Matching Items (10)
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- All Subjects: Resource allocation
- All Subjects: SaaS
- Creators: Xue, Guoliang
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of traditional network analysis and fault-tolerant metrics, that are valid under non-spatially correlated faults, are no longer applicable.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
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
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
DescriptionThe thesis will study price optimization techniques, SaaS industry pricing structures, A/B testing, and then build a unique framework to optimize price and maximize revenue. The ultimate goal of the thesis research is to create a framework that identifies the best pricing structure and price points for a SaaS company.
ContributorsRyu, Kibaek (Author) / Clark, Joseph (Thesis director) / Granberry, Chase (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor) / Department of Information Systems (Contributor)
Created2014-05
Description
Nowadays, Computing is so pervasive that it has become indeed the 5th utility (after water, electricity, gas, telephony) as Leonard Kleinrock once envisioned. Evolved from utility computing, cloud computing has emerged as a computing infrastructure that enables rapid delivery of computing resources as a utility in a dynamically scalable, virtualized manner. However, the current industrial cloud computing implementations promote segregation among different cloud providers, which leads to user lockdown because of prohibitive migration cost. On the other hand, Service-Orented Computing (SOC) including service-oriented architecture (SOA) and Web Services (WS) promote standardization and openness with its enabling standards and communication protocols. This thesis proposes a Service-Oriented Cloud Computing Architecture by combining the best attributes of the two paradigms to promote an open, interoperable environment for cloud computing development. Mutil-tenancy SaaS applicantions built on top of SOCCA have more flexibility and are not locked down by a certain platform. Tenants residing on a multi-tenant application appear to be the sole owner of the application and not aware of the existence of others. A multi-tenant SaaS application accommodates each tenant’s unique requirements by allowing tenant-level customization. A complex SaaS application that supports hundreds, even thousands of tenants could have hundreds of customization points with each of them providing multiple options, and this could result in a huge number of ways to customize the application. This dissertation also proposes innovative customization approaches, which studies similar tenants’ customization choices and each individual users behaviors, then provides guided semi-automated customization process for the future tenants. A semi-automated customization process could enable tenants to quickly implement the customization that best suits their business needs.
ContributorsSun, Xin (Author) / Tsai, Wei-Tek (Thesis advisor) / Xue, Guoliang (Committee member) / Davulcu, Hasan (Committee member) / Sarjoughian, Hessam S. (Committee member) / Arizona State University (Publisher)
Created2016
Description
We live in a networked world with a multitude of networks, such as communication networks, electric power grid, transportation networks and water distribution networks, all around us. In addition to such physical (infrastructure) networks, recent years have seen tremendous proliferation of social networks, such as Facebook, Twitter, LinkedIn, Instagram, Google+ and others. These powerful social networks are not only used for harnessing revenue from the infrastructure networks, but are also increasingly being used as “non-conventional sensors” for monitoring the infrastructure networks. Accordingly, nowadays, analyses of social and infrastructure networks go hand-in-hand. This dissertation studies resource allocation problems encountered in this set of diverse, heterogeneous, and interdependent networks. Three problems studied in this dissertation are encountered in the physical network domain while the three other problems studied are encountered in the social network domain.
The first problem from the infrastructure network domain relates to distributed files storage scheme with a goal of enhancing robustness of data storage by making it tolerant against large scale geographically-correlated failures. The second problem relates to placement of relay nodes in a deployment area with multiple sensor nodes with a goal of augmenting connectivity of the resulting network, while staying within the budget specifying the maximum number of relay nodes that can be deployed. The third problem studied in this dissertation relates to complex interdependencies that exist between infrastructure networks, such as power grid and communication network. The progressive recovery problem in an interdependent network is studied whose goal is to maximize system utility over the time when recovery process of failed entities takes place in a sequential manner.
The three problems studied from the social network domain relate to influence propagation in adversarial environment and political sentiment assessment in various states in a country with a goal of creation of a “political heat map” of the country. In the first problem of the influence propagation domain, the goal of the second player is to restrict the influence of the first player, while in the second problem the goal of the second player is to have a larger market share with least amount of initial investment.
The first problem from the infrastructure network domain relates to distributed files storage scheme with a goal of enhancing robustness of data storage by making it tolerant against large scale geographically-correlated failures. The second problem relates to placement of relay nodes in a deployment area with multiple sensor nodes with a goal of augmenting connectivity of the resulting network, while staying within the budget specifying the maximum number of relay nodes that can be deployed. The third problem studied in this dissertation relates to complex interdependencies that exist between infrastructure networks, such as power grid and communication network. The progressive recovery problem in an interdependent network is studied whose goal is to maximize system utility over the time when recovery process of failed entities takes place in a sequential manner.
The three problems studied from the social network domain relate to influence propagation in adversarial environment and political sentiment assessment in various states in a country with a goal of creation of a “political heat map” of the country. In the first problem of the influence propagation domain, the goal of the second player is to restrict the influence of the first player, while in the second problem the goal of the second player is to have a larger market share with least amount of initial investment.
ContributorsMazumder, Anisha (Author) / Sen, Arunabha (Thesis advisor) / Richa, Andrea (Committee member) / Xue, Guoliang (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2016
Description
The critical infrastructures of the nation are a large and complex network of human, physical and cyber-physical systems. In recent times, it has become increasingly apparent that individual critical infrastructures, such as the power and communication networks, do not operate in isolation, but instead are part of a complex interdependent ecosystem where a failure involving a small set of network entities can trigger a cascading event resulting in the failure of a much larger set of entities through the failure propagation process.
Recognizing the need for a deeper understanding of the interdependent relationships between such critical infrastructures, several models have been proposed and analyzed in the last few years. However, most of these models are over-simplified and fail to capture the complex interdependencies that may exist between critical infrastructures. To overcome the limitations of existing models, this dissertation presents a new model -- the Implicative Interdependency Model (IIM) that is able to capture such complex interdependency relations. As the potential for a failure cascade in critical interdependent networks poses several risks that can jeopardize the nation, this dissertation explores relevant research problems in the interdependent power and communication networks using the proposed IIM and lays the foundations for further study using this model.
Apart from exploring problems in interdependent critical infrastructures, this dissertation also explores resource allocation techniques for environments enabled with cyber-physical systems. Specifically, the problem of efficient path planning for data collection using mobile cyber-physical systems is explored. Two such environments are considered: a Radio-Frequency IDentification (RFID) environment with mobile “Tags” and “Readers”, and a sensor data collection environment where both the sensors and the data mules (data collectors) are mobile.
Finally, from an applied research perspective, this dissertation presents Raptor, an advanced network planning and management tool for mitigating the impact of spatially correlated, or region based faults on infrastructure networks. Raptor consolidates a wide range of studies conducted in the last few years on region based faults, and provides an interface for network planners, designers and operators to use the results of these studies for designing robust and resilient networks in the presence of spatially correlated faults.
Recognizing the need for a deeper understanding of the interdependent relationships between such critical infrastructures, several models have been proposed and analyzed in the last few years. However, most of these models are over-simplified and fail to capture the complex interdependencies that may exist between critical infrastructures. To overcome the limitations of existing models, this dissertation presents a new model -- the Implicative Interdependency Model (IIM) that is able to capture such complex interdependency relations. As the potential for a failure cascade in critical interdependent networks poses several risks that can jeopardize the nation, this dissertation explores relevant research problems in the interdependent power and communication networks using the proposed IIM and lays the foundations for further study using this model.
Apart from exploring problems in interdependent critical infrastructures, this dissertation also explores resource allocation techniques for environments enabled with cyber-physical systems. Specifically, the problem of efficient path planning for data collection using mobile cyber-physical systems is explored. Two such environments are considered: a Radio-Frequency IDentification (RFID) environment with mobile “Tags” and “Readers”, and a sensor data collection environment where both the sensors and the data mules (data collectors) are mobile.
Finally, from an applied research perspective, this dissertation presents Raptor, an advanced network planning and management tool for mitigating the impact of spatially correlated, or region based faults on infrastructure networks. Raptor consolidates a wide range of studies conducted in the last few years on region based faults, and provides an interface for network planners, designers and operators to use the results of these studies for designing robust and resilient networks in the presence of spatially correlated faults.
ContributorsDas, Arun (Author) / Sen, Arunabha (Thesis advisor) / Xue, Guoliang (Committee member) / Fainekos, Georgios (Committee member) / Qiao, Chunming (Committee member) / Arizona State University (Publisher)
Created2016
Description
Software-as-a-Service (SaaS) has received significant attention in recent years as major computer companies such as Google, Microsoft, Amazon, and Salesforce are adopting this new approach to develop software and systems. Cloud computing is a computing infrastructure to enable rapid delivery of computing resources as a utility in a dynamic, scalable, and virtualized manner. Computer Simulations are widely utilized to analyze the behaviors of software and test them before fully implementations. Simulation can further benefit SaaS application in a cost-effective way taking the advantages of cloud such as customizability, configurability and multi-tendency.
This research introduces Modeling, Simulation and Analysis for Software-as-Service in Cloud. The researches cover the following topics: service modeling, policy specification, code generation, dynamic simulation, timing, event and log analysis. Moreover, the framework integrates current advantages of cloud: configurability, Multi-Tenancy, scalability and recoverability.
The following chapters are provided in the architecture:
Multi-Tenancy Simulation Software-as-a-Service.
Policy Specification for MTA simulation environment.
Model Driven PaaS Based SaaS modeling.
