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- Genre: Academic theses
Description
This report investigates the improvement in the transmission throughput, when fountain codes are used in opportunistic data routing, for a proposed delay tolerant network to connect remote and isolated communities in the Amazon region in Brazil, to the main city of that area. To extend healthcare facilities to the remote and isolated communities, on the banks of river Amazon in Brazil, the network [7] utilizes regularly schedules boats as data mules to carry data from one city to other.
Frequent thunder and rain storms, given state of infrastructure and harsh geographical terrain; all contribute to increase in chances of massages not getting delivered to intended destination. These regions have access to medical facilities only through sporadic visits from medical team from the main city in the region, Belem. The proposed network uses records for routine clinical examinations such as ultrasounds on pregnant women could be sent to the doctors in Belem for evaluation.
However, due to the lack of modern communication infrastructure in these communities and unpredictable boat schedules due to delays and breakdowns, as well as high transmission failures due to the harsh environment in the region, mandate the design of robust delay-tolerant routing algorithms. The work presented here incorporates the unpredictability of the Amazon riverine scenario into the simulation model - accounting for boat mechanical failure in boats leading to delays/breakdowns, possible decrease in transmission speed due to rain and individual packet losses.
Extensive simulation results are presented, to evaluate the proposed approach and to verify that the proposed solution [7] could be used as a viable mode of communication, given the lack of available options in the region. While the simulation results are focused on remote healthcare applications in the Brazilian Amazon, we envision that our approach may also be used for other remote applications, such as distance education, and other similar scenarios.
Frequent thunder and rain storms, given state of infrastructure and harsh geographical terrain; all contribute to increase in chances of massages not getting delivered to intended destination. These regions have access to medical facilities only through sporadic visits from medical team from the main city in the region, Belem. The proposed network uses records for routine clinical examinations such as ultrasounds on pregnant women could be sent to the doctors in Belem for evaluation.
However, due to the lack of modern communication infrastructure in these communities and unpredictable boat schedules due to delays and breakdowns, as well as high transmission failures due to the harsh environment in the region, mandate the design of robust delay-tolerant routing algorithms. The work presented here incorporates the unpredictability of the Amazon riverine scenario into the simulation model - accounting for boat mechanical failure in boats leading to delays/breakdowns, possible decrease in transmission speed due to rain and individual packet losses.
Extensive simulation results are presented, to evaluate the proposed approach and to verify that the proposed solution [7] could be used as a viable mode of communication, given the lack of available options in the region. While the simulation results are focused on remote healthcare applications in the Brazilian Amazon, we envision that our approach may also be used for other remote applications, such as distance education, and other similar scenarios.
ContributorsAgarwal, Rachit (Author) / Richa, Andrea (Thesis advisor) / Dasgupta, Partha (Committee member) / Johnson, Thienne (Committee member) / Arizona State University (Publisher)
Created2015
Description
Resource allocation is one of the most challenging issues policy decision makers must address. The objective of this thesis is to explore the resource allocation from an economical perspective, i.e., how to purchase resources in order to satisfy customers' requests. In this thesis, we attend to answer the question: when and how to buy resources to fulfill customers' demands with minimum costs?
The first topic studied in this thesis is resource allocation in cloud networks. Cloud computing heralded an era where resources (such as computation and storage) can be scaled up and down elastically and on demand. This flexibility is attractive for its cost effectiveness: the cloud resource price depends on the actual utilization over time. This thesis studies two critical problems in cloud networks, focusing on the economical aspects of the resource allocation in the cloud/virtual networks, and proposes six algorithms to address the resource allocation problems for different discount models. The first problem attends a scenario where the virtual network provider offers different contracts to the service provider. Four algorithms for resource contract migration are proposed under two pricing models: Pay-as-You-Come and Pay-as-You-Go. The second problem explores a scenario where a cloud provider offers k contracts each with a duration and a rate respectively and a customer buys these contracts in order to satisfy its resource demand. This work shows that this problem can be seen as a 2-dimensional generalization of the classic online parking permit problem, and present a k-competitive online algorithm and an optimal online algorithm.
