Maintaining consistency in distributed systems

In systems designed as assemblies of independently developed components, concurrent access to data or data structures normally arises within individual programs, and is controlled using mutual exclusion constructs, such as semaphores and monitors. Where data is persistent and/or sets of operation are related to one another, transactions or linearizability may be more appropriate. Systems that incorporate cooperative styles of distributed execution often replicate or distribute data within groups of components. In these cases, group oriented consistency properties must be maintained, and tools based on the virtual synchrony execution model greatly simplify the task confronting an application developer. All three styles of distributed computing are likely to be seen in future systems - often, within the same application. This leads us to propose an integrated approach that permits applications that use virtual synchrony with concurrent objects that respect a linearizability constraint, and vice versa. Transactional subsystems are treated as a special case of linearizability

Robust data storage in a network of computer systems

PhD ThesisRobustness of data in this thesis is taken to mean reliable storage of data and also high availability of data .objects in spite of the occurrence of faults. Algorithms and data structures which can be used to provide such robustness in the presence of various disk, processor and communication network failures are described. Reliable storage of data at individual nodes in a network of computer systems is based on the use of a stable storage mechanism combined with strategies which are used to help ensure crash resis- tance of file operations in spite of the use of buffering mechan- isms by operating systems. High availability of data in the net- work is maintained by replicating data on different computers and mutual consistency between replicas is ensured in spite of network partitioning. A stable storage system which provides atomicity for more complex data structures instead of the usual fixed size page has been designed and implemented and its performance evaluated. A crash resistant file system has also been implemented and evaluated. Many of the techniques presented here are used in the design of what we call CRES (Crash-resistant, Replicated and Stable) storage. CRES storage provides fault tolerance facilities for various disk and processor faults. It also provides fault tolerance facilities for network partitioning through the provision of an algorithm for the update and merge of a partitioned data storage system

Timestamp-Based Approach for the Detection and Resolution of Mutual Conflicts in Distributed Systems

We present a timestamp based algorithm for the detection of both write-write and read-write conflicts for a single file in distributed systems during network partitions. Our algorithm allows operations to occur in different network partitions simultaneously. When the sites from different partitions merge, the algorithm detects and resolves both read-write and write-write conflicts without taking into account the semantics of the transactions. Once the conflicts have been detected some reconciliation steps for the resolution of conflicts have also been proposed. Our algorithm will be useful in real-time systems where timeliness of operations is more important than response time (delayed commit

A categorization scheme for concurrency control protocols in distributed databases

The problem of concurrency control in distributed databases is very complex. As a result, a great number of control algorithms have been proposed in recent years. This research is aimed at the development of a viable categorization scheme for these various algorithms. The scheme is based on the theoretical concept of serializability, but is qualitative in nature. An important class of serializable execution sequences, conflict-preserving-serializable, leads to the identification of fundamental attributes common to all algorithms included in this study. These attributes serve as the underlying philosophy for the categorization scheme. Combined with the two logical approaches of prevention and correction of nonserializability, the result is a flexible and extensive categorization scheme which accounts for all algorithms studied and suggests the possibility of new algorithms

On Practical machine Learning and Data Analysis

This thesis discusses and addresses some of the difficulties associated with practical machine learning and data analysis. Introducing data driven methods in e.g industrial and business applications can lead to large gains in productivity and efficiency, but the cost and complexity are often overwhelming. Creating machine learning applications in practise often involves a large amount of manual labour, which often needs to be performed by an experienced analyst without significant experience with the application area. We will here discuss some of the hurdles faced in a typical analysis project and suggest measures and methods to simplify the process. One of the most important issues when applying machine learning methods to complex data, such as e.g. industrial applications, is that the processes generating the data are modelled in an appropriate way. Relevant aspects have to be formalised and represented in a way that allow us to perform our calculations in an efficient manner. We present a statistical modelling framework, Hierarchical Graph Mixtures, based on a combination of graphical models and mixture models. It allows us to create consistent, expressive statistical models that simplify the modelling of complex systems. Using a Bayesian approach, we allow for encoding of prior knowledge and make the models applicable in situations when relatively little data are available. Detecting structures in data, such as clusters and dependency structure, is very important both for understanding an application area and for specifying the structure of e.g. a hierarchical graph mixture. We will discuss how this structure can be extracted for sequential data. By using the inherent dependency structure of sequential data we construct an information theoretical measure of correlation that does not suffer from the problems most common correlation measures have with this type of data. In many diagnosis situations it is desirable to perform a classification in an iterative and interactive manner. The matter is often complicated by very limited amounts of knowledge and examples when a new system to be diagnosed is initially brought into use. We describe how to create an incremental classification system based on a statistical model that is trained from empirical data, and show how the limited available background information can still be used initially for a functioning diagnosis system. To minimise the effort with which results are achieved within data analysis projects, we need to address not only the models used, but also the methodology and applications that can help simplify the process. We present a methodology for data preparation and a software library intended for rapid analysis, prototyping, and deployment. Finally, we will study a few example applications, presenting tasks within classification, prediction and anomaly detection. The examples include demand prediction for supply chain management, approximating complex simulators for increased speed in parameter optimisation, and fraud detection and classification within a media-on-demand system