Vol. 34 · No. 5

Cloud computing is becoming one of the preferred paradigms to deploy highly available and scalable systems. These systems usually demand the management of huge amounts of data, which cannot be solved with traditional database systems. Traditional replication protocols are not scalable enough for a cloud environment. This paper evolves different static replication techniques to achieve transactional support providing high availability and scalability as needed in cloud systems. This proposal offers different consistency levels according to the demands of client applications using a replication strategy based on a combination of traditional replication techniques with asynchronous epidemic updates. We have run several simulations that show this is an interesting approach to provide transactional support to clients with different consistency guaranties while leveraging the resources used.

The aspect-oriented software development (AOSD) paradigm permits modularizing crosscutting concerns of base modules, a non-usual task in other software development paradigms. Since AOSD was born in the programming stage as an extension of an object-oriented (OO) programming language, and AOSD considers, in addition to base modules, new modules named aspects, then a complete AOSD process requires that each stage considers the base and aspect modules. Therefore, looking for an AOSD process, mainly to apply AOSD in other phases of the OO software development process, so far, different OO modeling tools and language extensions to support AOSD have been proposed. As an example, AspectZ is an extension of the formal language Z to support AOSD. To reach a transparency of concepts and design in AOSD, the main contribution of this article is to propose OOAspectZ, a formal language for the requirements specification stage of aspect-oriented (AO) software applications, that, firstly, extends AspectZ and, secondly, integrates Object-Z and AspectZ formal specifications. Thus, OOAspectZ supports relevant AO elements such as join points, and This and Target objects for join point events. As an application example, this article applies OOAspectZ to a system named GradUTalca for a Chilean university. For GradUTalca, this article presents AO UML use cases and UML class diagrams, formal Object-Z and OOAspectZ specifications, and a final woven specification to show an integration of Object-Z and OOAspectZ specifications.

Classification problem for imbalanced datasets is pervasive in a lot of data mining domains. Imbalanced classification has been a hot topic in the academic community. From data level to algorithm level, a lot of solutions have been proposed to tackle the problems resulted from imbalanced datasets. SMOTE is the most popular data-level method and a lot of derivations based on it are developed to alleviate the problem of class imbalance. Our investigation indicates that there are severe flaws in SMOTE. We propose a new oversampling method SNOCC that can compensate the defects of SMOTE. In SNOCC, we increase the number of seed samples and that renders the new samples not confine in the line segment between two seed samples in SMOTE. We employ a novel algorithm to find the nearest neighbors of samples, which is different to the previous ones. These two improvements make the new samples created by SNOCC naturally reproduce the distribution of original seed samples. Our experiment results show that SNOCC outperform SMOTE and CBSO (a SMOTE-based method).

This paper first proposes a new graphical model for decision making under uncertainty based on min-based possibilistic networks. A decision problem under uncertainty is described by means of two distinct min-based possibilistic networks: the first one expresses agent's knowledge while the second one encodes agent's preferences representing a qualitative utility. We then propose an efficient algorithm for computing optimistic optimal decisions using our new model for representing possibilistic decision making under uncertainty. We show that the computation of optimal decisions comes down to compute a normalization degree of the junction tree associated with the graph resulting from the fusion of agent's beliefs and preferences. This paper also proposes an alternative way for computing optimal optimistic decisions. The idea is to transform the two possibilistic networks into two equivalent possibilistic logic knowledge bases, one representing agent's knowledge and the other represents agent's preferences. We show that computing an optimal optimistic decision comes down to compute the inconsistency degree of the union of the two possibilistic bases augmented with a given decision.

The work described in this paper is aimed at applying and co-operating of modern information technologies and mathematical modeling to make a risk analysis of the water-supply in big cities. It is instrumental in the investigation of the hydraulics of water-supply systems using the simulation model EPANET executed on the underlying high-performance computing infrastructure. The simulation process is integrated with the GIS environment in order to correct input data and visualize the simulation output. Input data for the model can be modified directly within the designed scientific gateway which enables hydraulic domain experts to interact comfortably with the HPC capacity. Furthermore, the system includes some data mining capabilities forming bridges between the hydraulic data storage and available hydrological measurements focused on water consumption modeling and predictions. In simulating the main emphasis is given to optimize the measure of a similarity between the mathematical model and the real system in order to obtain reliable results.

