Resource and Bandwidth Allocation in Hybrid Wireless Mobile Networks

In the lead up to the implementation of 802.16 and 4G wireless networks, there have been many proposals for addition of multi-hop MANET zones or relay stations in order to cut the cost of building a new backbone infrastructure from the ground up. These types of Hybrid Wireless Networks will certainly be a part of wireless network architecture in the future, and as such, simple problems such as resource allocation must be explored to maximize their potential. This study explores the resource allocation problem in three distinct ways. First, this study highlights two existing backbone architectures: max-coverage and max-resource, and how hybridization will affect bandwidth allocation, with special emphasis on OFDM-TMA wireless networks. Secondly, because of the different goals of these types of networks, the addition of relay stations or MANET zones will affect resource availability differently, and I will show how the addition of relay stations impacts the backbone network. Finally, I will discuss specific allocation algorithms and policies such as top-down, bottom-up, and auction-based allocation, and how each kind of allocation will maximize the revenue of both the backbone network as well as the mobile subscribers while maintaining a minimum Quality of Service (or fairness). Each of these approaches has merit in different hybrid wireless systems, and I will summarize the benefits of each in a study of a network system with a combination of the elements discussed in the previous chapters

Planetary Scale Data Storage

The success of virtualization and container-based application deployment has fundamentally changed computing infrastructure from dedicated hardware provisioning to on-demand, shared clouds of computational resources. One of the most interesting effects of this shift is the opportunity to localize applications in multiple geographies and support mobile users around the globe. With relatively few steps, an application and its data systems can be deployed and scaled across continents and oceans, leveraging the existing data centers of much larger cloud providers. The novelty and ease of a global computing context means that we are closer to the advent of an Oceanstore, an Internet-like revolution in personalized, persistent data that securely travels with its users. At a global scale, however, data systems suffer from physical limitations that significantly impact its consistency and performance. Even with modern telecommunications technology, the latency in communication from Brazil to Japan results in noticeable synchronization delays that violate user expectations. Moreover, the required scale of such systems means that failure is routine. To address these issues, we explore consistency in the implementation of distributed logs, key/value databases and file systems that are replicated across wide areas. At the core of our system is hierarchical consensus, a geographically-distributed consensus algorithm that provides strong consistency, fault tolerance, durability, and adaptability to varying user access patterns. Using hierarchical consensus as a backbone, we further extend our system from data centers to edge regions using federated consistency, an adaptive consistency model that gives satellite replicas high availability at a stronger global consistency than existing weak consistency models. In a deployment of 105 replicas in 15 geographic regions across 5 continents, we show that our implementation provides high throughput, strong consistency, and resiliency in the face of failure. From our experimental validation, we conclude that planetary-scale data storage systems can be implemented algorithmically without sacrificing consistency or performance

Goal Reasoning: Papers from the ACS Workshop

This technical report contains the 14 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2015 Conference on Advances in Cognitive Systems (ACS-15) in Atlanta, Georgia on 28 May 2015. This is the fourth in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy; the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012; and the third was the Goal Reasoning Workshop at ACS-13 in Baltimore, Maryland in December 2013

A Survey of Stochastic and Gazetteer Based Approaches for Named Entity Recognition

Abstract The task of identifying proper names of people, organizations, locations, or other entities is a subtask of information extraction from natural language documents. This paper presents a survey of techniques and methodologies that are currently being explored to solve this difficult subtask. After a brief review of the difficulties and challenges of the task, as well as a look at previous conventional approaches, the focus will shift to a comparison of stochastic and gazetteer based approaches. Several machine-learning approaches are identified and explored, as well as a discussion of knowledge acquisition relevant to recognition. This paper will show that applications that require named entity recognition will be served best by some combination of knowledgebased and non-deterministic approaches