Matching Items (18)
Description
The primary function of the medium access control (MAC) protocol is managing access to a shared communication channel. From the viewpoint of transmitters, the MAC protocol determines each transmitter's persistence, the fraction of time it is permitted to spend transmitting. Schedule-based schemes implement stable persistences, achieving low variation in delay and throughput, and sometimes bounding maximum delay. However, they adapt slowly, if at all, to changes in the network. Contention-based schemes are agile, adapting quickly to changes in perceived contention, but suffer from short-term unfairness, large variations in packet delay, and poor performance at high load. The perfect MAC protocol, it seems, embodies the strengths of both contention- and schedule-based approaches while avoiding their weaknesses. This thesis culminates in the design of a Variable-Weight and Adaptive Topology Transparent (VWATT) MAC protocol. The design of VWATT first required answers for two questions: (1) If a node is equipped with schedules of different weights, which weight should it employ? (2) How is the node to compute the desired weight in a network lacking centralized control? The first question is answered by the Topology- and Load-Aware (TLA) allocation which defines target persistences that conform to both network topology and traffic load. Simulations show the TLA allocation to outperform IEEE 802.11, improving on the expectation and variation of delay, throughput, and drop rate. The second question is answered in the design of an Adaptive Topology- and Load-Aware Scheduled (ATLAS) MAC that computes the TLA allocation in a decentralized and adaptive manner. Simulation results show that ATLAS converges quickly on the TLA allocation, supporting highly dynamic networks. With these questions answered, a construction based on transversal designs is given for a variable-weight topology transparent schedule that allows nodes to dynamically and independently select weights to accommodate local topology and traffic load. The schedule maintains a guarantee on maximum delay when the maximum neighbourhood size is not too large. The schedule is integrated with the distributed computation of ATLAS to create VWATT. Simulations indicate that VWATT offers the stable performance characteristics of a scheduled MAC while adapting quickly to changes in topology and traffic load.
ContributorsLutz, Jonathan (Author) / Colbourn, Charles J (Thesis advisor) / Syrotiuk, Violet R. (Thesis advisor) / Konjevod, Goran (Committee member) / Lloyd, Errol L. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Time series analysis of dynamic networks is an important area of study that helps in predicting changes in networks. Changes in networks are used to analyze deviations in the network characteristics. This analysis helps in characterizing any network that has dynamic behavior. This area of study has applications in many domains such as communication networks, climate networks, social networks, transportation networks, and biological networks. The aim of this research is to analyze the structural characteristics of such dynamic networks. This thesis examines tools that help to analyze the structure of the networks and explores a technique for computation and analysis of a large climate dataset. The computations for analyzing the structural characteristics are done in a computing cluster and there is a linear speed up in computation time compared to a single-core computer. As an application, a large sea ice concentration anomaly dataset is analyzed. The large dataset is used to construct a correlation based graph. The results suggest that the climate data has the characteristics of a small-world graph.
ContributorsParamasivam, Kumaraguru (Author) / Colbourn, Charles J (Thesis advisor) / Sen, Arunabhas (Committee member) / Syrotiuk, Violet R. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Mobile ad hoc networks (MANETs) have attracted attention for mission critical applications. This dissertation investigates techniques of statistical monitoring and control for overhead reduction in a proactive MANET routing protocol. Proactive protocols transmit overhead periodically. Instead, we propose that the local conditions of a node should determine this transmission decision. While the goal is to minimize overhead, a balance in the amount of overhead transmitted and the performance achieved is required. Statistical monitoring consists of techniques to determine if a characteristic has shifted away from an in-control state. A basic tool for monitoring is a control chart, a time-oriented representation of the characteristic. When a sample deviates outside control limits, a significant change has occurred and corrective actions are required to return to the in-control state. We investigate the use of statistical monitoring of local conditions in the Optimized Link State Routing (OLSR) protocol. Three versions are developed. In A-OLSR, each node uses a Shewhart chart to monitor betweenness of its two-hop neighbourhood. Betweenness is a social network metric that measures a node's influence; betweenness is larger when a node has more influence. Changes in topology are associated with changes in betweenness. We incorporate additional local node conditions including speed, density, packet arrival rate, and number of flows it forwards in A+-OLSR. Response Surface Methodology (RSM) is used to optimize timer values. As well, the Shewhart chart is replaced by an Exponentially Weighted Moving Average (EWMA) chart, which is more sensitive to small changes in the characteristic. It is known that control charts do not work as well in the presence of correlation. Hence, in A*-OLSR the autocorrelation in the time series is removed and an Auto-Regressive Integrated Moving Average (ARIMA) model found; this removes the dependence on node speed. A*-OLSR also extends monitoring to two characteristics concurrently using multivariate cumulative sum (MCUSUM) charts. The protocols are evaluated in simulation, and compared to OLSR and its variants. The techniques for statistical monitoring and control are general and have great potential to be applied to the adaptive control of many network protocols.