Recent investigations into proactive network defense have not produced a systematic methodology and structure; in addition, issues including multi-source information fusion and attacking behavior analysis have not been resolved. Borrowing ideas of danger sensing and immune response from danger theory, a proactive network defense model based on danger theory is proposed. This paper defines the signals and antigens in the network environment as well as attacking behavior analysis algorithm, providing evidence for future proactive defense strategy selection. The results of preliminary simulations demonstrate that this model can sense the onset of varied network attacks and corresponding endangered intensities, which help to understand the attack methods of hackers and assess the security situation of the current network, thus a better proactive defense strategy can be deployed. Moreover, this model possesses good robustness and accuracy.

Regression testing is performed throughout the software lifecycle to uncover the faults as early as possible and to ensure that changes do not have any adverse effect in the software that is operational. Test suites once developed are reused and updated frequently. As the software evolves, test cases in the test suite may become redundant. The reason behind this is that the requirements covered by newly added test cases may also be covered by the existing test cases. This redundant nature of test suite increases the cost of executing the same. Further, resource and time constraints impose the necessity to develop techniques to minimize test suites by removing redundant test cases. Few heuristic approaches have been used to solve the test suite minimization problem. Even though solutions exist, still the redundancy of test case remains. In order to solve this problem, this paper proposes two Harrold-Gupta-Soffa (HGS) based heuristic algorithms namely, Non Redundant HGS and Enhanced HGS. The former utilizes the redundant strategy available with Greedy, Redundant, Essential (GRE) to get rid of redundancy, whereas the latter selects a test case for higher cardinalities based on overall coverage of unmarked associated testing sets and thus arrives at reduced, non-redundant test suite. The experiments show that the proposed algorithms always select smaller size of test suite, compared to the existing HGS heuristics.

We study generalized multisets (multisets that allow possible negative multiplicities) both in the Zermelo-Fraenkel framework and in the finitely supported mathematics. We extend the notion of generalized multiset over a finite alphabet, and we replace it by the notion of algebraically finitely supported generalized multiset over a possibly infinite alphabet. We analyze the correspondence between some properties of generalized multisets obtained in finitely supported mathematics where only finitely supported objects are allowed, and those obtained in the classical Zermelo-Fraenkel framework.

In this article we introduce differential evolution based multiple vector prototype classifier (shortly MVDE). In this method we extend the previous DE classifier so that it can handle several class vectors in one class. Classification problems which are so complex that they are simply not separable by using distance based algorithms e.g. differential evolution (DE) classifier or support vector machine (SVM) classifier have troubled researchers for years. In this article, we propose a solution for one area of this problem type in which we extend DE classifier in a way that we allow several class vectors to exist for optimizing one class. This way a part of such complex data can be handled by one vector and other part can be handled by another vector. Differential evolution algorithm is a clear choice for handling such a multiple vector classification tasks because of its remarkable optimization capabilities. MVDE classifier is tested with several different benchmark classification problems to show its capabilities and its performance is compared to DE classifier, SVM and backpropagation neural network classifier. MVDE classifier managed to get best classification performance of these classifiers and clearly indicates it has a potential in this type of classification problems.

Processors are unable to achieve significant gains in speed using the conventional methods. For example increasing the clock rate increases the average access time to on-chip caches which in turn lowers the average number of instructions per cycle of the processor. On-chip memory system will be the major bottleneck in future processors. Software-managed on-chip memories (SMCs) are on-chip caches where software can explicitly read and write some or all of the memory references within a block of caches. This paper footnoteThe work presented in this paper is an expansion of the authors' previously published work in the conference paper citeSMC-HPCS-10, and was carried out when the first author was a Ph.D. student in the Department of Computer Science at University of Victoria. analyzes the current trends for optimizing the use of these SMCs. We separate and compare these trends based on general classifications developed during our study. The paper not only serves as a collection of recent references, information and classifications for easy comparison and analysis but also as a motivation for improving the SMC management framework for embedded systems. It will also make a first step towards making them useful for general purpose multicore processors.