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」

In this article we introduce GMU, a genuine partial replication protocol for transactional systems, which exploits an innovative, highly scalable, distributed multiversioning scheme. Unlike existing multiversion-based solutions, GMU does not rely on a global logical clock, which represents a contention point and can limit system scalability. Also, GMU never aborts read-only transactions and spares them from distributed validation schemes. This makes GMU particularly efficient in presence of read-intensive workloads, as typical of a wide range of real-world applications. GMU guarantees the Extended Update Serializability (EUS) isolation level. This consistency criterion is particularly attractive as it is sufficiently strong to ensure correctness even for very demanding applications (such as TPC-C), but is also weak enough to allow efficient and scalable implementations, such as GMU. Further, unlike several relaxed consistency models proposed in literature, EUS has simple and intuitive semantics, thus being an attractive, scalable consistency model for ordinary programmers. We integrated the GMU protocol in a popular open source in-memory transactional data grid, namely Infinispan. On the basis of a large scale experimental study performed on heterogeneous experimental platforms and using industry standard benchmarks (namely TPC-C and YCSB), we show that GMU achieves linear scalability and that it introduces negligible overheads (less than 10%), with respect to solutions ensuring non-serializable semantics, in a wide range of workloads.

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When Scalability Meets Consistency: Genuine Multiversion Update-Serializable Partial Data Replication

In this article we present SCORe, a scalable one-copy serializable partial replication protocol. Differently from any other literature proposal, SCORe jointly guarantees the following properties: (i) it is genuine, thus ensuring that only the replicas that maintain data accessed by a transaction are involved in its processing, and (ii) it guarantees that read operations always access consistent snapshots, thanks to a one-copy serializable multiversion scheme, which never aborts read-only transactions and spares them from any (distributed) validation phase. This makes SCORe particularly efficient in presence of read-intensive workloads, as typical of a wide range of real-world applications. We have integrated SCORe into a popular open source distributed data grid and performed a large scale experimental study with well-known benchmarks using both private and public cloud infrastructures. The experimental results demonstrate that SCORe provides stronger consistency guarantees (namely One-Copy Serializability) than existing multiversion partial replication protocols at no additional overhead.

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SCORe: a scalable one-copy serializable partial replication protocol

Recent papers have shown that the performance of Time Warp simulators can be improved by appropriately selecting the positions of checkpoints, instead of taking them on a periodic basis. In this paper, we present a checkpointing technique in which the selection of the positions of checkpoints is based on a checkpointing-recovery cost model. Given the current state S, the model determines the convenience of recording S as a checkpoint before the next event is executed. This is done by taking into account the position of the last taken checkpoint, the granularity (i.e., the execution time) of intermediate events, and using an estimate of the probability that S will have to be restored due to rollback in the future of the execution. A synthetic benchmark in different configurations is used for evaluating and comparing this approach to classical periodic techniques. As a testing environment we used a cluster of PCs connected through a Myrinet switch coupled with a fast communication layer specifically designed to exploit the potential of this type of switch. The obtained results point out that our solution allows faster execution and, in some cases, exhibits the additional advantage that less memory is required for recording state vectors. This possibly contributes to further performance improvements when memory is a critical resource for the specific application. A performance study for the case of a cellular phone system simulation is finally reported to demonstrate the effectiveness of this solution for a real world application.

A cost model for selecting checkpoint positions in time warp parallel simulation

This paper presents an index-based checkpointing algorithm for distributed systems with the aim of reducing the total number of checkpoints while ensuring that each checkpoint belongs to at least one consistent global checkpoint (or recovery line). The algorithm is based on an equivalence relation defined between pairs of successive checkpoints of a process which allows us, in some cases, to advance the recovery line of the computation without forcing checkpoints in other processes. The algorithm is well-suited for autonomous and heterogeneous environments, where each process does not know any private information about other processes and private information of the same type of distinct processes is not related (e.g., clock granularity, local checkpointing strategy, etc.). We also present a simulation study which compares the checkpointing-recovery overhead of this algorithm to the ones of previous solutions.

An index-based checkpointing algorithm for autonomous distributed systems

Classical approaches to performance prediction rely on two, typically antithetic, techniques: Machine Learning (ML) and Analytical Modeling (AM). ML takes a black box approach, whose accuracy strongly depends on the representativeness of the dataset used during the initial training phase. Specifically, it can achieve very good accuracy in areas of the features' space that have been sufficiently explored during the training process. Conversely, AM techniques require no or minimal training, hence exhibiting the potential for supporting prompt instantiation of the performance model of the target system. However, in order to ensure their tractability, they typically rely on a set of simplifying assumptions. Consequently, AM's accuracy can be seriously challenged in scenarios (e.g., workload conditions) in which such assumptions are not matched.In this paper we explore several hybrid/gray box techniques that exploit AM and ML in synergy in order to get the best of the two worlds. We evaluate the proposed techniques in case studies targeting two complex and widely adopted middleware systems: a NoSQL distributed key-value store and a Total Order Broadcast (TOB) service.

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Francesco Quaglia

Papers

When Scalability Meets Consistency: Genuine Multiversion Update-Serializable Partial Data Replication

SCORe: a scalable one-copy serializable partial replication protocol

A cost model for selecting checkpoint positions in time warp parallel simulation

An index-based checkpointing algorithm for autonomous distributed systems

Enhancing Performance Prediction Robustness by Combining Analytical Modeling and Machine Learning