ContributorsShaukat, Kahkashan (Author) / Syrotiuk, Violet R. (Thesis advisor) / Colbourn, Charles J (Committee member) / Montgomery, Douglas C. (Committee member) / Sarjoughian, Hessam S. (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2012
Description
Broadcast Encryption is the task of cryptographically securing communication in a broadcast environment so that only a dynamically specified subset of subscribers, called the privileged subset, may decrypt the communication. In practical applications, it is desirable for a Broadcast Encryption Scheme (BES) to demonstrate resilience against attacks by colluding, unprivileged subscribers. Minimal Perfect Hash Families (PHFs) have been shown to provide a basis for the construction of memory-efficient t-resilient Key Pre-distribution Schemes (KPSs) from multiple instances of 1-resilient KPSs. Using this technique, the task of constructing a large t-resilient BES is reduced to finding a near-minimal PHF of appropriate parameters. While combinatorial and probabilistic constructions exist for minimal PHFs with certain parameters, the complexity of constructing them in general is currently unknown. This thesis introduces a new type of hash family, called a Scattering Hash Family (ScHF), which is designed to allow for the scalable and ingredient-independent design of memory-efficient BESs for large parameters, specifically resilience and total number of subscribers. A general BES construction using ScHFs is shown, which constructs t-resilient KPSs from other KPSs of any resilience ≤w≤t. In addition to demonstrating how ScHFs can be used to produce BESs , this thesis explores several ScHF construction techniques. The initial technique demonstrates a probabilistic, non-constructive proof of existence for ScHFs . This construction is then derandomized into a direct, polynomial time construction of near-minimal ScHFs using the method of conditional expectations. As an alternative approach to direct construction, representing ScHFs as a k-restriction problem allows for the indirect construction of ScHFs via randomized post-optimization. Using the methods defined, ScHFs are constructed and the parameters' effects on solution size are analyzed. For large strengths, constructive techniques lose significant performance, and as such, asymptotic analysis is performed using the non-constructive existential results. This work concludes with an analysis of the benefits and disadvantages of BESs based on the constructed ScHFs. Due to the novel nature of ScHFs, the results of this analysis are used as the foundation for an empirical comparison between ScHF-based and PHF-based BESs . The primary bases of comparison are construction efficiency, key material requirements, and message transmission overhead.
ContributorsO'Brien, Devon James (Author) / Colbourn, Charles J (Thesis advisor) / Bazzi, Rida (Committee member) / Richa, Andrea (Committee member) / Arizona State University (Publisher)
Created2012
Description
Exhaustive testing is generally infeasible except in the smallest of systems. Research
has shown that testing the interactions among fewer (up to 6) components is generally
sufficient while retaining the capability to detect up to 99% of defects. This leads to a
substantial decrease in the number of tests. Covering arrays are combinatorial objects
that guarantee that every interaction is tested at least once.
In the absence of direct constructions, forming small covering arrays is generally
an expensive computational task. Algorithms to generate covering arrays have been
extensively studied yet no single algorithm provides the smallest solution. More
recently research has been directed towards a new technique called post-optimization.
These algorithms take an existing covering array and attempt to reduce its size.
This thesis presents a new idea for post-optimization by representing covering
arrays as graphs. Some properties of these graphs are established and the results are
contrasted with existing post-optimization algorithms. The idea is then generalized to
close variants of covering arrays with surprising results which in some cases reduce
the size by 30%. Applications of the method to generation and test prioritization are
studied and some interesting results are reported.
has shown that testing the interactions among fewer (up to 6) components is generally
sufficient while retaining the capability to detect up to 99% of defects. This leads to a
substantial decrease in the number of tests. Covering arrays are combinatorial objects
that guarantee that every interaction is tested at least once.