The second topic studied in this thesis is to explore how resource allocation and purchasing strategies work in our daily life. For example, is it worth buying a Yoga pass which costs USD 100 for ten entries, although it will expire at the end of this year? Decisions like these are part of our daily life, yet, not much is known today about good online strategies to buy discount vouchers with expiration dates. This work hence introduces a Discount Voucher Purchase Problem (DVPP). It aims to optimize the strategies for buying discount vouchers, i.e., coupons, vouchers, groupons which are valid only during a certain time period. The DVPP comes in three flavors: (1) Once Expire Lose Everything (OELE): Vouchers lose their entire value after expiration. (2) Once Expire Lose Discount (OELD): Vouchers lose their discount value after expiration. (3) Limited Purchasing Window (LPW): Vouchers have the property of OELE and can only be bought during a certain time window.
This work explores online algorithms with a provable competitive ratio against a clairvoyant offline algorithm, even in the worst case. In particular, this work makes the following contributions: we present a 4-competitive algorithm for OELE, an 8-competitive algorithm for OELD, and a lower bound for LPW. We also present an optimal offline algorithm for OELE and LPW, and show it is a 2-approximation solution for OELD.
The first topic studied in this thesis is resource allocation in cloud networks. Cloud computing heralded an era where resources (such as computation and storage) can be scaled up and down elastically and on demand. This flexibility is attractive for its cost effectiveness: the cloud resource price depends on the actual utilization over time. This thesis studies two critical problems in cloud networks, focusing on the economical aspects of the resource allocation in the cloud/virtual networks, and proposes six algorithms to address the resource allocation problems for different discount models. The first problem attends a scenario where the virtual network provider offers different contracts to the service provider. Four algorithms for resource contract migration are proposed under two pricing models: Pay-as-You-Come and Pay-as-You-Go. The second problem explores a scenario where a cloud provider offers k contracts each with a duration and a rate respectively and a customer buys these contracts in order to satisfy its resource demand. This work shows that this problem can be seen as a 2-dimensional generalization of the classic online parking permit problem, and present a k-competitive online algorithm and an optimal online algorithm.
The second topic studied in this thesis is to explore how resource allocation and purchasing strategies work in our daily life. For example, is it worth buying a Yoga pass which costs USD 100 for ten entries, although it will expire at the end of this year? Decisions like these are part of our daily life, yet, not much is known today about good online strategies to buy discount vouchers with expiration dates. This work hence introduces a Discount Voucher Purchase Problem (DVPP). It aims to optimize the strategies for buying discount vouchers, i.e., coupons, vouchers, groupons which are valid only during a certain time period. The DVPP comes in three flavors: (1) Once Expire Lose Everything (OELE): Vouchers lose their entire value after expiration. (2) Once Expire Lose Discount (OELD): Vouchers lose their discount value after expiration. (3) Limited Purchasing Window (LPW): Vouchers have the property of OELE and can only be bought during a certain time window.
This work explores online algorithms with a provable competitive ratio against a clairvoyant offline algorithm, even in the worst case. In particular, this work makes the following contributions: we present a 4-competitive algorithm for OELE, an 8-competitive algorithm for OELD, and a lower bound for LPW. We also present an optimal offline algorithm for OELE and LPW, and show it is a 2-approximation solution for OELD.
ContributorsHu, Xinhui (Author) / Richa, Andrea (Thesis advisor) / Schmid, Stefan (Committee member) / Sen, Arunabha (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2015
Description
The purpose of this research is to efficiently analyze certain data provided and to see if a useful trend can be observed as a result. This trend can be used to analyze certain probabilities. There are three main pieces of data which are being analyzed in this research: The value for δ of the call and put option, the %B value of the stock, and the amount of time until expiration of the stock option. The %B value is the most important. The purpose of analyzing the data is to see the relationship between the variables and, given certain values, what is the probability the trade makes money. This result will be used in finding the probability certain trades make money over a period of time.
