Matching Items (14)
Description
New OpenFlow switches support a wide range of network applications, such as firewalls, load balancers, routers, and traffic monitoring. While ternary content addressable memory (TCAM) allows switches to process packets at high speed based on multiple header fields, today's commodity switches support just thousands to tens of thousands of forwarding rules. To allow for finer-grained policies on this hardware, efficient ways to support the abstraction of a switch are needed with arbitrarily large rule tables. To do so, a hardware-software hybrid switch is designed that relies on rule caching to provide large rule tables at low cost. Unlike traditional caching solutions, neither individual rules are cached (to respect rule dependencies) nor compressed (to preserve the per-rule traffic counts). Instead long dependency chains are ``spliced'' to cache smaller groups of rules while preserving the semantics of the network policy. The proposed hybrid switch design satisfies three criteria: (1) responsiveness, to allow rapid changes to the cache with minimal effect on traffic throughput; (2) transparency, to faithfully support native OpenFlow semantics; (3) correctness, to cache rules while preserving the semantics of the original policy. The evaluation of the hybrid switch on large rule tables suggest that it can effectively expose the benefits of both hardware and software switches to the controller and to applications running on top of it.
ContributorsAlipourfard, Omid (Author) / Syrotiuk, Violet R. (Thesis advisor) / Richa, Andréa W. (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2014
Description
Interference constitutes a major challenge for communication networks operating over a shared medium where availability is imperative. This dissertation studies the problem of designing and analyzing efficient medium access protocols which are robust against strong adversarial jamming. More specifically, four medium access (MAC) protocols (i.e., JADE, ANTIJAM, COMAC, and SINRMAC) which aim to achieve high throughput despite jamming activities under a variety of network and adversary models are presented. We also propose a self-stabilizing leader election protocol, SELECT, that can effectively elect a leader in the network with the existence of a strong adversary. Our protocols can not only deal with internal interference without the exact knowledge on the number of participants in the network, but they are also robust to unintentional or intentional external interference, e.g., due to co-existing networks or jammers. We model the external interference by a powerful adaptive and/or reactive adversary which can jam a (1 − ε)-portion of the time steps, where 0 < ε ≤ 1 is an arbitrary constant. We allow the adversary to be adaptive and to have complete knowledge of the entire protocol history. Moreover, in case the adversary is also reactive, it uses carrier sensing to make informed decisions to disrupt communications. Among the proposed protocols, JADE, ANTIJAM and COMAC are able to achieve Θ(1)-competitive throughput with the presence of the strong adversary; while SINRMAC is the first attempt to apply SINR model (i.e., Signal to Interference plus Noise Ratio), in robust medium access protocols design; the derived principles are also useful to build applications on top of the MAC layer, and we present SELECT, which is an exemplary study for leader election, which is one of the most fundamental tasks in distributed computing.
ContributorsZhang, Jin (Author) / Richa, Andréa W. (Thesis advisor) / Scheideler, Christian (Committee member) / Sen, Arunabha (Committee member) / Xue, Guoliang (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
What makes living systems different than non-living ones? Unfortunately this question is impossible to answer, at least currently. Instead, we must face computationally tangible questions based on our current understanding of physics, computation, information, and biology. Yet we have few insights into how living systems might quantifiably differ from their non-living counterparts, as in a mathematical foundation to explain away our observations of biological evolution, emergence, innovation, and organization. The development of a theory of living systems, if at all possible, demands a mathematical understanding of how data generated by complex biological systems changes over time. In addition, this theory ought to be broad enough as to not be constrained to an Earth-based biochemistry. In this dissertation, the philosophy of studying living systems from the perspective of traditional physics is first explored as a motivating discussion for subsequent research. Traditionally, we have often thought of the physical world from a bottom-up approach: things happening on a smaller scale aggregate into things happening on a larger scale. In addition, the laws of physics are generally considered static over time. Research suggests that biological evolution may follow dynamic laws that (at least in part) change as a function of the state of the system. Of the three featured research projects, cellular automata (CA) are used as a model to study certain aspects of living systems in two of them. These aspects include self-reference, open-ended evolution, local physical universality, subjectivity, and information processing. Open-ended evolution and local physical universality are attributed to the vast amount of innovation observed throughout biological evolution. Biological systems may distinguish themselves in terms of information processing and storage, not outside the theory of computation. The final research project concretely explores real-world phenomenon by means of mapping dominance hierarchies in the evolution of video game strategies. Though the main question of how life differs from non-life remains unanswered, the mechanisms behind open-ended evolution and physical universality are revealed.
