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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

National Institute of Standards and Technology における超伝導研究及び生活

This survey covers rollback-recovery techniques that do not require special language constructs. In the first part of the survey we classify rollback-recovery protocols into checkpoint-based and log-based.Checkpoint-based protocols rely solely on checkpointing for system state restoration. Checkpointing can be coordinated, uncoordinated, or communication-induced. Log-based protocols combine checkpointing with logging of nondeterministic events, encoded in tuples called determinants. Depending on how determinants are logged, log-based protocols can be pessimistic, optimistic, or causal. Throughout the survey, we highlight the research issues that are at the core of rollback-recovery and present the solutions that currently address them. We also compare the performance of different rollback-recovery protocols with respect to a series of desirable properties and discuss the issues that arise in the practical implementations of these protocols.

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A survey of rollback-recovery protocols in message-passing systems

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[서평]「Computer Organization and Design, The Hardware/Software Interface」

Whether it is for audit or for recovery purposes, data checkpointing is an important problem of transaction systems. Actually, transactions establish dependence relations on data checkpoints taken by data object managers. So, given an arbitrary set of data checkpoints (including at least a single data checkpoint from a data manager, and at most a data checkpoint from each data manager), an important question is the following one: \\Can these data checkpoints be members of a same consistent global checkpoint?\". This paper answers this question by providing a necessary and suucient condition suited to transaction systems. Moreover, to show its usefulness, two non-intrusive data checkpointing protocols are designed from this condition.

Consistent checkpointing for transaction systems

One approach to fully exploit the potential of Cloud technologies consists in leveraging on the Autonomic Computing paradigm. It could be exploited in order to put in place reconfiguration strategies spanning the whole protocol stack, starting from the infrastructure and then going up to platform/application level protocols. On the other hand, the very base for the design and development of Cloud oriented Autonomic Managers is represented by monitoring sub-systems, able to provide audit data related to any layer within the stack. In this article we present the approach that has been taken while designing and implementing the monitoring sub-system for the Cloud-TM FP7 project, which is aimed at realizing a self-adapting, Cloud based middleware platform providing transactional data access to generic customer applications.

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Integrated monitoring of infrastructures and applications in cloud environments

The large diffusion of highly-parallel shared-memory multi-core machines has led Parallel Discrete Event Simulation (PDES) platforms to a shift towards a share-everything model. This model is based on loose coupling between simulation objects and threads, lasting (as an extreme) no more than the lifetime of individual events. Concurrent threads can therefore CPU-dispatch events destined to any object at any point in time, thus fully sharing the workload of events to be processed on a fine grain basis. This demands for efficient mechanisms to share the overall pool of pending events by enabling parallelism in insertion and extraction operations. In this article we present a lock-free event pool which also provides amortized O(1) time complexity for both insertions and extractions. It can sustain highly concurrent accesses, while not leading to noticeable performance degradation when scaling up the thread count. Experimental results demonstrate that our solution stands as a core facility capable of further raising up the pragmatical impact of such an emerging share-everything PDES paradigm.

/pdf/a-lock-free-o-1-event-pool-and-its-application-to-share-5bso9yvxq0.pdf

A Lock-Free O(1) Event Pool and its Application to Share-Everything PDES Platforms

In this paper we present a software approach, namely Fast-Software-Checkpointing (FSC), to reduce the running time of the state saving protocol in optimistic parallel discrete event simulation. The idea behind FSC is to use the instructions performed during the execution of an event as part of the state saving protocol, hence the total number of instructions due to state saving is reduced. Under FSC the time for saving the state of a logical process prior to the execution of an event e requires an amount of time proportional to the amount of state variables not updated by e's execution, as only these variables must be copied. This outlines that FSC shows some dualism with respect to incremental state saving. We show, however, that there exists a basic difference between the two solutions as in FSC some of the state saving instructions are actually event routine instructions, while in incremental state saving they are only added and mixed to the latter ones. We also present a simple software architecture to support FSC and simulation results to demonstrate the effectiveness of such solution. The obtained data show that FSC, combined with a sparse state saving strategy, may represent the best checkpointing solution in case of both medium/small state granularity simulations and large state granularity simulations even with small (but non-minimal) portions of the state updated by event execution. FSC may result therefore suited for a wide class of simulation problems.

Fast-software-checkpointing in optimistic simulation: embedding state saving into the event routine instructions

Checkpointing distributed applications involving mobile hosts is an important task to reduce the rollback during a recovery from a failure and to manage voluntary disconnections. In this paper we show the basic characteristics a checkpointing protocol needs to work with mobile hosts, namely, reduction of the number of checkpoints, the use of incremental checkpointing and consistent global checkpoint built on the fly. Previous points must be implemented by using as small control information as possible and ensuring little rollback. A comparative analysis of the performance of some interesting communication-induced checkpointing protocols, adapted to a mobile setting, is presented. The analysis has been carried out by using discrete event simulation and several models have been considered for the hosts mobility.

Francesco Quaglia

Papers

Consistent checkpointing for transaction systems

Integrated monitoring of infrastructures and applications in cloud environments

A Lock-Free O(1) Event Pool and its Application to Share-Everything PDES Platforms

Fast-software-checkpointing in optimistic simulation: embedding state saving into the event routine instructions

Checkpointing protocols in distributed systems with mobile hosts: A performance analysis