In the absence of direct constructions, forming small covering arrays is generally
an expensive computational task. Algorithms to generate covering arrays have been
extensively studied yet no single algorithm provides the smallest solution. More
recently research has been directed towards a new technique called post-optimization.
These algorithms take an existing covering array and attempt to reduce its size.
This thesis presents a new idea for post-optimization by representing covering
arrays as graphs. Some properties of these graphs are established and the results are
contrasted with existing post-optimization algorithms. The idea is then generalized to
close variants of covering arrays with surprising results which in some cases reduce
the size by 30%. Applications of the method to generation and test prioritization are
studied and some interesting results are reported.
ContributorsKaria, Rushang Vinod (Author) / Colbourn, Charles J (Thesis advisor) / Syrotiuk, Violet (Committee member) / Richa, Andréa W. (Committee member) / Arizona State University (Publisher)
Created2015
Description
Modern computer systems are complex engineered systems involving a large collection of individual parts, each with many parameters, or factors, affecting system performance. One way to understand these complex systems and their performance is through experimentation. However, most modern computer systems involve such a large number of factors that thorough experimentation on all of them is impossible. An initial screening step is thus necessary to determine which factors are relevant to the system's performance and which factors can be eliminated from experimentation.
Factors may impact system performance in different ways. A factor at a specific level may significantly affect performance as a main effect, or in combination with other main effects as an interaction. For screening, it is necessary both to identify the presence of these effects and to locate the factors responsible for them. A locating array is a relatively new experimental design that causes every main effect and interaction to occur and distinguishes all sets of d main effects and interactions from each other in the tests where they occur. This design is therefore helpful in screening complex systems.
The process of screening using locating arrays involves multiple steps. First, a locating array is constructed for all possibly significant factors. Next, the system is executed for all tests indicated by the locating array and a response is observed. Finally, the response is analyzed to identify the significant system factors for future experimentation. However, simply constructing a reasonably sized locating array for a large system is no easy task and analyzing the response of the tests presents additional difficulties due to the large number of possible predictors and the inherent imbalance in the experimental design itself. Further complications can arise from noise in the system or errors in testing.
This thesis has three contributions. First, it provides an algorithm to construct locating arrays using the Lovász Local Lemma with Moser-Tardos resampling. Second, it gives an algorithm to analyze the system response efficiently. Finally, it studies the robustness of the analysis to the heavy-hitters assumption underlying the approach as well as to varying amounts of system noise.
Factors may impact system performance in different ways. A factor at a specific level may significantly affect performance as a main effect, or in combination with other main effects as an interaction. For screening, it is necessary both to identify the presence of these effects and to locate the factors responsible for them. A locating array is a relatively new experimental design that causes every main effect and interaction to occur and distinguishes all sets of d main effects and interactions from each other in the tests where they occur. This design is therefore helpful in screening complex systems.
The process of screening using locating arrays involves multiple steps. First, a locating array is constructed for all possibly significant factors. Next, the system is executed for all tests indicated by the locating array and a response is observed. Finally, the response is analyzed to identify the significant system factors for future experimentation. However, simply constructing a reasonably sized locating array for a large system is no easy task and analyzing the response of the tests presents additional difficulties due to the large number of possible predictors and the inherent imbalance in the experimental design itself. Further complications can arise from noise in the system or errors in testing.
This thesis has three contributions. First, it provides an algorithm to construct locating arrays using the Lovász Local Lemma with Moser-Tardos resampling. Second, it gives an algorithm to analyze the system response efficiently. Finally, it studies the robustness of the analysis to the heavy-hitters assumption underlying the approach as well as to varying amounts of system noise.
