Knowledge Representation Concepts for Automated SLA Management

Outsourcing of complex IT infrastructure to IT service providers has increased substantially during the past years. IT service providers must be able to fulfil their service-quality commitments based upon predefined Service Level Agreements (SLAs) with the service customer. They need to manage, execute and maintain thousands of SLAs for different customers and different types of services, which needs new levels of flexibility and automation not available with the current technology. The complexity of contractual logic in SLAs requires new forms of knowledge representation to automatically draw inferences and execute contractual agreements. A logic-based approach provides several advantages including automated rule chaining allowing for compact knowledge representation as well as flexibility to adapt to rapidly changing business requirements. We suggest adequate logical formalisms for representation and enforcement of SLA rules and describe a proof-of-concept implementation. The article describes selected formalisms of the ContractLog KR and their adequacy for automated SLA management and presents results of experiments to demonstrate flexibility and scalability of the approach.Comment: Paschke, A. and Bichler, M.: Knowledge Representation Concepts for Automated SLA Management, Int. Journal of Decision Support Systems (DSS), submitted 19th March 200

Towards a Decoupled Context-Oriented Programming Language for the Internet of Things

Easily programming behaviors is one major issue of a large and reconfigurable deployment in the Internet of Things. Such kind of devices often requires to externalize part of their behavior such as the sensing, the data aggregation or the code offloading. Most existing context-oriented programming languages integrate in the same class or close layers the whole behavior. We propose to abstract and separate the context tracking from the decision process, and to use event-based handlers to interconnect them. We keep a very easy declarative and non-layered programming model. We illustrate by defining an extension to Golo-a JVM-based dynamic language

An extensible web interface for databases and its application to storing biochemical data

This paper presents a generic web-based database interface implemented in Prolog. We discuss the advantages of the implementation platform and demonstrate the system's applicability in providing access to integrated biochemical data. Our system exploits two libraries of SWI-Prolog to create a schema-transparent interface within a relational setting. As is expected in declarative programming, the interface was written with minimal programming effort due to the high level of the language and its suitability to the task. We highlight two of Prolog's features that are well suited to the task at hand: term representation of structured documents and relational nature of Prolog which facilitates transparent integration of relational databases. Although we developed the system for accessing in-house biochemical and genomic data the interface is generic and provides a number of extensible features. We describe some of these features with references to our research databases. Finally we outline an in-house library that facilitates interaction between Prolog and the R statistical package. We describe how it has been employed in the present context to store output from statistical analysis on to the database.Comment: Online proceedings of the Joint Workshop on Implementation of Constraint Logic Programming Systems and Logic-based Methods in Programming Environments (CICLOPS-WLPE 2010), Edinburgh, Scotland, U.K., July 15, 201

PlinyCompute: A Platform for High-Performance, Distributed, Data-Intensive Tool Development

This paper describes PlinyCompute, a system for development of high-performance, data-intensive, distributed computing tools and libraries. In the large, PlinyCompute presents the programmer with a very high-level, declarative interface, relying on automatic, relational-database style optimization to figure out how to stage distributed computations. However, in the small, PlinyCompute presents the capable systems programmer with a persistent object data model and API (the "PC object model") and associated memory management system that has been designed from the ground-up for high performance, distributed, data-intensive computing. This contrasts with most other Big Data systems, which are constructed on top of the Java Virtual Machine (JVM), and hence must at least partially cede performance-critical concerns such as memory management (including layout and de/allocation) and virtual method/function dispatch to the JVM. This hybrid approach---declarative in the large, trusting the programmer's ability to utilize PC object model efficiently in the small---results in a system that is ideal for the development of reusable, data-intensive tools and libraries. Through extensive benchmarking, we show that implementing complex objects manipulation and non-trivial, library-style computations on top of PlinyCompute can result in a speedup of 2x to more than 50x or more compared to equivalent implementations on Spark.Comment: 48 pages, including references and Appendi