A Mechanism Design Approach to Bandwidth Allocation in Tactical Data Networks

The defense sector is undergoing a phase of rapid technological advancement, in the pursuit of its goal of information superiority. This goal depends on a large network of complex interconnected systems - sensors, weapons, soldiers - linked through a maze of heterogeneous networks. The sheer scale and size of these networks prompt behaviors that go beyond conglomerations of systems or `system-of-systems\u27. The lack of a central locus and disjointed, competing interests among large clusters of systems makes this characteristic of an Ultra Large Scale (ULS) system. These traits of ULS systems challenge and undermine the fundamental assumptions of today\u27s software and system engineering approaches. In the absence of a centralized controller it is likely that system users may behave opportunistically to meet their local mission requirements, rather than the objectives of the system as a whole. In these settings, methods and tools based on economics and game theory (like Mechanism Design) are likely to play an important role in achieving globally optimal behavior, when the participants behave selfishly. Against this background, this thesis explores the potential of using computational mechanisms to govern the behavior of ultra-large-scale systems and achieve an optimal allocation of constrained computational resources Our research focusses on improving the quality and accuracy of the common operating picture through the efficient allocation of bandwidth in tactical data networks among self-interested actors, who may resort to strategic behavior dictated by self-interest. This research problem presents the kind of challenges we anticipate when we have to deal with ULS systems and, by addressing this problem, we hope to develop a methodology which will be applicable for ULS system of the future. We build upon the previous works which investigate the application of auction-based mechanism design to dynamic, performance-critical and resource-constrained systems of interest to the defense community. In this thesis, we consider a scenario where a number of military platforms have been tasked with the goal of detecting and tracking targets. The sensors onboard a military platform have a partial and inaccurate view of the operating picture and need to make use of data transmitted from neighboring sensors in order to improve the accuracy of their own measurements. The communication takes place over tactical data networks with scarce bandwidth. The problem is compounded by the possibility that the local goals of military platforms might not be aligned with the global system goal. Such a scenario might occur in multi-flag, multi-platform military exercises, where the military commanders of each platform are more concerned with the well-being of their own platform over others. Therefore there is a need to design a mechanism that efficiently allocates the flow of data within the network to ensure that the resulting global performance maximizes the information gain of the entire system, despite the self-interested actions of the individual actors. We propose a two-stage mechanism based on modified strictly-proper scoring rules, with unknown costs, whereby multiple sensor platforms can provide estimates of limited precisions and the center does not have to rely on knowledge of the actual outcome when calculating payments. In particular, our work emphasizes the importance of applying robust optimization techniques to deal with the uncertainty in the operating environment. We apply our robust optimization - based scoring rules algorithm to an agent-based model framework of the combat tactical data network, and analyze the results obtained. Through the work we hope to demonstrate how mechanism design, perched at the intersection of game theory and microeconomics, is aptly suited to address one set of challenges of the ULS system paradigm - challenges not amenable to traditional system engineering approaches

An ABM java applet to explore the free market equality/efficiency tradeoff

SCOPUS: cp.kinfo:eu-repo/semantics/publishe

A complex system, agent based model for studying and improving the resilience of production and distribution networks

The very complexity and the extended reach of today’s globe-spanning supply chain networks, the low inventory levels and lack of redundancies required to achieve efficient operations expose businesses to a huge range of unexpected disruptions. This calls for building resilience in supply chains, which is not just recovery from the mishaps, but is a proactive, structured and integrated exploration of capabilities within the supply chain to resist and win against unforeseen happenings. Literature on supply chain and organisational resilience are informative in identifying resilience enhancing strategies and capabilities, but a detailed dynamic analysis of behaviour of the supply chain to understand the suitability of different resilience capabilities over time and under different scenarios is not carried out. The thesis addresses this gap by studying the internal decision making mechanisms, rules and control procedures through development of an agent-based model and its application to a paper tissue manufacturing supply chain. The model with a decentralised informational structure with informed and intelligent combination of push or pull type of replenishment strategy, flexibility, agility, redundancy and efficiency is found to enhance the resilience of the actual supply network in the face of large deviation of demand from forecasts. The effects of adopting several resilience improvement strategies in tandem or in isolation and the impact of applying different behavioural rules by different agents are studied in this thesis by carrying out numerical experimentation. The findings from the experiments suggest that, however flexible the resources are, however well-informed the different members are, however well-integrated the members are through coordination and communication, however wellequipped a supply chain is with mitigation and recovery capabilities the individual managerial judgements that can obtain a balance between various dimensions of performance (both global and local efficiency, quality and speed of responding to customer orders) and resilience (speedy reaction, maintaining buffers, flexibility in resource management) play the most important role in improving the resilience of the entire network. An important contribution of this thesis is to produce a conceptual framework for supply chain resilience. This framework is used to test the appropriateness of different resilience enhancement procedures. Another significant contribution of this thesis is to provide a theoretical template for further research in supply chain resilience. The template will guide development of effective procedures for managing different situations of uncertainty. By using complex systems modelling methods, such as multi-agent models described in the thesis, outcomes of the system under a significant range of possible agent behavioural rules and environmental events can be explored, and improved levels of functioning and of resilience can be found. Building such models as a means to understand and improve resilience of supply networks is a significant contribution.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp