395 research outputs found

    Tiered Based Addressing in Internetwork Routing Protocols for the Future Internet

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    The current Internet has exhibited a remarkable sustenance to evolution and growth; however, it is facing unprecedented challenges and may not be able to continue to sustain this evolution and growth in the future because it is based on design decisions made in the 1970s when the TCP/IP concepts were developed. The research thus has provided incremental solutions to the evolving Internet to address every new vulnerabilities. As a result, the Internet has increased in complexity, which makes it hard to manage, more vulnerable to emerging threats, and more fragile in the face of new requirements. With a goal towards overcoming this situation, a clean-slate future Internet architecture design paradigm has been suggested by the research communities. This research is focused on addressing and routing for a clean-slate future Internet architecture, called the Floating Cloud Tiered (FCT) internetworking model. The major goals of this study are: (i) to address the two related problems of routing scalability and addressing, through an approach which would leverage the existing structures in the current Internet architecture, (ii) to propose a solution that is acceptable to the ISP community that supports the Internet, and lastly (iii) to provide a transition platform and mechanism which is very essential to the successful deployment of the proposed design

    Virtual Mobility Domains - A Mobility Architecture for the Future Internet

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    The advances in hardware and wireless technologies have made mobile communication devices affordable by a vast user community. With the advent of rich multimedia and social networking content, an influx of myriads of applications, and Internet supported services, there is an increasing user demand for the Internet connectivity anywhere and anytime. Mobility management is thus a crucial requirement for the Internet today. This work targets novel mobility management techniques, designed to work with the Floating Cloud Tiered (FCT) internetworking model, proposed for a future Internet. We derive the FCT internetworking model from the tiered structure existing among Internet Service Provider (ISP) networks, to define their business and peering relationships. In our novel mobility management scheme, we define Virtual Mobility Domains (VMDs) of various scopes, that can support both intra and inter-domain roaming using a single address for a mobile node. The scheme is network based and hence imposes no operational load on the mobile node. This scheme is the first of its kind, by leveraging the tiered structure and its hierarchical properties, the collaborative network-based mobility management mechanism, and the inheritance information in the tiered addresses to route packets. The contributions of this PhD thesis can be summarized as follows: · We contribute to the literature with a comprehensive analysis of the future Internet architectures and mobility protocols over the period of 2002-2012, in light of their identity and handoff management schemes. We present a qualitative evaluation of current and future schemes on a unified platform. · We design and implement a novel user-centric future Internet mobility architecture called Virtual Mobility Domain. VMD proposes a seamless, network-based, unique collaborative mobility management within/across ASes and ISPs in the FCT Internetworking model. The analytical and simulation-based handoff performance analysis of the VMD architecture in comparison with the IPv6-based mobility protocols presents the considerable performance improvements achieved by the VMD architecture. · We present a novel and user-centric handoff cost framework to analyze handoff performance of different mobility schemes. The framework helps to examine the impacts of registration costs, signaling overhead, and data loss for Internet connected mobile users employing a unified cost metric. We analyze the effect of each parameter in the handoff cost framework on the handoff cost components. We also compare the handoff performance of IPv6-based mobility protocols to the VMD. · We present a handoff cost optimization problem and analysis of its characteristics. We consider a mobility user as the primary focus of our study. We then identify the suitable mathematical methods that can be leveraged to solve the problem. We model the handoff cost problem in an optimization tool. We also conduct a mobility study - best of our knowledge, first of its kind - on providing a guide for finding the number of handoffs in a typical VMD for any given user\u27s mobility model. Plugging the output of mobility study, we then conduct a numerical analysis to find out optimum VMD for a given user mobility model and check if the theoretical inferences are in agreement with the output of the optimization tool

    Configurable data center switch architectures

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    In this thesis, we explore alternative architectures for implementing con_gurable Data Center Switches along with the advantages that can be provided by such switches. Our first contribution centers around determining switch architectures that can be implemented on Field Programmable Gate Array (FPGA) to provide configurable switching protocols. In the process, we identify a gap in the availability of frameworks to realistically evaluate the performance of switch architectures in data centers and contribute a simulation framework that relies on realistic data center traffic patterns. Our framework is then used to evaluate the performance of currently existing as well as newly proposed FPGA-amenable switch designs. Through collaborative work with Meng and Papaphilippou, we establish that only small-medium range switches can be implemented on today's FPGAs. Our second contribution is a novel switch architecture that integrates a custom in-network hardware accelerator with a generic switch to accelerate Deep Neural Network training applications in data centers. Our proposed accelerator architecture is prototyped on an FPGA, and a scalability study is conducted to demonstrate the trade-offs of an FPGA implementation when compared to an ASIC implementation. In addition to the hardware prototype, we contribute a light weight load-balancing and congestion control protocol that leverages the unique communication patterns of ML data-parallel jobs to enable fair sharing of network resources across different jobs. Our large-scale simulations demonstrate the ability of our novel switch architecture and light weight congestion control protocol to both accelerate the training time of machine learning jobs by up to 1.34x and benefit other latency-sensitive applications by reducing their 99%-tile completion time by up to 4.5x. As for our final contribution, we identify the main requirements of in-network applications and propose a Network-on-Chip (NoC)-based architecture for supporting a heterogeneous set of applications. Observing the lack of tools to support such research, we provide a tool that can be used to evaluate NoC-based switch architectures.Open Acces

    On the evaluation of exact-match and range queries over multidimensional data in distributed hash tables

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    2012 Fall.Includes bibliographical references.The quantity and precision of geospatial and time series observational data being collected has increased alongside the steady expansion of processing and storage capabilities in modern computing hardware. The storage requirements for this information are vastly greater than the capabilities of a single computer, and are primarily met in a distributed manner. However, distributed solutions often impose strict constraints on retrieval semantics. In this thesis, we investigate the factors that influence storage and retrieval operations on large datasets in a cloud setting, and propose a lightweight data partitioning and indexing scheme to facilitate these operations. Our solution provides expressive retrieval support through range-based and exact-match queries and can be applied over massive quantities of multidimensional data. We provide benchmarks to illustrate the relative advantage of using our solution over a general-purpose cloud storage engine in a distributed network of heterogeneous computing resources

    Green cloud software engineering for big data processing

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    © 2020 by the authors. Licensee MDPI, Basel, Switzerland. Internet of Things (IoT) coupled with big data analytics is emerging as the core of smart and sustainable systems which bolsters economic, environmental and social sustainability. Cloud-based data centers provide high performance computing power to analyze voluminous IoT data to provide invaluable insights to support decision making. However, multifarious servers in data centers appear to be the black hole of superfluous energy consumption that contributes to 23% of the global carbon dioxide (CO2) emissions in ICT (Information and Communication Technology) industry. IoT-related energy research focuses on low-power sensors and enhanced machine-to-machine communication performance. To date, cloud-based data centers still face energy-related challenges which are detrimental to the environment. Virtual machine (VM) consolidation is a well-known approach to affect energy-efficient cloud infrastructures. Although several research works demonstrate positive results for VM consolidation in simulated environments, there is a gap for investigations on real, physical cloud infrastructure for big data workloads. This research work addresses the gap of conducting real physical cloud infrastructure-based experiments. The primary goal of setting up a real physical cloud infrastructure is for the evaluation of dynamic VM consolidation approaches which include integrated algorithms from existing relevant research. An open source VM consolidation framework, Openstack NEAT is adopted and experiments are conducted on a Multi-node Openstack Cloud with Apache Spark as the big data platform. Open sourced Openstack has been deployed because it enables rapid innovation, and boosts scalability as well as resource utilization. Additionally, this research work investigates the performance based on service level agreement (SLA) metrics and energy usage of compute hosts. Relevant results concerning the best performing combination of algorithms are presented and discussed

    Self-Evolving Integrated Vertical Heterogeneous Networks

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    6G and beyond networks tend towards fully intelligent and adaptive design in order to provide better operational agility in maintaining universal wireless access and supporting a wide range of services and use cases while dealing with network complexity efficiently. Such enhanced network agility will require developing a self-evolving capability in designing both the network architecture and resource management to intelligently utilize resources, reduce operational costs, and achieve the coveted quality of service (QoS). To enable this capability, the necessity of considering an integrated vertical heterogeneous network (VHetNet) architecture appears to be inevitable due to its high inherent agility. Moreover, employing an intelligent framework is another crucial requirement for self-evolving networks to deal with real-time network optimization problems. Hence, in this work, to provide a better insight on network architecture design in support of self-evolving networks, we highlight the merits of integrated VHetNet architecture while proposing an intelligent framework for self-evolving integrated vertical heterogeneous networks (SEI-VHetNets). The impact of the challenges associated with SEI-VHetNet architecture, on network management is also studied considering a generalized network model. Furthermore, the current literature on network management of integrated VHetNets along with the recent advancements in artificial intelligence (AI)/machine learning (ML) solutions are discussed. Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are identified. Finally, the potential future research directions for advancing the autonomous and self-evolving capabilities of SEI-VHetNets are discussed.Comment: 25 pages, 5 figures, 2 table