ContributorsAdams, Alyssa M (Author) / Walker, Sara I (Thesis advisor) / Davies, Paul CW (Committee member) / Pavlic, Theodore P (Committee member) / Chamberlin, Ralph V (Committee member) / Arizona State University (Publisher)
Created2017
Description
Many applications require efficient data routing and dissemination in Delay Tolerant Networks (DTNs) in order to maximize the throughput of data in the network, such as providing healthcare to remote communities, and spreading related information in Mobile Social Networks (MSNs). In this thesis, the feasibility of using boats in the Amazon Delta Riverine region as data mule nodes is investigated and a robust data routing algorithm based on a fountain code approach is designed to ensure fast and timely data delivery considering unpredictable boat delays, break-downs, and high transmission failures. Then, the scenario of providing healthcare in Amazon Delta Region is extended to a general All-or-Nothing (Splittable) Multicommodity Flow (ANF) problem and a polynomial time constant approximation algorithm is designed for the maximum throughput routing problem based on a randomized rounding scheme with applications to DTNs. In an MSN, message content is closely related to users’ preferences, and can be used to significantly impact the performance of data dissemination. An interest- and content-based algorithm is developed where the contents of the messages, along with the network structural information are taken into consideration when making message relay decisions in order to maximize data throughput in an MSN. Extensive experiments show the effectiveness of the above proposed data dissemination algorithm by comparing it with state-of-the-art techniques.
ContributorsLiu, Mengxue (Author) / Richa, Andréa W. (Thesis advisor) / Johnson, Thienne (Committee member) / Syrotiuk, Violet R. (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2018
Description
Modern software and hardware systems are composed of a large number of components. Often different components of a system interact with each other in unforeseen and undesired ways to cause failures. Covering arrays are a useful mathematical tool for testing all possible t-way interactions among the components of a system.
The two major issues concerning covering arrays are explicit construction of a covering array, and exact or approximate determination of the covering array number---the minimum size of a covering array. Although these problems have been investigated extensively for the last couple of decades, in this thesis we present significant improvements on both of these questions using tools from the probabilistic method and randomized algorithms.
First, a series of improvements is developed on the previously known upper bounds on covering array numbers. An estimate for the discrete Stein-Lovász-Johnson bound is derived and the Stein- Lovász -Johnson bound is improved upon using an alteration strategy. Then group actions on the set of symbols are explored to establish two asymptotic upper bounds on covering array numbers that are tighter than any of the presently known bounds.
Second, an algorithmic paradigm, called the two-stage framework, is introduced for covering array construction. A number of concrete algorithms from this framework are analyzed, and it is shown that they outperform current methods in the range of parameter values that are of practical relevance. In some cases, a reduction in the number of tests by more than 50% is achieved.
Third, the Lovász local lemma is applied on covering perfect hash families to obtain an upper bound on covering array numbers that is tightest of all known bounds. This bound leads to a Moser-Tardos type algorithm that employs linear algebraic computation over finite fields to construct covering arrays. In some cases, this algorithm outperforms currently used methods by more than an 80% margin.
Finally, partial covering arrays are introduced to investigate a few practically relevant relaxations of the covering requirement. Using probabilistic methods, bounds are obtained on partial covering arrays that are significantly smaller than for covering arrays. Also, randomized algorithms are provided that construct such arrays in expected polynomial time.
The two major issues concerning covering arrays are explicit construction of a covering array, and exact or approximate determination of the covering array number---the minimum size of a covering array. Although these problems have been investigated extensively for the last couple of decades, in this thesis we present significant improvements on both of these questions using tools from the probabilistic method and randomized algorithms.
First, a series of improvements is developed on the previously known upper bounds on covering array numbers. An estimate for the discrete Stein-Lovász-Johnson bound is derived and the Stein- Lovász -Johnson bound is improved upon using an alteration strategy. Then group actions on the set of symbols are explored to establish two asymptotic upper bounds on covering array numbers that are tighter than any of the presently known bounds.
Second, an algorithmic paradigm, called the two-stage framework, is introduced for covering array construction. A number of concrete algorithms from this framework are analyzed, and it is shown that they outperform current methods in the range of parameter values that are of practical relevance. In some cases, a reduction in the number of tests by more than 50% is achieved.
