Abstract
Connector colouring provided an intuitive semantics of Reo connectors which lead to effective implementation techniques, first based on computing colouring tables directly, and later on encodings of colouring into constraints. One weakness of the framework is that it operates globally, giving a colouring to all primitives of the connector in lock-step, including those not involved in the interaction. This global approach limits both scalability and the available concurrency. This paper addresses these problems by introducing partiality into the connector colouring model. Partial colourings allow parts of a connector to operate independently and in isolation, increasing scalability and concurrency. © 2012 IFIP International Federation for Information Processing.

Abstract
This paper presents CoopREP, a system that provides support for fault replication of concurrent programs, based on cooperative recording and partial log combination. CoopREP employs partial recording to reduce the amount of information that a given program instance is required to store in order to support deterministic replay. This allows to substantially reduce the overhead imposed by the instrumentation of the code, but raises the problem of finding the combination of logs capable of replaying the fault. CoopREP tackles this issue by introducing several innovative statistical analysis techniques aimed at guiding the search of partial logs to be combined and used during the replay phase. CoopREP has been evaluated using both standard benchmarks for multi-threaded applications and a real-world application. The results highlight that CoopREP can successfully replay concurrency bugs involving tens of thousands of memory accesses, reducing logging overhead with respect to state of the art non-cooperative logging schemes by up to 50 times in computationally intensive applications.

Abstract
Sampling techniques play a key role in achieving efficient network measurements by reducing the amount of traffic processed while trying to maintain the accuracy of network statistical behavior estimation. Despite the evolution of current techniques regarding the correctness of network parameters estimation, the overhead associated with the volume of data involved in the sampling process is still considerable. In this context, this paper proposes a new technique for multiadaptive traffic sampling based on linear prediction, which allows to reduce significantly the traffic under analysis, keeping the representativeness of samples in capturing network behavior. A proof-of-concept, evaluating this technique for real traffic traces representing distinct traffic profiles, demonstrates the effectiveness of the proposal, outperforming classic techniques both in accuracy and data volumes processed. © 2012 Springer-Verlag.

Abstract
Traffic sampling techniques are crucial and extensively used to assist network management tasks. Nevertheless, combining accurate network parameters' estimation and flexible lightweight measurements is an open challenge. In this context, this paper proposes a self-adaptive sampling technique, based on linear prediction, which allows to reduce significantly the measurement overhead, while assuring that sampled traffic reflects the statistical characteristics of the global traffic under analysis. The technique is multiadaptive as several parameters are considered in the dynamic configuration of the traffic selection process. The devised test scenarios aim at exploring the proposed sampling technique ability to join accurate network estimates to reduced overhead, using throughput as reference parameter. The evaluation results, obtained resorting to real traffic traces representing wired and wireless aggregated traffic scenarios and actual network services, prove that the simplicity, flexibility and self-adaptability of this technique can be successfully explored to improve network measurements efficiency over distinct traffic conditions. For optimization purposes, this paper also includes a study of the impact of varying the order of prediction, i.e., of considering different degrees of past memory in the self-adaptive estimation mechanism. The significance of the obtained results is demonstrated through statistical benchmarking.

Abstract
Facing the huge traffic volumes involved in today's networks it is of utmost importance to deploy efficient network measurement solutions to assist network management and traffic engineering tasks correctly, without interfering with normal network operation. Sampling techniques contribute effectively for this purpose as the amount of traffic processed is reduced, ideally without endangering the accuracy of network statistical behavior estimation. Although recent proposals of sampling techniques tend to improve the correctness of the estimation process, their underlying overhead is yet considerably when handling high traffic volumes. This paper proposes a new traffic sampling technique for performing lightweight network measurements. This technique, based on linear prediction, is multiadaptive regarding the packet sampling process, allowing to reduce significantly the amount of traffic under analysis while maintaining the representativeness of network samples for accurate network parameters' estimation. The performance evaluation of the sampling technique demonstrates the effectiveness and versatility of the proposal when considering real traces representing distinct traffic load scenarios. The statistical analysis provided evinces that the present solution outperforms classic sampling techniques, both in accuracy and amount of data involved in the measurement process.

Abstract
Bidirectional Texture Functions (BTF) allow high quality visualization of real world materials exhibiting complex appearance and details that can not be faithfully represented using simpler analytical or parametric representations. Accurate representations of such materials require huge amounts of data, hindering real time rendering. BTFs compress the raw original data, constituting a compromise between visual quality and rendering time. This paper presents an implementation of a state of the art BTF representation on the GPU, allowing interactive high fidelity visualization of complex geometric models textured with multiple BTFs. Scalability with respect to the geometric complexity, amount of lights and number of BTFs is also studied.