    Improving Application Performance in the Emerging Hyper-converged Infrastructure

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    University of Minnesota Ph.D. dissertation.April 2019. Major: Computer Science. Advisor: David Du. 1 computer file (PDF); viii, 118 pages.In today's world, the hyper-converged infrastructure is emerging as a new type of infrastructure. In the hyper-converged infrastructure, service providers deploy compute, network and storage services on inexpensive hardware rather than expensive proprietary hardware. It allows the service providers to customize the services they can provide by deploying applications in Virtual Machines (VMs) or containers. They can have controls on all resources including compute, network and storage. In this hyper-converged infrastructure, improving the application performance is an important issue. Throughout my Ph.D. research, I have been studying how to improve the performance of applications in the emerging hyper-converged infrastructure. I have been focusing on improving the performance of applications in VMs and in containers when accessing data, and how to improve the performance of applications in the networked storage environment. In the hyper-converged infrastructure, administrators can provide desktop services by deploying Virtual Desktop Infrastructure application (VDI) based on VMs. We first investigate how to identify storage requirements and determine how to meet such requirements with minimal storage resources for VDI application. We create a model to describe the behavior of VDI, and collect real VDI traces to populate this model. The model allows us to identify the storage requirements of VDI and determine the potential bottlenecks in storage. Based on this information, we can tell what capacity and minimum capability a storage system needs in order to support and satisfy a given VDI configuration. We show that our model can describe more fine-grained storage requirements of VDI compared with the rules of thumb which are currently used in industry. In the hyper-converged infrastructure, more and more applications are running in containers. We design and implement a system, called k8sES (k8s Enhanced Storage), that efficiently supports applications with various storage SLOs (Service Level Objectives) along with all other requirements deployed in the Kubernetes environment which is based on containers. Kubernetes (k8s) is a system for managing containerized applications across multiple hosts. The current storage support for containerized applications in k8s is limited. To satisfy users' SLOs, k8s administrators must manually configure storage in advance, and users must know the configurations and capabilities of different types of the provided storage. In k8sES, storage resources are dynamically allocated based on users' requirements. Given users' SLOs, k8sES will select the correct node and storage that can meet their requirements when scheduling applications. The storage allocation mechanism in k8sES also improves the storage utilization efficiency. In addition, we provide a tool to monitor the I/O activities of both applications and storage devices in Kubernetes. With the capabilities of controlling client, network and storage with hyper-convergence, we study how to coordinate different components along the I/O path to ensure latency SLOs for applications in the networked storage environment. We propose and implement JoiNS, a system trying to ensure latency SLOs for applications that access data on remote networked storage. JoiNS carefully considers all the components along the I/O path and controls them in a coordinated fashion. JoiNS has both global network and storage visibility with a logically centralized controller which keeps monitoring the status of each involved component. JoiNS coordinates these components and adjusts the priority of I/Os in each component based on the latency SLO, network and storage status, time estimation, and characteristics of each I/O request

    Integration of LXD System Containers with OpenStack, CHEF and its Application on a 3-Tier IoT Architecture

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    Internet of Things has moved from being a 2-tier server-client into a 3-tier server-gateway-client architecture. The gateway plays a vital role in this 3-tier architecture with intelligence being built into it. With no proper standardization and with more vendors having proprietary apps, which are shared in this multi-tenant gateway, it demands sandboxing and isolation of apps at the gateway. My thesis explores light weight LXD System containers and state of the art configuration management tools like Chef, to build an architecture, leveraging Infrastructure as a Code, creating an app delivery pipeline to deploy apps in jailed environments at an IoT Gateway while maintaining a minimal overhead. The framework also provides ways to automate tests for deployment validation
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