1,555 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Dynamic Virtual Network Restoration with Optimal Standby Virtual Router Selection
Title form PDF of title page, viewed on September 4, 2015Dissertation advisor: Deep MedhiVitaIncludes bibliographic references (pages 141-157)Thesis (Ph.D.)--School of Computing and Engineering and Department of Mathematics and Statistics. University of Missouri--Kansas City, 2015Network virtualization technologies allow service providers to request partitioned,
QoS guaranteed and fault-tolerant virtual networks provisioned by the substrate network
provider (i.e., physical infrastructure provider). A virtualized networking environment
(VNE) has common features such as partition, flexibility, etc., but fault-tolerance requires
additional efforts to provide survivability against failures on either virtual networks or the
substrate network.
Two common survivability paradigms are protection (proactive) and restoration
(reactive). In the protection scheme, the substrate network provider (SNP) allocates redundant
resources (e.g., nodes, paths, bandwidths, etc) to protect against potential failures
in the VNE. In the restoration scheme, the SNP dynamically allocates resources to restore
the networks, and it usually occurs after the failure is detected.
In this dissertation, we design a restoration scheme that can be dynamically implemented
in a centralized manner by an SNP to achieve survivability against node failures
in the VNE. The proposed restoration scheme is designed to be integrated with a protection
scheme, where the SNP allocates spare virtual routers (VRs) as standbys for the
virtual networks (VN) and they are ready to serve in the restoration scheme after a node
failure has been identified. These standby virtual routers (S-VR) are reserved as a sharedbackup
for any single node failure, and during the restoration procedure, one of the S-VR
will be selected to replace the failed VR. In this work, we present an optimal S-VR selection
approach to simultaneously restore multiple VNs affected by failed VRs, where
these VRs may be affected by failures within themselves or at their substrate host (i.e.,
power outage, hardware failures, maintenance, etc.). Furthermore, the restoration scheme
is embedded into a dynamic reconfiguration scheme (DRS), so that the affected VNs can
be dynamically restored by a centralized virtual network manager (VNM).
We first introduce a dynamic reconfiguration scheme (DRS) against node failures
in a VNE, and then present an experimental study by implementing this DRS over a
realistic VNE using GpENI testbed. For this experimental study, we ran the DRS to
restore one VN with a single-VR failure, and the results showed that with a proper S-VR
selection, the performance of the affected VN could be well restored. Next, we proposed
an Mixed-Integer Linear Programming (MILP) model with dual–goals to optimally select
S-VRs to restore all VNs affected by VR failures while load balancing. We also present a
heuristic algorithm based on the model. By considering a number of factors, we present
numerical studies to show how the optimal selection is affected. The results show that
the proposed heuristic’s performance is close to the optimization model when there were
sufficient standby virtual routers for each virtual network and the substrate nodes have
the capability to support multiple standby virtual routers to be in service simultaneously.
Finally, we present the design of a software-defined resilient VNE with the optimal S-VR
selection model, and discuss a prototype implementation on the GENI testbed.Introduction -- Literature survey -- Dynamic reconfiguration scheme in a VNE -- An experimental study on GpENI-VNI -- Optimal standby virtual router selection model -- Prototype design and implementation on GENI -- Conclusion and future work -- Appendix A. Resource Specification (RSpec) in GENI -- Appendix B. Optimal S-VR Selection Model in AMP
Software-Driven and Virtualized Architectures for Scalable 5G Networks
In this dissertation, we argue that it is essential to rearchitect 4G cellular core networks–sitting between the Internet and the radio access network–to meet the scalability, performance, and flexibility requirements of 5G networks. Today, there is a growing consensus among operators and research community that software-defined networking (SDN), network function virtualization (NFV), and mobile edge computing (MEC) paradigms will be the key ingredients of the next-generation cellular networks. Motivated by these trends, we design and optimize three core network architectures, SoftMoW, SoftBox, and SkyCore, for different network scales, objectives, and conditions. SoftMoW provides global control over nationwide core networks with the ultimate goal of enabling new routing and mobility optimizations. SoftBox attempts to enhance policy enforcement in statewide core networks to enable low-latency, signaling-efficient, and customized services for mobile devices. Sky- Core is aimed at realizing a compact core network for citywide UAV-based radio networks that are going to serve first responders in the future. Network slicing techniques make it possible to deploy these solutions on the same infrastructure in parallel. To better support mobility and provide verifiable security, these architectures can use an addressing scheme that separates network locations and identities with self-certifying, flat and non-aggregatable address components. To benefit the proposed architectures, we designed a high-speed and memory-efficient router, called Caesar, for this type of addressing schemePHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146130/1/moradi_1.pd
Ono: an open platform for social robotics
In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform
Performance analysis and optimization in software defined networks
In this thesis, candidate addressed two interesting and practical problems: performance analysis and optimization for (1) controllers and (2) switches in Software-Defined Networks. Candidate developed a queueing theory based optimization framework in a distributed SDN architecture that provides QoS-guaranteed flow-balancing in pro-active operations of SDN controllers. Further, candidate developed an analytical model for modeling SDN switches. The results in this thesis will contribute to the design and development of future Software-Defined Networks.<br /
Optimization and Management Techniques for Geo-distributed SDN-enabled Cloud Datacenters\u27 Provisioning
Cloud computing has become a business reality that impacts technology users around the world. It has become a cornerstone for emerging technologies and an enabler of future Internet services as it provides on-demand IT services delivery via geographically distributed data centers. At the core of cloud computing, virtualization technology has played a crucial role by allowing resource sharing, which in turn allows cloud service providers to offer computing services without discrepancies in platform compatibility.
At the same time, a trend has emerged in which enterprises are adopting a software-based network infrastructure with paradigms, such as software-defined networking, gaining further attention for large-scale networks. This trend is due to the flexibility and agility offered to networks by such paradigms. Software-defined networks allow for network resource sharing by facilitating network virtualization. Hence, combining cloud computing with a software-defined network architecture promises to enhance the quality of services that are delivered to clients and reduces the operational costs to service providers. However, this combined architecture introduces several challenges to cloud service providers, including resource management, energy efficiency, virtual network provisioning, and controller placement.
This thesis tackles these challenges by proposing innovative resource provisioning techniques and developing novel frameworks to improve resource utilization, power efficiency, and quality of service performance. These metrics have a direct impact on the capital and operational expenditure of service providers.
In this thesis, the problem of virtual computing and network provisioning in geographically distributed software-defined network-enabled cloud data centers is modeled and formulated. It proposes and evaluates optimal and sub-optimal heuristic solutions to validate their efficiency. To address the energy efficiency of cloud environments that are enabled for software-defined networks, this thesis presents an innovative architecture and develops a comprehensive power consumption model that accurately describes the power consumption behavior of such environments. To address the challenge of the number of software-defined network controllers and locations, a sub-optimal solution is proposed that combines unsupervised hierarchical clustering. Finally, betweenness centrality is proposed as an efficient solution to the controller placement problem
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