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

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A Break in the Clouds: Towards a Cloud Definition

The Transmission Control Protocol.

Technical foundations introduction software reliability and system reliability the operational profile software reliability modelling survey model evaluation and recalibration techniques practices and experiences best current practice of SRE software reliability measurement experience measurement-based analysis of software reliability software fault and failure classification techniques trend analysis in validation and maintenance software reliability and field data analysis software reliability process assessment emerging techniques software reliability prediction metrics software reliability and testing fault-tolerant SRE software reliability using fault trees software reliability process simulation neural networks and software reliability. Appendices: software reliability tools software failure data set repository.

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Handbook of software reliability engineering

An improved method and apparatus is used for storing and delivering information over the Internet and using Internet technologies. According to one embodiment of the present invention, a method and apparatus for maintaining statistics on a server (204) is disclosed. According to an alternative embodiment, a method and apparatus (204) is disclosed for predicting data that a client device (100) may request from a server on a network. In another embodiment of the present invention, a method and apparatus (204) is disclosed for managing bandwith between a client device (100) and a network. According to yet another embodiment, a method and apparatus (204) is disclosed for validating a collection of data (200). According to yet another embodiment, a method for providing notification to clients (100) from servers (204) is disclosed.

A method and apparatus for storing and delivering documents on the internet

A scalable distributed caching system on a network receives a request for a data object from a user. The caching system carries out a locator function that locates a directory cache for the object. The directory cache stores a directory list that identifies the locations of object caches that purport to store copies of the object requested by the user. The object caches on the object directory list are polled, and in response send messages to the cache that received the user request indicating if each object cache stores a copy of the requested object. The receiving cache sends a message requesting a copy of the object to the object cache that sent the message first received by the receiving cache indicating that an object cache stores the requested object. The object cache that sent the first received message then sends a copy of the object to the receiving cache, which stores a copy and then sends a copy to the user. The directory list for the object is then updated by adding the network address of the receiving cache. Outdated copies of objects stored on object caches are deleted in a distributed fashion to maintain the coherence of the cached copies. This is further reinforced by the association of time-to-live parameters with the each copy and each object cache address on directory lists.

Scalable distributed caching system and method

Small-scale multiprocessors are becoming increasingly economical and common, whereas larger multiprocessors continue to have higher per-node costs. The NUMAchine multiprocessor project seeks to make large-scale multiprocessors more economical while maintaining high performance by exploring architectural and hardware features for low-cost, modular multiprocessors. To demonstrate our approach, we have implemented a prototype system that is scalable to 128 processors. An efficient directory-based cache coherence protocol exploits our hierarchical ring-based interconnect and supports sequential consistency. This paper documents the design choices and the resulting performance of the system using both simulation results and measurements on the prototype hardware.

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The NUMAchine multiprocessor

While constructing QoS-aware composite work-flows based on service oriented systems, it is necessary to assess nonfunctional properties of potential service selection candidates. In this paper, we present CLUS , a model for reliability prediction of atomic web services that estimates the reliability for an ongoing service invocation based on the data assembled from previous invocations. With the aim to improve the accuracy of the current state-of-the-art prediction models, we incorporate user-service-, and environment-specific parameters of the invocation context. To reduce the scalability issues present in the state-of-the-art approaches, we aggregate the past invocation data using K-means clustering algorithm. In order to evaluate different quality aspects of our model, we conducted experiments on services deployed in different regions of the Amazon cloud. The evaluation results confirm that our model produces more scalable and accurate predictions when compared to the current state-of-the-art approaches.

Prediction of Atomic Web Services Reliability for QoS-Aware Recommendation

Contemporary web applications are often designed as composite services built by coordinating atomic services with the aim of providing the appropriate functionality. Although functional properties of each atomic service assure correct functionality of the entire application, nonfunctional properties such as availability, reliability, or security might significantly influence the user-perceived quality of the application. In this paper, we present CLUS, a model for reliability prediction of atomic web services that improves state-of-the-art approaches used in modern recommendation systems. CLUS predicts the reliability for the ongoing service invocation using the data collected from previous invocations. We improve the accuracy of the current state-of-the-art prediction models by considering user-, service- and environment-specific parameters of the invocation context. To address the computational performance related to scalability issues, we aggregate the available previous invocation data using K-means clustering algorithm. We evaluated our model by conducting experiments on services deployed in different regions of the Amazon cloud. The evaluation results suggest that our model improves both performance and accuracy of the prediction when compared to the current state-of-the-art models.

https://ccl.fer.hr/wp-content/uploads/2013/07/CLUS-PrePrint.pdf

Prediction of atomic web services reliability based on k-means clustering

The modern information systems on the Internet are often implemented as composite services built from multiple atomic services. These atomic services have their interfaces publicly available while their inner structure is unknown. The quality of the composite service is dependent on both the availability of each atomic service and their appropriate orchestration. In this paper, we present LUCS, a formal model for predicting the availability of atomic web services that enhances the current state-of-the-art models used in service recommendation systems. LUCS estimates the service availability for an ongoing request by considering its similarity to prior requests according to the following dimensions: the user's and service's geographic location, the service load, and the service's computational requirements. In order to evaluate our model, we conducted experiments on services deployed in different regions of the Amazon cloud. For each service, we varied the geographic origin of its incoming requests as well as the request frequency. The evaluation results suggest that our model significantly improves availability prediction when all of the LUCS input parameters are available, reducing the prediction error by 71 percent compared to the current state-of-the-art.

Sinisa Srbljic

Papers

Scalable distributed caching system and method

The NUMAchine multiprocessor

Prediction of Atomic Web Services Reliability for QoS-Aware Recommendation

Prediction of atomic web services reliability based on k-means clustering

Scalable and Accurate Prediction of Availability of Atomic Web Services