Abstract
Due to the large number of optimizations provided in modern compilers and to compiler optimization specific opportunities, a Design Space Exploration (DSE) is necessary to search for the best sequence of compiler optimizations for a given code fragment (e. g., function). As this exploration is a complex and time consuming task, in this paper we present DSE strategies to select optimization sequences to both improve the performance of each function and reduce the exploration time. The DSE is based on a clustering approach which groups functions with similarities and then explore the reduced search space provided by the optimizations previously suggested for the functions in each group. The identification of similarities between functions uses a data mining method which is applied to a symbolic code representation of the source code. The DSE process uses the reduced set identified by clustering in two ways: as the design space or as the initial configuration. In both ways, the adoption of a pre-selection based on clustering allows the use of simple and fast DSE algorithms. Our experiments for evaluating the effectiveness of the proposed approach address the exploration of compiler optimization sequences considering 49 compilation passes and targeting a Xilinx MicroBlaze processor, and were performed aiming performance improvements for 41 functions. Experimental results reveal that the use of our new clustering-based DSE approach achieved a significant reduction on the total exploration time of the search space (18 x over a Genetic Algorithm approach for DSE) at the same time that important performance speedups (43% over the baseline) were obtained by the optimized codes.

Abstract
This paper presents an approach accounting for the classification of the main knowledge resources related to the new Science of Service. The main knowledge categories are defined as concepts integrated in an extended Service Science ontology. The ontology derived from several sources was captured using UML and Protege, and then, through a RDF/OWL transformation, a semantically annotated wiki has been directly implemented offering an execution of the ontology together with implemented use cases. Further, a dedicated application was developed - the Service Science Knowledge Environment (SSKE) in order to grant user access to different knowledge categories created along with the proposed ontology. The SSKE is a cloud based collaborative software service, aiming at providing co-created knowledge resources shared by academia, industry and government organizations. This application can be accessed through the Web (http://sske.cloud.upb.ro/) and it can be used for managing service related knowledge.

Abstract

Abstract
Developments in autonomic network management offer many promises, but its economic benefits are hard to assess. This paper proposes a method to calculate the OPEX gains of a typical network on the basis of a scenario, for which a management framework and autonomous mechanisms have been developed. It makes use of a novel approach, a Toy model, taking into account expert opinions as well as simulation results. The new approach allows assessment of the OPEX impact of both individual mechanisms as well as the overall impact, which, in the present scenario results in an expected OPEX saving of 11 to 13 per cent. © 2014 IEEE.

Abstract
This work proposes a hybrid genetic algorithm (GA) to address the unit commitment (UC) problem. In the UC problem, the goal is to schedule a subset of a given group of electrical power generating units and also to determine their production output in order to meet energy demands at minimum cost. In addition, the solution must satisfy a set of technological and operational constraints. The algorithm developed is a hybrid biased random key genetic algorithm (HBRKGA). It uses random keys to encode the solutions and introduces bias both in the parent selection procedure and in the crossover strategy. To intensify the search close to good solutions, the GA is hybridized with local search. Tests have been performed on benchmark large-scale power systems. The computational results demonstrate that the HBRKGA is effective and efficient. In addition, it is also shown that it improves the solutions obtained by current state-of-the-art methodologies.

Abstract
Despite being staggeringly error prone, spreadsheets are a highly flexible programming environment that is widely used in industry. In fact, spreadsheets are widely adopted for decision making, and decisions taken upon wrong (spreadsheet-based) assumptions may have serious economical impacts on businesses, among other consequences. This paper proposes a technique to automatically pinpoint potential faults in spreadsheets. It combines a catalog of spreadsheet smells that provide a first indication of a potential fault, with a generic spectrum-based fault localization strategy in order to improve (in terms of accuracy and false positive rate) on these initial results. Our technique has been implemented in a tool which helps users detecting faults. To validate the proposed technique, we consider a well-known and well-documented catalog of faulty spreadsheets. Our experiments yield two main results: we were able to distinguish between smells that can point to faulty cells from smells and those that are not capable of doing so; and we provide a technique capable of detecting a significant number of errors: two thirds of the cells labeled as faulty are in fact (documented) errors.