-filling algorithm that finds the MMF in resource allocation for general problems and works on problems with the nonconvex structure problems. The second part of the contribution is in evacuation modeling. The common dynamic evacuation models lack a piece of essential information for achieving fairness, which is the time each evacuee or group of evacuees spend in the network. Most evacuation models compute the total time for all evacuees to move from the endangered zone to the safe destination. Lack of information about the users of the transportation network is the motivation to develop a new optimization model that reports more information about the users of the network. The model finds the travel time, evacuation time, departure time, and the route selected for each group of evacuees. Given that the travel time function is a non-linear convex function of the traffic volume, the function is linearized through a piecewise linear approximation. The developed model is a mixed-integer linear programming (MILP) model with high complexity. Hence, the model is not capable of solving large scale problems. The complexity of the model was reduced by introducing a linear programming (LP) version of the full model. The complexity is significantly reduced while maintaining the exact output. In addition, the new θ-progressive-filling algorithm was implemented on the evacuation model to find a fair and efficient evacuation plan. The algorithm is also used to identify the optimal routes in the transportation network. Moreover, the robustness of the evacuation model was tested against demand uncertainty to observe the model behavior when the demand is uncertain. Finally, the robustness of the model is tested when the traffic flow is uncontrolled. In this case, the model's only decision is to distribute the evacuees on routes and has no control over the departure time

Developing a medical software to enhance patient participation

Mental health disorder is a frequent issue among cancer patients. It is estimated that in about 30% of cancer patients, psychological issues are undetected. Psychooncology is a subdomain of psychology, which studies cancer related psychological issues and, hence, develop appropriate treatments. With the help of screening instruments like the Distress Thermometer, patients are rated according to their mental state. The result of the screening indicates, whether a patient needs psychological treatment or not. However, in most medical facilities this screening is processed using paper-based questionnaires, which complicates the treatment. This thesis aims for enhancing the screening process as well as the overall psychological treatment with a newly developed mobile application Feelback. The mentioned application uses patient participation principles by applying the latter. Patients shall feel more involved in the psychological treatment process. This results in patients that take a more active role in making decisions related to their treatment. Moreover, sophisticated gamification concepts guarantee long-term motivated users. From the medical facility’s point of view, screened patients are evaluated in an automated manner, which, in term, saves time and money. In addition, Feelback makes it easier to document psychological treatments. At the current state of development, further steps should focus on user acceptance testing, in order to verify, whether the mentioned concepts work as intended

Enhanced adaptive RTCP-based inter-destination multimedia synchronization approach for distributed applications

[EN] Newer social multimedia applications, such as Social TV or networked multi-player games, enable independent groups (or clusters) of users to interact among themselves and share services within the context of simultaneous media content consumption. In such scenarios, concurrently synchronized playout points must be ensured so as not to degrade the user experience on such interaction. We refer to this process as Inter-Destination Multimedia Synchronization (IDMS). This paper presents the design, implementation and evaluation of an evolved version of an RTCP-based IDMS approach, including an Adaptive Media Playout (AMP) scheme that aims to dynamically and smoothly adjust the playout timing of each one of the geographically distributed consumers in a specific cluster if an allowable asynchrony threshold between their playout states is exceeded. For that purpose, we previously had also to develop a full implementation of RTP/RTCP protocols for NS-2, in which we included the IDMS approach as an optional functionality. Simulation results prove the feasibility of such IDMS and AMP proposals, by adopting several dynamic master reference selection policies, to maintain an overall synchronization status (within allowable limits) in each cluster of participants, while minimizing the occurrence of long-term playout discontinuities (such as skips/pauses) which are subjectively more annoying and less tolerable to users than small variations in the media playout rate.This work has been financed, partially, by Universitat Politecnica de Valencia (UPV), under its R&D Support Program in PAID-05-11-002-331 Project and in PAID-01-10. Authors also would like to thank the anonymous reviewers that helped to significantly improve the quality of the paper with their constructive comments.Montagud, M.; Boronat, F. (2012). Enhanced adaptive RTCP-based inter-destination multimedia synchronization approach for distributed applications. Computer Networks. 56(12):2912-2933. https://doi.org/10.1016/j.comnet.2012.05.00329122933561

Spartan Daily, December 14, 1959

Volume 47, Issue 54https://scholarworks.sjsu.edu/spartandaily/3970/thumbnail.jp

Elastic Multi-resource Network Slicing: Can Protection Lead to Improved Performance?

In order to meet the performance/privacy requirements of future data-intensive mobile applications, e.g., self-driving cars, mobile data analytics, and AR/VR, service providers are expected to draw on shared storage/computation/connectivity resources at the network "edge". To be cost-effective, a key functional requirement for such infrastructure is enabling the sharing of heterogeneous resources amongst tenants/service providers supporting spatially varying and dynamic user demands. This paper proposes a resource allocation criterion, namely, Share Constrained Slicing (SCS), for slices allocated predefined shares of the network's resources, which extends the traditional alpha-fairness criterion, by striking a balance among inter- and intra-slice fairness vs. overall efficiency. We show that SCS has several desirable properties including slice-level protection, envyfreeness, and load driven elasticity. In practice, mobile users' dynamics could make the cost of implementing SCS high, so we discuss the feasibility of using a simpler (dynamically) weighted max-min as a surrogate resource allocation scheme. For a setting with stochastic loads and elastic user requirements, we establish a sufficient condition for the stability of the associated coupled network system. Finally, and perhaps surprisingly, we show via extensive simulations that while SCS (and/or the surrogate weighted max-min allocation) provides inter-slice protection, they can achieve improved job delay and/or perceived throughput, as compared to other weighted max-min based allocation schemes whose intra-slice weight allocation is not share-constrained, e.g., traditional max-min or discriminatory processor sharing

Risk-Aware Planning for Sensor Data Collection

With the emergence of low-cost unmanned air vehicles, civilian and military organizations are quickly identifying new applications for affordable, large-scale collectives to support and augment human efforts via sensor data collection. In order to be viable, these collectives must be resilient to the risk and uncertainty of operating in real-world environments. Previous work in multi-agent planning has avoided planning for the loss of agents in environments with risk. In contrast, this dissertation presents a problem formulation that includes the risk of losing agents, the effect of those losses on the mission being executed, and provides anticipatory planning algorithms that consider risk. We conduct a thorough analysis of the effects of risk on path-based planning, motivating new solution methods. We then use hierarchical clustering to generate risk-aware plans for a variable number of agents, outperforming traditional planning methods. Next, we provide a mechanism for distributed negotiation of stable plans, utilizing coalitional game theory to provide cost allocation methods that we prove to be fair and stable. Centralized planning with redundancy is then explored, planning for parallel task completion to mitigate risk and provide further increased expected value. Finally, we explore the role of cost uncertainty as additional source of risk, using bi-objective optimization to generate sets of alternative plans. We demonstrate the capability of our algorithms on randomly generated problem instances, showing an improvement over traditional multi-agent planning methods as high as 500% on very large problem instances