ContributorsSeidel, Stephen (Author) / Syrotiuk, Violet R. (Thesis advisor) / Colbourn, Charles J (Committee member) / Montgomery, Douglas C. (Committee member) / Arizona State University (Publisher)
Created2018
Description
This dissertation studies three classes of combinatorial arrays with practical applications in testing, measurement, and security. Covering arrays are widely studied in software and hardware testing to indicate the presence of faulty interactions. Locating arrays extend covering arrays to achieve identification of the interactions causing a fault by requiring additional conditions on how interactions are covered in rows. This dissertation introduces a new class, the anonymizing arrays, to guarantee a degree of anonymity by bounding the probability a particular row is identified by the interaction presented. Similarities among these arrays lead to common algorithmic techniques for their construction which this dissertation explores. Differences arising from their application domains lead to the unique features of each class, requiring tailoring the techniques to the specifics of each problem.
One contribution of this work is a conditional expectation algorithm to build covering arrays via an intermediate combinatorial object. Conditional expectation efficiently finds intermediate-sized arrays that are particularly useful as ingredients for additional recursive algorithms. A cut-and-paste method creates large arrays from small ingredients. Performing transformations on the copies makes further improvements by reducing redundancy in the composed arrays and leads to fewer rows.
This work contains the first algorithm for constructing locating arrays for general values of $d$ and $t$. A randomized computational search algorithmic framework verifies if a candidate array is $(\bar{d},t)$-locating by partitioning the search space and performs random resampling if a candidate fails. Algorithmic parameters determine which columns to resample and when to add additional rows to the candidate array. Additionally, analysis is conducted on the performance of the algorithmic parameters to provide guidance on how to tune parameters to prioritize speed, accuracy, or a combination of both.
This work proposes anonymizing arrays as a class related to covering arrays with a higher coverage requirement and constraints. The algorithms for covering and locating arrays are tailored to anonymizing array construction. An additional property, homogeneity, is introduced to meet the needs of attribute-based authorization. Two metrics, local and global homogeneity, are designed to compare anonymizing arrays with the same parameters. Finally, a post-optimization approach reduces the homogeneity of an anonymizing array.
One contribution of this work is a conditional expectation algorithm to build covering arrays via an intermediate combinatorial object. Conditional expectation efficiently finds intermediate-sized arrays that are particularly useful as ingredients for additional recursive algorithms. A cut-and-paste method creates large arrays from small ingredients. Performing transformations on the copies makes further improvements by reducing redundancy in the composed arrays and leads to fewer rows.
This work contains the first algorithm for constructing locating arrays for general values of $d$ and $t$. A randomized computational search algorithmic framework verifies if a candidate array is $(\bar{d},t)$-locating by partitioning the search space and performs random resampling if a candidate fails. Algorithmic parameters determine which columns to resample and when to add additional rows to the candidate array. Additionally, analysis is conducted on the performance of the algorithmic parameters to provide guidance on how to tune parameters to prioritize speed, accuracy, or a combination of both.
This work proposes anonymizing arrays as a class related to covering arrays with a higher coverage requirement and constraints. The algorithms for covering and locating arrays are tailored to anonymizing array construction. An additional property, homogeneity, is introduced to meet the needs of attribute-based authorization. Two metrics, local and global homogeneity, are designed to compare anonymizing arrays with the same parameters. Finally, a post-optimization approach reduces the homogeneity of an anonymizing array.
ContributorsLanus, Erin (Author) / Colbourn, Charles J (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Montgomery, Douglas C. (Committee member) / Syrotiuk, Violet R. (Committee member) / Arizona State University (Publisher)
Created2019
Description
In the age of growing technology, Computer Science (CS) professionals have come into high demand. However, despite popular demand there are not enough computer scientists to fill these roles. The current demographic of computer scientists consists mainly of white men. This apparent gender gap must be addressed to promote diversity and inclusivity in a career that requires high creativity and innovation. To understand what enforces gender stereotypes and the gender gap within CS, survey and interview data were collected from both male and female senior students studying CS and those who have left the CS program at Arizona State University. Students were asked what experiences either diminished or reinforced their sense of belonging in this field as well as other questions related to their involvement in CS. Interview and survey data reveal a lack of representation within courses as well as lack of peer support are key factors that influence the involvement and retention of students in CS, especially women. This data was used to identify key factors that influence retention and what can be done to remedy the growing deficit of professionals in this field.