Third, the Lovász local lemma is applied on covering perfect hash families to obtain an upper bound on covering array numbers that is tightest of all known bounds. This bound leads to a Moser-Tardos type algorithm that employs linear algebraic computation over finite fields to construct covering arrays. In some cases, this algorithm outperforms currently used methods by more than an 80% margin.
Finally, partial covering arrays are introduced to investigate a few practically relevant relaxations of the covering requirement. Using probabilistic methods, bounds are obtained on partial covering arrays that are significantly smaller than for covering arrays. Also, randomized algorithms are provided that construct such arrays in expected polynomial time.
ContributorsSarakāra, Kauśika (Author) / Colbourn, Charles J. (Thesis advisor) / Czygrinow, Andrzej (Committee member) / Richa, Andréa W. (Committee member) / Syrotiuk, Violet R. (Committee member) / Arizona State University (Publisher)
Created2016
Description
Peer-to-peer systems are known to be vulnerable to the Sybil attack. The lack of a central authority allows a malicious user to create many fake identities (called Sybil nodes) pretending to be independent honest nodes. The goal of the malicious user is to influence the system on his/her behalf. In order to detect the Sybil nodes and prevent the attack, a reputation system is used for the nodes, built through observing its interactions with its peers. The construction makes every node a part of a distributed authority that keeps records on the reputation and behavior of the nodes. Records of interactions between nodes are broadcast by the interacting nodes and honest reporting proves to be a Nash Equilibrium for correct (non-Sybil) nodes. In this research is argued that in realistic communication schedule scenarios, simple graph-theoretic queries such as the computation of Strongly Connected Components and Densest Subgraphs, help in exposing those nodes most likely to be Sybil, which are then proved to be Sybil or not through a direct test executed by some peers.
ContributorsCárdenas-Haro, José Antonio (Author) / Konjevod, Goran (Thesis advisor) / Richa, Andréa W. (Thesis advisor) / Sen, Arunabha (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2010
Description
Variation in living systems and how it cascades across organizational levels is central to biology. To understand the constraints and amplifications of variation in collective systems, I mathematically study how group-level differences emerge from individual variation in eusocial-insect colonies, which are inherently diverse and easily observable individually and collectively. Considering collective processes in three species where increasing degrees of heterogeneity are relevant, I address how individual variation scales to colony-level variation and to what degree it is adaptive. In Chapter 2, I introduce a Markov-chain decision model for stochastic individual quorum-based recruitment decisions of rock-ant workers during house hunting, and how they determine collective speed--accuracy balance. Differences in the average threshold-dependent response characteristics of workers between colonies cause collective differences in decision-making. Moreover, noisy behavior may prevent drastic collective cascading into poor nests. In Chapter 3, I develop an ordinary differential equation (ODE) model to study how cognitive diversity among honey-bee foragers influences collective attention allocation between novel and familiar resources. Results provide a mechanistic basis for changes in foraging activity and preference with group composition. Moreover, sensitivity analysis reveals that the main individual driver for foraging allocation shifts from recruitment (communication) to persistence (independent effort) as colony composition changes. This might favor specific degrees of heterogeneity that best amplify communication in wild colonies. Lastly, in Chapter 4, I consider diversity in size, age, and task for nest defense in stingless bees. To better understand how these dimensions of diversity interact to balance defensive demands with other colony needs, I study their effect on colony size and task allocation through a demographic Filippov ODE model. Along each dimension, variation is beneficial in a certain range, outside of which colony adaptation and survival are compromised. This work elucidates how variation in collective properties emerges from nonlinear interactions between varying components in eusocial insects, but it can be generalized to other biological systems with similar fundamental characteristics but less empirical tractability. Moreover, it has the potential of inspiring algorithms that capitalize on heterogeneity in engineered systems where simple components with limited information and no central control must solve complex tasks.
ContributorsNavas Zuloaga, Maria Gabriela (Author) / Kang, Yun (Thesis advisor) / Smith, Brian H (Thesis advisor) / Pavlic, Theodore P (Committee member) / Arizona State University (Publisher)
Created2022
Description
Chemical Reaction Networks (CRNs) provide a useful framework for modeling andcontrolling large numbers of agents that undergo stochastic transitions between a set of states
in a manner similar to chemical compounds. By utilizing CRN models to design agent control
policies, some of the computational challenges in the coordination of multi-agent systems can
be overcome. In this thesis, a CRN model is developed that defines agent control policies for a
multi-agent construction task. The use of surface CRNs to overcome the tradeoff between
speed and accuracy of task performance is explained. The computational difficulties involved
in coordinating multiple agents to complete collective construction tasks is then discussed. A
method for stochastic task and motion planning (TAMP) is proposed to explain how a TAMP
solver can be applied with CRNs to coordinate multiple agents.