Dynamic analysis and dynamic calibration for timing analysis.
Event-driven Service-Oriented Simulation Framework.
LTBD: A Triage Solution for SaaS.
This research introduces Modeling, Simulation and Analysis for Software-as-Service in Cloud. The researches cover the following topics: service modeling, policy specification, code generation, dynamic simulation, timing, event and log analysis. Moreover, the framework integrates current advantages of cloud: configurability, Multi-Tenancy, scalability and recoverability.
The following chapters are provided in the architecture:
Multi-Tenancy Simulation Software-as-a-Service.
Policy Specification for MTA simulation environment.
Model Driven PaaS Based SaaS modeling.
Dynamic analysis and dynamic calibration for timing analysis.
Event-driven Service-Oriented Simulation Framework.
LTBD: A Triage Solution for SaaS.
ContributorsLi, Wu (Author) / Tsai, Wei-Tek (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Ye, Jieping (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2015
Description
Emerging from years of research and development, the Internet-of-Things (IoT) has finally paved its way into our daily lives. From smart home to Industry 4.0, IoT has been fundamentally transforming numerous domains with its unique superpower of interconnecting world-wide devices. However, the capability of IoT is largely constrained by the limited resources it can employ in various application scenarios, including computing power, network resource, dedicated hardware, etc. The situation is further exacerbated by the stringent quality-of-service (QoS) requirements of many IoT applications, such as delay, bandwidth, security, reliability, and more. This mismatch in resources and demands has greatly hindered the deployment and utilization of IoT services in many resource-intense and QoS-sensitive scenarios like autonomous driving and virtual reality.
I believe that the resource issue in IoT will persist in the near future due to technological, economic and environmental factors. In this dissertation, I seek to address this issue by means of smart resource allocation. I propose mathematical models to formally describe various resource constraints and application scenarios in IoT. Based on these, I design smart resource allocation algorithms and protocols to maximize the system performance in face of resource restrictions. Different aspects are tackled, including networking, security, and economics of the entire IoT ecosystem. For different problems, different algorithmic solutions are devised, including optimal algorithms, provable approximation algorithms, and distributed protocols. The solutions are validated with rigorous theoretical analysis and/or extensive simulation experiments.
I believe that the resource issue in IoT will persist in the near future due to technological, economic and environmental factors. In this dissertation, I seek to address this issue by means of smart resource allocation. I propose mathematical models to formally describe various resource constraints and application scenarios in IoT. Based on these, I design smart resource allocation algorithms and protocols to maximize the system performance in face of resource restrictions. Different aspects are tackled, including networking, security, and economics of the entire IoT ecosystem. For different problems, different algorithmic solutions are devised, including optimal algorithms, provable approximation algorithms, and distributed protocols. The solutions are validated with rigorous theoretical analysis and/or extensive simulation experiments.
ContributorsYu, Ruozhou, Ph.D (Author) / Xue, Guoliang (Thesis advisor) / Huang, Dijiang (Committee member) / Sen, Arunabha (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2019
Description
Nowadays, wireless communications and networks have been widely used in our daily lives. One of the most important topics related to networking research is using optimization tools to improve the utilization of network resources. In this dissertation, we concentrate on optimization for resource-constrained wireless networks, and study two fundamental resource-allocation problems: 1) distributed routing optimization and 2) anypath routing optimization. The study on the distributed routing optimization problem is composed of two main thrusts, targeted at understanding distributed routing and resource optimization for multihop wireless networks. The first thrust is dedicated to understanding the impact of full-duplex transmission on wireless network resource optimization. We propose two provably good distributed algorithms to optimize the resources in a full-duplex wireless network. We prove their optimality and also provide network status analysis using dual space information. The second thrust is dedicated to understanding the influence of network entity load constraints on network resource allocation and routing computation. We propose a provably good distributed algorithm to allocate wireless resources. In addition, we propose a new subgradient optimization framework, which can provide findgrained convergence, optimality, and dual space information at each iteration. This framework can provide a useful theoretical foundation for many networking optimization problems. The study on the anypath routing optimization problem is composed of two main thrusts. The first thrust is dedicated to understanding the computational complexity of multi-constrained anypath routing and designing approximate solutions. We prove that this problem is NP-hard when the number of constraints is larger than one. We present two polynomial time K-approximation algorithms. One is a centralized algorithm while the other one is a distributed algorithm. For the second thrust, we study directional anypath routing and present a cross-layer design of MAC and routing. For the MAC layer, we present a directional anycast MAC. For the routing layer, we propose two polynomial time routing algorithms to compute directional anypaths based on two antenna models, and prove their ptimality based on the packet delivery ratio metric.
ContributorsFang, Xi (Author) / Xue, Guoliang (Thesis advisor) / Yau, Sik-Sang (Committee member) / Ye, Jieping (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2013