ContributorsKent, Victoria (Author) / Kappes, Janelle (Thesis director) / Forrest, Stephanie (Committee member) / Richa, Andrea (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2023-05
Description
A storage system requiring file redundancy and on-line repairability can be represented as a Steiner system, a combinatorial design with the property that every $t$-subset of its points occurs in exactly one of its blocks. Under this representation, files are the points and storage units are the blocks of the Steiner system, or vice-versa. Often, the popularities of the files of such storage systems run the gamut, with some files receiving hardly any attention, and others receiving most of it. For such systems, minimizing the difference in the collective popularity between any two storage units is nontrivial; this is the access balancing problem. With regard to the representative Steiner system, the access balancing problem in its simplest form amounts to constructing either a point or block labelling: an assignment of a set of integer labels (popularity ranks) to the Steiner system's point set or block set, respectively, requiring of the former assignment that the sums of the labelled points of any two blocks differ as little as possible and of the latter that the sums of the labels assigned to the containing blocks of any two distinct points differ as little as possible. The central aim of this dissertation is to supply point and block labellings for Steiner systems of block size greater than three, for which up to this point no attempt has been made. Four major results are given in this connection. First, motivated by the close connection between the size of the independent sets of a Steiner system and the quality of its labellings, a Steiner triple system of any admissible order is constructed with a pair of disjoint independent sets of maximum cardinality. Second, the spectrum of resolvable Bose triple systems is determined in order to label some Steiner 2-designs with block size four. Third, several kinds of independent sets are used to point-label Steiner 2-designs with block size four. Finally, optimal and close to optimal block labellings are given for an infinite class of 1-rotational resolvable Steiner 2-designs with arbitrarily large block size by exploiting their underlying group-theoretic properties.
ContributorsLusi, Dylan (Author) / Colbourn, Charles J (Thesis advisor) / Czygrinow, Andrzej (Committee member) / Fainekos, Georgios (Committee member) / Richa, Andrea (Committee member) / Arizona State University (Publisher)
Created2021
Description
Resistance to existing anti-cancer drugs poses a key challenge in the field of medical oncology, in that it results in the tumor not responding to treatment using the same medications to which it responded previously, leading to treatment failure. Adaptive therapy utilizes evolutionary principles of competitive suppression, leveraging competition between drug resistant and drug sensitive cells, to keep the population of drug resistant cells under control, thereby extending time to progression (TTP), relative to standard treatment using maximum tolerated dose (MTD). Development of adaptive therapy protocols is challenging, as it involves many parameters, and the number of parameters increase exponentially for each additional drug. Furthermore, the drugs could have a cytotoxic (killing cells directly), or a cytostatic (inhibiting cell division) mechanism of action, which could affect treatment outcome in important ways. I have implemented hybrid agent-based computational models to investigate adaptive therapy, using either a single drug (cytotoxic or cytostatic), or two drugs (cytotoxic or cytostatic), simulating three different adaptive therapy protocols for treatment using a single drug (dose modulation, intermittent, dose-skipping), and seven different treatment protocols for treatment using two drugs: three dose modulation (DM) protocols (DM Cocktail Tandem, DM Ping-Pong Alternate Every Cycle, DM Ping-Pong on Progression), and four fixed-dose (FD) protocols (FD Cocktail Intermittent, FD Ping-Pong Intermittent, FD Cocktail Dose-Skipping, FD Ping-Pong Dose-Skipping). The results indicate a Goldilocks level of drug exposure to be optimum, with both too little and too much drug having adverse effects. Adaptive therapy works best under conditions of strong cellular competition, such as high fitness costs, high replacement rates, or high turnover. Clonal competition is an important determinant of treatment outcome, and as such treatment using two drugs leads to more favorable outcome than treatment using a single drug. Switching drugs every treatment cycle (ping-pong) protocols work particularly well, as well as cocktail dose modulation, particularly when it is feasible to have a highly sensitive measurement of tumor burden. In general, overtreating seems to have adverse survival outcome, and triggering a treatment vacation, or stopping treatment sooner when the tumor is shrinking seems to work well.
ContributorsSaha, Kaushik (Author) / Maley, Carlo C (Thesis advisor) / Forrest, Stephanie (Committee member) / Anderson, Karen S (Committee member) / Cisneros, Luis H (Committee member) / Arizona State University (Publisher)
Created2023