This work defines a collective construction scenario in which a group of noncommunicating agents must rearrange blocks on a discrete domain with obstacles into a
predefined target distribution. Four different construction tasks are considered with 10, 20,
30, or 40 blocks, and a simulation of each scenario with 2, 4, 6, or 8 agents is performed. As the
number of blocks increases, the construction problem becomes more complex, and a given
population of agents requires more time to complete the task. Populations of fewer than 8
agents are unable to solve the 30-block and 40-block problems in the allotted simulation time,
suggesting an inflection point for computational feasibility, implying that beyond that point
the solution times for fewer than 8 agents would be expected to increase significantly. For a
group of 8 agents, the time to complete the task generally increases as the number of blocks
increases, except for the 30-block problem, which has specifications that make the task slightly
easier for the agents to complete compared to the 20-block problem. For the 10-block and 20-
block problems, the time to complete the task decreases as the number of agents increases;
however, the marginal effect of each additional two agents on this time decreases. This can be
explained through the pigeonhole principle: since there area finite number of states, when the
number of agents is greater than the number of available spaces, deadlocks start to occur and
the expectation is that the overall solution time to tend to infinity.
ContributorsKamojjhala, Pranav (Author) / Berman, Spring (Thesis advisor) / Fainekos, Gergios E (Thesis advisor) / Pavlic, Theodore P (Committee member) / Arizona State University (Publisher)
Created2022
Description
In a multi-robot system, locating a team robot is an important issue. If robots
can refer to the location of team robots based on information through passive action
recognition without explicit communication, various advantages (e.g. improving security
for military purposes) can be obtained. Specifically, when team robots follow
the same motion rule based on information about adjacent robots, associations can
be found between robot actions. If the association can be analyzed, this can be a clue
to the remote robot. Using these clues, it is possible to infer remote robots which are
outside of the sensor range.
In this paper, a multi-robot system is constructed using a combination of Thymio
II robotic platforms and Raspberry pi controllers. Robots moving in chain-formation
take action using motion rules based on information obtained through passive action
recognition. To find associations between robots, a regression model is created using
Deep Neural Network (DNN) and Long Short-Term Memory (LSTM), one of state-of-art technologies.
The input data of the regression model is divided into historical data, which
are consecutive positions of the robot, and observed data, which is information about the
observed robot. Historical data is sequence data that is analyzed through the LSTM
layer. The accuracy of the regression model designed using DNN can vary depending
on the quantity and quality of the input. In this thesis, three different input situations
are assumed for comparison. First, the amount of observed data is different, second, the
type of observed data is different, and third, the history length is different. Comparative
models are constructed for each case, and prediction accuracy is compared to analyze
the effect of input data on the regression model. This exploration validates that these
methods from deep learning can reduce the communication demands in coordinated
motion of multi-robot systems
can refer to the location of team robots based on information through passive action
recognition without explicit communication, various advantages (e.g. improving security
for military purposes) can be obtained. Specifically, when team robots follow
the same motion rule based on information about adjacent robots, associations can
be found between robot actions. If the association can be analyzed, this can be a clue
to the remote robot. Using these clues, it is possible to infer remote robots which are
outside of the sensor range.
In this paper, a multi-robot system is constructed using a combination of Thymio
II robotic platforms and Raspberry pi controllers. Robots moving in chain-formation
take action using motion rules based on information obtained through passive action
recognition. To find associations between robots, a regression model is created using
Deep Neural Network (DNN) and Long Short-Term Memory (LSTM), one of state-of-art technologies.
The input data of the regression model is divided into historical data, which
are consecutive positions of the robot, and observed data, which is information about the
observed robot. Historical data is sequence data that is analyzed through the LSTM
layer. The accuracy of the regression model designed using DNN can vary depending
on the quantity and quality of the input. In this thesis, three different input situations
are assumed for comparison. First, the amount of observed data is different, second, the
type of observed data is different, and third, the history length is different. Comparative
models are constructed for each case, and prediction accuracy is compared to analyze
the effect of input data on the regression model. This exploration validates that these
methods from deep learning can reduce the communication demands in coordinated
motion of multi-robot systems
ContributorsKang, Sehyeok (Author) / Pavlic, Theodore P (Thesis advisor) / Richa, Andréa W. (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020