Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Network virtualization is recognized as an enabling technology for the future Internet. It aims to overcome the resistance of the current Internet to architectural change. Application of this technology relies on algorithms that can instantiate virtualized networks on a substrate infrastructure, optimizing the layout for service-relevant metrics. This class of algorithms is commonly known as "Virtual Network Embedding (VNE)" algorithms. This paper presents a survey of current research in the VNE area. Based upon a novel classification scheme for VNE algorithms a taxonomy of current approaches to the VNE problem is provided and opportunities for further research are discussed.

Virtual Network Embedding: A Survey

Since wireless network virtualization enables abstraction and sharing of infrastructure and radio spectrum resources, the overall expenses of wireless network deployment and operation can be reduced significantly. Moreover, wireless network virtualization can provide easier migration to newer products or technologies by isolating part of the network. Despite the potential vision of wireless network virtualization, several significant research challenges remain to be addressed before widespread deployment of wireless network virtualization, including isolation, control signaling, resource discovery and allocation, mobility management, network management and operation, and security as well as non-technical issues such as governance regulations, etc. In this paper, we provide a brief survey on some of the works that have already been done to achieve wireless network virtualization, and discuss some research issues and challenges. We identify several important aspects of wireless network virtualization: overview, motivations, framework, performance metrics, enabling technologies, and challenges. Finally, we explore some broader perspectives in realizing wireless network virtualization.

Wireless Network Virtualization: A Survey, Some Research Issues and Challenges

With the proliferation of mobile demands and increasingly multifarious services and applications, mobile Internet has been an irreversible trend. Unfortunately, the current mobile and wireless network (MWN) faces a series of pressing challenges caused by the inherent design. In this paper, we extend two latest and promising innovations of Internet, software-defined networking and network virtualization, to mobile and wireless scenarios. We first describe the challenges and expectations of MWN, and analyze the opportunities provided by the software-defined wireless network (SDWN) and wireless network virtualization (WNV). Then, this paper focuses on SDWN and WNV by presenting the main ideas, advantages, ongoing researches and key technologies, and open issues respectively. Moreover, we interpret that these two technologies highly complement each other, and further investigate efficient joint design between them. This paper confirms that SDWN and WNV may efficiently address the crucial challenges of MWN and significantly benefit the future mobile and wireless network.

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Software-Defined and Virtualized Future Mobile and Wireless Networks: A Survey

Expert Cloud

Rate Adaptation (RA) is a fundamental mechanism in 802.11 systems. It allows transmitters to adapt the coding and modulation scheme as well as the MIMO transmission mode to the radio channel condit ...

/pdf/optimal-rate-sampling-in-802-11-systems-mpabyr9jrk.pdf

Optimal Rate Sampling in 802.11 Systems

Rate Adaptation (RA) is a fundamental mechanism in 80211 systems It allows transmitters to adapt the coding and modulation scheme as well as the MIMO transmission mode to the radio channel conditions, to learn and track the (mode, rate) pair providing the highest throughput The design of RA mechanisms has been mainly driven by heuristics In contrast, we rigorously formulate RA as an online stochastic optimization problem We solve this problem and present G-ORS (Graphical Optimal Rate Sampling), a family of provably optimal (mode, rate) pair adaptation algorithms Our main result is that G-ORS outperforms state-of-the-art algorithms such as MiRA and Minstrel HT, as demonstrated by experiments on a 80211n network test-bed The design of G-ORS is supported by a theoretical analysis, where we study its performance in stationary radio environments where the successful packet transmission probabilities at the various (mode, rate) pairs do not vary over time, and in non-stationary environments where these probabilities evolve We show that under G-ORS, the throughput loss due to the need to explore sub-optimal (mode, rate) pairs does not depend on the number of available pairs This is a crucial advantage as evolving 80211 standards offer an increasingly large number of (mode, rate) pairs We illustrate the superiority of G-ORS over state-of-the-art algorithms, using both trace-driven simulations and test-bed experiments

Optimal Rate Sampling in 802.11 Systems: Theory, Design, and Implementation

Network virtualization has become one of the key technologies in the future Internet research. Compared to significant attention to network virtualization in wired networks, wireless virtualization has just started to gain attention recently, where most research has focused on implementation issues and/or single-hop cellular systems. In this paper, we consider network virtualization in wireless multi-hop networks, where a critical algorithmic component, virtual network embedding problem, is studied. We discuss the key challenges, propose our preliminary algorithm, and validates its performance.

Virtual network embedding in wireless multihop networks

http://lanada.kaist.ac.kr/Publication/Journal/Embedding_of_Virtual_Network_Requests_over_Static_wireless_Multihop_Networks.pdf

Embedding of virtual network requests over static wireless multihop networks

The data explosion and resource scarcity of mobile cellular networks require new paradigms to effectively integrate heterogeneous radio resources. Of many candidate approaches, smart aggregation of LTE and Wi-Fi radios is a promising solution that bonds heterogeneous links to meet a mobile terminal’s bandwidth need. Motivated by the existence of a significant number of carrier operated Wi-Fi APs, we propose an easily deployable mechanism, called LTE-W, which efficiently utilizes LTE and Wi-Fi links only with the minimum change of eNodeBs, LTE backhaul networks, and mobile terminals. LTE-W, which is a link-level aggregation mechanism, has the following two key components: 1) mode selection and 2) bearer-split scheduling. First, in the mode selection, LTE-W internally decides who should be served by either LTE-only or LTE-Wi-Fi aggregation considering intra-cell fairness rather than just following users’ intention of aggregation. For the users’ preference to be offered the aggregation service, we choose a bearer (roughly defined in LTE as a set of flows with a similar QoS) as a basic unit of aggregation and propose a smart intra-bearer scheduling algorithm that splits a bearer’s traffic into LTE and Wi-Fi links, considering the performance of TCP flows that take two heterogeneous wireless links. We evaluate our mechanism using the NS-3 with LENA, under various configurations, including nodes with mobility and HTTP traffic, and compare it with a transport-level aggregation mechanism, multipath TCP (MPTCP), demonstrating that LTE-W significantly improves MPTCP, e.g., up to 75% in terms of Jain’s fairness index.

Donggyu Yun

Papers

Optimal Rate Sampling in 802.11 Systems

Optimal Rate Sampling in 802.11 Systems: Theory, Design, and Implementation

Virtual network embedding in wireless multihop networks

Embedding of virtual network requests over static wireless multihop networks

Aggregating LTE and Wi-Fi: Toward Intra-Cell Fairness and High TCP Performance