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

The computing world today is in the middle of a revolution: mobile clients and cloud computing have emerged as the dominant paradigms driving programming and hardware innovation today. The Fifth Edition of Computer Architecture focuses on this dramatic shift, exploring the ways in which software and technology in the "cloud" are accessed by cell phones, tablets, laptops, and other mobile computing devices. Each chapter includes two real-world examples, one mobile and one datacenter, to illustrate this revolutionary change. Updated to cover the mobile computing revolutionEmphasizes the two most important topics in architecture today: memory hierarchy and parallelism in all its forms.Develops common themes throughout each chapter: power, performance, cost, dependability, protection, programming models, and emerging trends ("What's Next")Includes three review appendices in the printed text. Additional reference appendices are available online.Includes updated Case Studies and completely new exercises.

Computer Architecture, Fifth Edition: A Quantitative Approach

Resilience and survivability in communication networks: Strategies, principles, and survey of disciplines

Handbook of logic in computer science.

On-chip interconnection networks are rapidly becoming a key enabling technology for commodity multicore processors and SoCs common in consumer embedded systems, the National Science Foundation initiated a workshop that addressed upcoming research issues in OCIN technology, design, and implementation and set a direction for researchers in the field.

Research Challenges for On-Chip Interconnection Networks

As the Internet takes an increasingly central role in our communications infrastructure, the slow convergence of routing protocols after a network failure becomes a growing problem. To assure fast recovery from link and node failures in IP networks, we present a new recovery scheme called Multiple Routing Configurations (MRC). MRC is based on keeping additional routing information in the routers, and allows packet forwarding to continue on an alternative output link immediately after the detection of a failure. Our proposed scheme guarantee s recovery in all single failure scenarios, using a single mechanism to handle both link and node failures, and without knowing the root cause of the failure. MRC is strictly connectionless, and assumes only destination based hop-by-hop forwarding. It can be implemented with only minor changes to existing solutions. In this paper we present MRC, and analyze its performance with respect to scalability, backup path lengths, and load distribution after a failure.

Fast IP Network Recovery Using Multiple Routing Configurations

As the Internet takes an increasingly central role in our communications infrastructure, the slow convergence of routing protocols after a network failure becomes a growing problem. To assure fast recovery from link and node failures in IP networks, we present a new recovery scheme called Multiple Routing Configurations (MRC). Our proposed scheme guarantees recovery in all single failure scenarios, using a single mechanism to handle both link and node failures, and without knowing the root cause of the failure. MRC is strictly connectionless, and assumes only destination based hop-by-hop forwarding. MRC is based on keeping additional routing information in the routers, and allows packet forwarding to continue on an alternative output link immediately after the detection of a failure. It can be implemented with only minor changes to existing solutions. In this paper we present MRC, and analyze its performance with respect to scalability, backup path lengths, and load distribution after a failure. We also show how an estimate of the traffic demands in the network can be used to improve the distribution of the recovered traffic, and thus reduce the chances of congestion when MRC is used.

Multiple routing configurations for fast IP network recovery

Freedom from deadlock is a key issue in cut-through, wormhole, and store and forward networks, and such freedom is usually obtained through careful design of the routing algorithm. Most existing deadlock-free routing methods for irregular topologies do, however, impose severe limitations on the available routing paths. We present a method called layered routing, which gives rise to a series of routing algorithms, some of which perform considerably better than previous ones. Our method groups virtual channels into network layers and to each layer it assigns a limited set of source/destination address pairs. This separation of traffic yields a significant increase in routing efficiency. We show how the method can be used to improve the performance of irregular networks, both through load balancing and by guaranteeing shortest-path routing. The method is simple to implement, and its application does not require any features in the switches other than the existence of a modest number of virtual channels. The performance of the approach is evaluated through extensive experiments within three classes of technologies. These experiments reveal a need for virtual channels as well as an improvement in throughput for each technology class.

Layered routing in irregular networks

Most standard cluster interconnect technologies are flexible with respect to network topology. This has spawned a substantial amount of research on topology-agnostic routing algorithms, which make no assumption about the network structure, thus providing the flexibility needed to route on irregular networks. Actually, such an irregularity should be often interpreted as minor modifications of some regular interconnection pattern, such as those induced by faults. In fact, topology-agnostic routing algorithms are also becoming increasingly useful for networks on chip (NoCs), where faults may make the preferred 2D mesh topology irregular. Existing topology-agnostic routing algorithms were developed for varying purposes, giving them different and not always comparable properties. Details are scattered among many papers, each with distinct conditions, making comparison difficult. This paper presents a comprehensive overview of the known topology-agnostic routing algorithms. We classify these algorithms by their most important properties, and evaluate them consistently. This provides significant insight into the algorithms and their appropriateness for different on- and off-chip environments.

A Survey and Evaluation of Topology-Agnostic Deterministic Routing Algorithms

Massively parallel computing systems are being built with thousands of nodes. The interconnection network plays a key role for the performance of such systems. However, the high number of components significantly increases the probability of failure. Additionally, failures in the interconnection network may isolate a large fraction of the machine. It is therefore critical to provide an efficient fault-tolerant mechanism to keep the system running, even in the presence of faults. This paper presents a new fault-tolerant routing methodology that does not degrade performance in the absence of faults and tolerates a reasonably large number of faults without disabling any healthy node. In order to avoid faults, for some source-destination pairs, packets are first sent to an intermediate node and then from this node to the destination node. Fully adaptive routing is used along both subpaths. The methodology assumes a static fault model and the use of a checkpoint/restart mechanism. However, there are scenarios where the faults cannot be avoided solely by using an intermediate node. Thus, we also provide some extensions to the methodology. Specifically, we propose disabling adaptive routing and/or using misrouting on a per-packet basis. We also propose the use of more than one intermediate node for some paths. The proposed fault-tolerant routing methodology is extensively evaluated in terms of fault tolerance, complexity, and performance.

/pdf/a-routing-methodology-for-achieving-fault-tolerance-in-1xpo79m6cp.pdf

Olav Lysne

Papers

Fast IP Network Recovery Using Multiple Routing Configurations

Multiple routing configurations for fast IP network recovery

Layered routing in irregular networks

A Survey and Evaluation of Topology-Agnostic Deterministic Routing Algorithms

A routing methodology for achieving fault tolerance in direct networks