Improving Network Performance Through Endpoint Diagnosis And Multipath Communications

Components of networks, and by extension the internet can fail. It is, therefore, important to find the points of failure and resolve existing issues as quickly as possible. Resolution, however, takes time and its important to maintain high quality of service (QoS) for existing clients while it is in progress. In this work, our goal is to provide clients with means of avoiding failures if/when possible to maintain high QoS while enabling them to assist in the diagnosis process to speed up the time to recovery. Fixing failures relies on first detecting that there is one and then identifying where it occurred so as to be able to remedy it. We take a two-step approach in our solution. First, we identify the entity (Client, Server, Network) responsible for the failure. Next, if a failure is identified as network related additional algorithms are triggered to detect the device responsible. To achieve the first step, we revisit the question: how much can you infer about a failure using TCP statistics collected at one of the endpoints in a connection? Using an agent that captures TCP statistics at one of the end points we devise a classification algorithm that identifies the root cause of failures. Using insights derived from this classification algorithm we identify dominant TCP metrics that indicate where/why problems occur. If/when a failure is identified as a network related problem, the second step is triggered, where the algorithm uses additional information that is collected from ``failed\u27\u27 connections to identify the device which resulted in the failure. Failures are also disruptive to user\u27s performance. Resolution may take time. Therefore, it is important to be able to shield clients from their effects as much as possible. One option for avoiding problems resulting from failures is to rely on multiple paths (they are unlikely to go bad at the same time). The use of multiple paths involves both selecting paths (routing) and using them effectively. The second part of this thesis explores the efficacy of multipath communication in such situations. It is expected that multi-path communications have monetary implications for the ISP\u27s and content providers. Our solution, therefore, aims to minimize such costs to the content providers while significantly improving user performance

Next generation communications satellites: multiple access and network studies

Efficient resource allocation and network design for satellite systems serving heterogeneous user populations with large numbers of small direct-to-user Earth stations are discussed. Focus is on TDMA systems involving a high degree of frequency reuse by means of satellite-switched multiple beams (SSMB) with varying degrees of onboard processing. Algorithms for the efficient utilization of the satellite resources were developed. The effect of skewed traffic, overlapping beams and batched arrivals in packet-switched SSMB systems, integration of stream and bursty traffic, and optimal circuit scheduling in SSMB systems: performance bounds and computational complexity are discussed

Modelling adaptive routing in Wide Area Networks

Bibliography: leaves 132-138.This study investigates the modelling of adative routing algorithms with specific reference to the algorithm of an existing Wide Area Network (WAN). Packets in the network are routed at each node on the basis of routing tables which contain internal and external delays for each route from the node. The internal delay on a route represents the time that packets queued for transmission will have to wait before being transmitted, while the external delay on a route represents the delay to other nodes via that route. Several modelling methods are investigated and compared for the purpose of identifying the most appropriate and applicable technique. A model of routing in the WAN using an analytic technique is described. The hypothesis of this study is that dynamic routing can be modelled as a sequence of models exhibiting fixed routing. The modelling rationale is that a series of analytic models is run and solved. The routing algorithm of the WAN studied is such that, if viewed at any time instant, the network is one with static routing and no buffer overflow. This characteristic, together with a real time modelling requirement, influences the modelling technique which is applied. Each model represents a routing update interval and a multiclass open queueing network is used to solve the model during a particular interval. Descriptions of the design and implementation of X wan, an X Window based modelling system, are provided. A feature of the modelling system is that it provides a Graphical User Interface (GUI), allowing interactive network specification and the direct observation of network routing through the medium of this interface. Various applications of the modelling system are presented, and overall network behaviour is examined. Experimentation with the routing algorithm is conducted, and (tentative) recommendations are made on ways in which network performance could be improved. A different routing algorithm is also implemented, for the purpose of comparison and to demonstrate the ease with which this can be affected

Load Balancing Algorithms for Parallel Spatial Join on HPC Platforms

Geospatial datasets are growing in volume, complexity, and heterogeneity. For efficient execution of geospatial computations and analytics on large scale datasets, parallel processing is necessary. To exploit fine-grained parallel processing on large scale compute clusters, partitioning of skewed datasets in a load-balanced way is challenging. The workload in spatial join is data dependent and highly irregular. Moreover, wide variation in the size and density of geometries from one region of the map to another, further exacerbates the load imbalance. This dissertation focuses on spatial join operation used in Geographic Information Systems (GIS) and spatial databases, where the inputs are two layers of geospatial data, and the output is a combination of the two layers according to join predicate.This dissertation introduces a novel spatial data partitioning algorithm geared towards load balancing the parallel spatial join processing. Unlike existing partitioning techniques, the proposed partitioning algorithm divides the spatial join workload instead of partitioning the individual datasets separately to provide better load-balancing. This workload partitioning algorithm has been evaluated on a high-performance computing system using real-world datasets. An intermediate output-sensitive duplication avoidance technique is proposed that decreases the external memory space requirement for storing spatial join candidates across the partitions. GPU acceleration is used to further reduce the spatial partitioning runtime. For dynamic load balancing in spatial join, a novel framework for fine-grained work stealing is presented. This framework is efficient and NUMA-aware. Performance improvements are demonstrated on shared and distributed memory architectures using threads and message passing. Experimental results show effective mitigation of data skew. The framework supports a variety of spatial join predicates and spatial overlay using partitioned and un-partitioned datasets

Designing, Implementing, and Testing Hardware for Cybersecurity

Cybersecurity is one of the key issues facing the world today. With an ever-increasing number of devices connected across the internet, the need to secure all these different devices against potential attackers is an endless effort. This thesis is focussed on the most promising new developments in the hardware aspect of this battle for security. The first section of the thesis looks at what is the current state of the art when it comes to hardware security primitives, with a focus on random number generators and Physically Unclonable Functions (PUF). The strengths and weakness of the current implementations of these systems are analysed so that the areas which are most in need of improvement can be highlighted. The second major section of this thesis is looking to improve how random numbers are generated, which is essential for many current security systems. True random number generators have been presented as a potential solution to this problem but improvements in output bit rate, power consumption, and design complexity must be made. In this work we present a novel and experimentally verified true random number generator that exclusively uses conventional CMOS technology as well as offering key improvements over previous designs in complexity, output bit rate, and power consumption. It uses the inherent randomness of telegraph noise in the channel current of a single CMOS transistor as an entropy source. For the first time, multi-level and abnormal telegraph noise can be utilised, which greatly reduces device selectivity and offers much greater bit rates. The design is verified using a breadboard and FPGA proof of concept circuit and passes all 15 of the NIST randomness tests without any need for post-processing of the generated bitstream. The design also shows resilience against machine learning attacks performed by an LSTM neural network. The third major section describes the development of a novel PUF concept, which offers a new approach to authentication, allowing low power devices to be included in existing networks without compromising overall security. The new PUF concept introduces time dependence to vastly increase the efficiency of entropy source usage, when compared with a traditional PUF. This new PUF also introduces a probability-based model which greatly reduces the required server memory for Challenge Response Pair (CRP) storage when large numbers of CRPs are used. The concept is verified experimentally on nano-scale CMOS technology as well as through simulation and a proof-of-concept circuit. These combined benefits bring the PUF concept much closer to being a viable solution for widespread cybersecurity applications