Since options are so dependent on probability, this research specifically analyzes stock options rather than stocks themselves. Stock options have value like stocks except options are leveraged. The most common model used to calculate the value of an option is the Black-Scholes Model [1]. There are five main variables the Black-Scholes Model uses to calculate the overall value of an option. These variables are θ, δ, γ, v, and ρ. The variable, θ is the rate of change in price of the option due to time decay, δ is the rate of change of the option’s price due to the stock’s changing value, γ is the rate of change of δ, v represents the rate of change of the value of the option in relation to the stock’s volatility, and ρ represents the rate of change in value of the option in relation to the interest rate [2]. In this research, the %B value of the stock is analyzed along with the time until expiration of the option. All options have the same δ. This is due to the fact that all the options analyzed in this experiment are less than two months from expiration and the value of δ reveals how far in or out of the money an option is.
The machine learning technique used to analyze the data and the probability
is support vector machines. Support vector machines analyze data that can be classified in one of two or more groups and attempts to find a pattern in the data to develop a model, which reliably classifies similar, future data into the correct group. This is used to analyze the outcome of stock options.
ContributorsReeves, Michael (Author) / Richa, Andrea (Thesis advisor) / McCarville, Daniel R. (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2015
Description
In software testing, components are tested individually to make sure each performs as expected. The next step is to confirm that two or more components are able to work together. This stage of testing is often difficult because there can be numerous configurations between just two components.
Covering arrays are one way to ensure a set of tests will cover every possible configuration at least once. However, on systems with many settings, it is computationally intensive to run every possible test. Test prioritization methods can identify tests of greater importance. This concept of test prioritization can help determine which tests can be removed with minimal impact to the overall testing of the system.
This thesis presents three algorithms that generate covering arrays that test the interaction of every two components at least twice. These algorithms extend the functionality of an established greedy test prioritization method to ensure important components are selected in earlier tests. The algorithms are tested on various inputs and the results reveal that on average, the resulting covering arrays are two-fifths to one-half times smaller than a covering array generated through brute force.
Covering arrays are one way to ensure a set of tests will cover every possible configuration at least once. However, on systems with many settings, it is computationally intensive to run every possible test. Test prioritization methods can identify tests of greater importance. This concept of test prioritization can help determine which tests can be removed with minimal impact to the overall testing of the system.
This thesis presents three algorithms that generate covering arrays that test the interaction of every two components at least twice. These algorithms extend the functionality of an established greedy test prioritization method to ensure important components are selected in earlier tests. The algorithms are tested on various inputs and the results reveal that on average, the resulting covering arrays are two-fifths to one-half times smaller than a covering array generated through brute force.
ContributorsAng, Nicole (Author) / Syrotiuk, Violet (Thesis advisor) / Colbourn, Charles (Committee member) / Richa, Andrea (Committee member) / Arizona State University (Publisher)
Created2015
Description
The Dual Marching Tetrahedra algorithm is a generalization of the Dual Marching Cubes algorithm, used to build a boundary surface around points which have been assigned a particular scalar density value, such as the data produced by and Magnetic Resonance Imaging or Computed Tomography scanner. This boundary acts as a skin between points which are determined to be "inside" and "outside" of an object. However, the DMT is vague in regards to exactly where each vertex of the boundary should be placed, which will not necessarily produce smooth results. Mesh smoothing algorithms which ignore the DMT data structures may distort the output mesh so that it could incorrectly include or exclude density points. Thus, an algorithm is presented here which is designed to smooth the output mesh, while obeying the underlying data structures of the DMT algorithm.
