facultad de informática universidad politécnica de madrid

facultad de informática universidad politécnica de madrid

Abstract. Compile-time program analysis techniques can be applied to Web service orchestrations to prove or check various properties. In particular, service orchestrations can be subjected to resource analysis, in which safe approximations of upper and lower resource usage bounds are deduced. A uniform analysis can be simultaneously performed for different generalized resources that can be directly correlated with cost- and performance-related quality attributes, such as invocations of partners, network traffic, number of activities, iterations, and data accesses. The resulting safe upper and lower bounds do not depend on probabilistic assumptions, and are expressed as functions of size or length of data components from an initiating message, using a finegrained structured data model that corresponds to the XML-style of information structuring. The analysis is performed by transforming a BPEL-like representation of an orchestration into an equivalent program in another programming language for which the appropriate analysis tools already exist.

Automatic parallelization has become a mainstream research topic for different reasons. For example, multicore architectures, which are now present even in laptops, have awakened an interest in software tools that can exploit the computing power of parallel processors. Distributed and (multi)agent systems also benefit from techniques and tools for deciding in which locations should processes be run to make a better use of the available resources. Any decision on whether to execute some processes in parallel or sequentially must ensure correctness (i.e., the parallel execution obtains the same results as the sequential), but also has to take into account a number of practical overheads, such as those associated with tasks creation, possible migration of tasks to remote processors, the associated communication overheads, etc. Due to these overheads and if the granularity of parallel tasks, i.e., the “work available ” underneath them, is too small, it may happen that the costs are larger than the benefits in their parallel execution. Thus, the aim of granularity control is to change parallel execution to sequential execution or vice-versa based on some conditions related to grain size and overheads. In this work, we have applied fuzzy logic to automatic granularity control in parallel/distributed computing and proposed fuzzy conditions for deciding whether to execute some given tasks in parallel or sequentially. We have compared our proposed fuzzy conditions with existing (conservative) sufficient conditions and our experiments showed that the proposed fuzzy conditions result in more efficient executions on average than the conservative conditions. 1

Abstract. (Non-)portability of Prolog programs is widely considered as an important factor in the lack of acceptance of the language. Since 1995, the core of the language is covered by the ISO standard 13211-1. Since 2007, YAP and SWI-Prolog have established a basic compatibility framework. This article describes and evaluates this framework. The aim of the framework is running the same code on both systems rather than migrating an application. We show that today, the portability within the family of Edinburgh/Quintus derived Prolog implementations is good enough to allow for maintaining portable real-world applications. 1

Abstract—Several activities in service oriented computing can benefit from the knowledge of properties of a given service composition ahead of time. We will focus here on properties related to computational cost and resource usage, in a wide sense, which can be linked to QoS characteristics. In order to attain more accuracy, we formulate computational cost / resource usage as functions on input data (or appropriate abstractions thereof) and show how these functions can be used to make more informed decisions when performing composition, proactive adaptation, and predictive monitoring. We present an approach to, on one hand, automatically synthesize these functions from orchestrations and, on the other hand, to effectively use them to increase the quality of nontrivial service-based systems with data-dependent behavior. We validate our approach by means of simulations with runtime selection of services and adaptation due to service failure. Keywords-orchestrations; resource usage analysis; data awareness; monitoring; adaptation I.

facultad de informática universidad politécnica de madrid

This chapter gives an introduction to all the main programming paradigms, their underlying concepts, and the relationships between them. We give a broad view to help programmers choose the right concepts they need to solve the problems at hand. We give a taxonomy of almost 30 useful programming paradigms and how they are related. Most of them differ only in one or a few concepts, but this can make a world of difference in programming. We explain briefly how programming paradigms influence language design, and we show two sweet spots: dual-paradigm languages and a definitive language. We introduce the main concepts of programming languages: records, closures, independence (concurrency), and named state. We explain the main principles of data abstraction and how it lets us organize large programs. Finally, we conclude by focusing on concurrency, which is widely considered the hardest concept to program with. We present four little-known but important paradigms that greatly simplify concurrent programming with respect to mainstream languages: declarative concurrency (both eager and lazy), functional reactive programming, discrete synchronous programming, and constraint programming. These paradigms have no race conditions and can be used in cases where no other paradigm works. We explain why for multi-core processors and we give several examples from computer music, which often uses these paradigms. More is not better (or worse) than less, just different. – The paradigm paradox. 1

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