ContributorsJohnson, Sean (Author) / Farin, Gerald (Thesis advisor) / Richa, Andrea (Committee member) / Nallure Balasubramanian, Vineeth (Committee member) / Arizona State University (Publisher)
Created2011
Description
While network problems have been addressed using a central administrative domain with a single objective, the devices in most networks are actually not owned by a single entity but by many individual entities. These entities make their decisions independently and selfishly, and maybe cooperate with a small group of other entities only when this form of coalition yields a better return. The interaction among multiple independent decision-makers necessitates the use of game theory, including economic notions related to markets and incentives. In this dissertation, we are interested in modeling, analyzing, addressing network problems caused by the selfish behavior of network entities. First, we study how the selfish behavior of network entities affects the system performance while users are competing for limited resource. For this resource allocation domain, we aim to study the selfish routing problem in networks with fair queuing on links, the relay assignment problem in cooperative networks, and the channel allocation problem in wireless networks. Another important aspect of this dissertation is the study of designing efficient mechanisms to incentivize network entities to achieve certain system objective. For this incentive mechanism domain, we aim to motivate wireless devices to serve as relays for cooperative communication, and to recruit smartphones for crowdsourcing. In addition, we apply different game theoretic approaches to problems in security and privacy domain. For this domain, we aim to analyze how a user could defend against a smart jammer, who can quickly learn about the user's transmission power. We also design mechanisms to encourage mobile phone users to participate in location privacy protection, in order to achieve k-anonymity.
ContributorsYang, Dejun (Author) / Xue, Guoliang (Thesis advisor) / Richa, Andrea (Committee member) / Sen, Arunabha (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Contention based IEEE 802.11MAC uses the binary exponential backoff algorithm (BEB) for the contention resolution. The protocol suffers poor performance in the heavily loaded networks and MANETs, high collision rate and packet drops, probabilistic delay guarantees, and unfairness. Many backoff strategies were proposed to improve the performance of IEEE 802.11 but all ignore the network topology and demand. Persistence is defined as the fraction of time a node is allowed to transmit, when this allowance should take into account topology and load, it is topology and load aware persistence (TLA). We develop a relation between contention window size and the TLA-persistence. We implement a new backoff strategy where the TLA-persistence is defined as the lexicographic max-min channel allocation. We use a centralized algorithm to calculate each node's TLApersistence and then convert it into a contention window size. The new backoff strategy is evaluated in simulation, comparing with that of the IEEE 802.11 using BEB. In most of the static scenarios like exposed terminal, flow in the middle, star topology, and heavy loaded multi-hop networks and in MANETs, through the simulation study, we show that the new backoff strategy achieves higher overall average throughput as compared to that of the IEEE 802.11 using BEB.
ContributorsBhyravajosyula, Sai Vishnu Kiran (Author) / Syrotiuk, Violet R. (Thesis advisor) / Sen, Arunabha (Committee member) / Richa, Andrea (Committee member) / Arizona State University (Publisher)
Created2013
Description
As networks are playing an increasingly prominent role in different aspects of our lives, there is a growing awareness that improving their performance is of significant importance. In order to enhance performance of networks, it is essential that scarce networking resources be allocated smartly to match the continuously changing network environment. This dissertation focuses on two different kinds of networks - communication and social, and studies resource allocation problems in these networks. The study on communication networks is further divided into different networking technologies - wired and wireless, optical and mobile, airborne and terrestrial. Since nodes in an airborne network (AN) are heterogeneous and mobile, the design of a reliable and robust AN is highly complex. The dissertation studies connectivity and fault-tolerance issues in ANs and proposes algorithms to compute the critical transmission range in fault free, faulty and delay tolerant scenarios. Just as in the case of ANs, power optimization and fault tolerance are important issues in wireless sensor networks (WSN). In a WSN, a tree structure is often used to deliver sensor data to a sink node. In a tree, failure of a node may disconnect the tree. The dissertation investigates the problem of enhancing the fault tolerance capability of data gathering trees in WSN. The advent of OFDM technology provides an opportunity for efficient resource utilization in optical networks and also introduces a set of novel problems, such as routing and spectrum allocation (RSA) problem. This dissertation proves that RSA problem is NP-complete even when the network topology is a chain, and proposes approximation algorithms. In the domain of social networks, the focus of this dissertation is study of influence propagation in presence of active adversaries. In a social network multiple vendors may attempt to influence the nodes in a competitive fashion. This dissertation investigates the scenario where the first vendor has already chosen a set of nodes and the second vendor, with the knowledge of the choice of the first, attempts to identify a smallest set of nodes so that after the influence propagation, the second vendor's market share is larger than the first.
ContributorsShirazipourazad, Shahrzad (Author) / Sen, Arunabha (Committee member) / Xue, Guoliang (Committee member) / Richa, Andrea (Committee member) / Saripalli, Srikanth (Committee member) / Arizona State University (Publisher)
Created2014
Description
Software Managed Manycore (SMM) architectures - in which each core has only a scratch pad memory (instead of caches), - are a promising solution for scaling memory hierarchy to hundreds of cores. However, in these architectures, the code and data of the tasks mapped to the cores must be explicitly managed in the software by the compiler. State-of-the-art compiler techniques for SMM architectures require inter-procedural information and analysis. A call graph of the program does not have enough information, and Global CFG, i.e., combining all the control flow graphs of the program has too much information, and becomes too big. As a result, most new techniques have informally defined and used GCCFG (Global Call Control Flow Graph) - a whole program representation which captures the control-flow as well as function call information in a succinct way - to perform inter-procedural analysis. However, how to construct it has not been shown yet. We find that for several simple call and control flow graphs, constructing GCCFG is relatively straightforward, but there are several cases in common applications where unique graph transformation is needed in order to formally and correctly construct the GCCFG. This paper fills this gap, and develops graph transformations to allow the construction of GCCFG in (almost) all cases. Our experiments show that by using succinct representation (GCCFG) rather than elaborate representation (GlobalCFG), the compilation time of state-of-the-art code management technique [4] can be improved by an average of 5X, and that of stack management [20] can be improved by an average of 4X.
ContributorsHolton, Bryce (Author) / Shrivastava, Aviral (Thesis advisor) / Collofello, James (Committee member) / Richa, Andrea (Committee member) / Arizona State University (Publisher)
Created2014
Description
Communication networks, both wired and wireless, are expected to have a certain level of fault-tolerance capability.These networks are also expected to ensure a graceful degradation in performance when some of the network components fail. Traditional studies on fault tolerance in communication networks, for the most part, make no assumptions regarding the location of node/link faults, i.e., the faulty nodes and links may be close to each other or far from each other. However, in many real life scenarios, there exists a strong spatial correlation among the faulty nodes and links. Such failures are often encountered in disaster situations, e.g., natural calamities or enemy attacks. In presence of such region-based faults, many of traditional network analysis and fault-tolerant metrics, that are valid under non-spatially correlated faults, are no longer applicable. To this effect, the main thrust of this research is design and analysis of robust networks in presence of such region-based faults. One important finding of this research is that if some prior knowledge is available on the maximum size of the region that might be affected due to a region-based fault, this piece of knowledge can be effectively utilized for resource efficient design of networks. It has been shown in this dissertation that in some scenarios, effective utilization of this knowledge may result in substantial saving is transmission power in wireless networks. In this dissertation, the impact of region-based faults on the connectivity of wireless networks has been studied and a new metric, region-based connectivity, is proposed to measure the fault-tolerance capability of a network. In addition, novel metrics, such as the region-based component decomposition number(RBCDN) and region-based largest component size(RBLCS) have been proposed to capture the network state, when a region-based fault disconnects the network. Finally, this dissertation presents efficient resource allocation techniques that ensure tolerance against region-based faults, in distributed file storage networks and data center networks.
ContributorsBanerjee, Sujogya (Author) / Sen, Arunabha (Thesis advisor) / Xue, Guoliang (Committee member) / Richa, Andrea (Committee member) / Hurlbert, Glenn (Committee member) / Arizona State University (Publisher)
Created2013