Scalable Cloud Application Deployment Service for Versatile Cloud Service Deployment and Configuration

We present a cloud management service called RAIN. It has been designed specifically for versatility and scalability of operation, allowing for the processing of a large number of requests at the same time. Its operation is transactional and controlled by a workflow of operations forming one requisition. Requisitions and their operations can be executed in parallel, allowing for high throughput and scalability of the controlled cloud environment(s). The service is being used in day-to-day operations in a commercial environment. It is also designed for high failure tolerance, which is necessary when operating on third party cloud infrastructures. It has been developed and actively used for several years now, giving us a mature tool with many important features added over time, allowing for practical day-to-day operations. The architecture of the service is open and easily extendable to allow the inclusion of new cloud services of various types -- PaaS providers as well as providers of higher-level services. The service is accessed via an asynchronous REST API. It allows the caller to resume execution and not wait for cloud deployment operations to take an arbitrary amount of time to finish, receiving progress updates via a simple callback REST API

Novel Approach to Hide Sensitive Association Rules by Introducing Transaction Affinity

In this paper, a novel approach has been proposed for hiding sensitive association rules based on the affinity between the frequent items of the transaction. The affinity between the items is defined as Jaccard similarity. This work proposes five algorithms to ensure the minimum side-effects resulting after applying sanitization algorithms to hide sensitive knowledge. Transaction affinity has been introduced which is calculated by adding the affinity of frequent items present in the transaction with the victim-item (item to be modified). Transactions are selected either by increasing or decreasing value of affinity for data distortion to hide association rules. The first two algorithms, MaxaffinityDSR and MinaffinityDSR, hide the sensitive information by selecting the victim item as the right-hand side of the sensitive association rule. The next two algorithms, MaxaffinityDSL and MinaffinityDSL, select the victim item from the left-hand side of the rule whereas the Hybrid approach picks the victim item from either the left-hand side or right-hand side. The performance of proposed algorithms has been evaluated by comparison with state-of-art methods (Algo 1.a and Algo 1.b), MinFIA, MaxFIA and Naive algorithms. The experiments were performed using the dataset generated from IBM synthetic data generator, and implementation has been performed in R language

Vigilant Salp Swarm Algorithm for Feature Selection

Feature selection (FS) averts the consideration of unwanted features which may tend the classification algorithm to classify wrongly. Choosing an optimal feature subset from the given set of features is challenging due to the complex associations present within the features. In non-convex conditions, the gradient-based algorithms suffer due to local optima or saddle points with respect to initial conditions where swarm intelligence algorithms pose a higher chance to converge over the global optima. The Salp Swarm Algorithm (SSA) proposed by Mirjalili et al. is based on the chaining behaviour of sea salps but the algorithm lacks diversity in the exploration stage. Rectifying the exploratory behaviour and testing the algorithm against the FS problem is the motivation behind this work. Three variants of the algorithm are proposed, of which the Vigilant Salp Swarm Algorithm (VSSA) inherits the vigilant mechanism in Grey Wolf Optimizer (GWO), the second variant and the third variant replace a simple crossover operator and shuffle crossover operator instead of the follower's position update mechanism used in the VSSA to form Vanilla Crossover VSSA (VCVSSA) and Shuffle Crossover VSSA (SCVSSA)

Mobile Edge Computing Based Immersive Virtual Reality Streaming Scheme

Recently, new services using virtual reality (VR)/augmented reality (AR) have appeared and then exploded in entertainment fields like video games and multimedia contents. In order to efficiently provide these services to users, an infrastructure for mobile cloud computing with powerful computing capabilities is widely utilized. However, existing mobile cloud system utilizes a cloud server located at a relatively long distance, so that there are problems that a user is not effectively provided with personalized immersive multimedia service. So, this paper proposes the home VR streaming system that can provide fast content access time and high immersiveness by using mobile edge computing (MEC)

Application of the Fuzzy Model Theory for Modeling QA-Systems

The work is devoted to the description of the question-answer system QA-RiskPanel, which provides means of determining and forecasting the risks related to computer attacks. The QA-RiskPanel system uses a constantly updated database of previous computer attacks as a source of knowledge. We thus guarantee the most up-to-date risk prediction. The ontological approach to the formalization of the object domain allows the analysis of risks at various levels of specification/generalization. In this paper we provide a model-theoretic formalization of the Knowledge Base of the described object domain. Then we describe the classification of question types, which are probabilistic in this system. Finally we present algorithms for finding the answers to all question types of our classification

Performance Evaluation of Parallel Haemodynamic Computations on Heterogeneous Clouds

The article presents performance evaluation of parallel haemodynamic flow computations on heterogeneous resources of the OpenStack cloud infrastructure. The main focus is on the parallel performance analysis, energy consumption and virtualization overhead of the developed software service based on ANSYS Fluent platform which runs on Docker containers of the private university cloud. The haemodynamic aortic valve flow described by incompressible Navier-Stokes equations is considered as a target application of the hosted cloud infrastructure. The parallel performance of the developed software service is assessed measuring the parallel speedup of computations carried out on virtualized heterogeneous resources. The performance measured on Docker containers is compared with that obtained by using the native hardware. The alternative solution algorithms are explored in terms of the parallel performance and power consumption. The investigation of a trade-off between the computing speed and the consumed energy is performed by using Pareto front analysis and a linear scalarization method

Event Detection in Twitter Using Multi Timing Chained Windows

Twitter is a popular microblogging and social networking service. Twitter posts are continuously generated and well suited for knowledge discovery using different data mining techniques. We present a novel near real-time approach for processing tweets and detecting events. The proposed method, Multi Timing Chained Windows (MTCW), is independent of the language of the tweets. The MTCW defines several Timing Windows and links them to each other like a chain. Indeed, in this chain, the input of the larger window will be the output of the smaller previous one. Using MTCW, the events can be detected over a few minutes. To evaluate this idea, the required dataset has been collected using the Twitter API. The results of evaluations show the accuracy and the effectiveness of our approach compared with other state-of-the-art methods in the event detection in Twitter

Improvement of Information Retrieval Systems by Using Hidden Vertical Search

The exponential growth of the number of documents in digital libraries and on the Web calls for very intensive development of retrieval systems. One possible architectural approach to IRS, an architecture with hidden verticals, is proposed in this paper. In IRS with hidden verticals, documents from the searched corpus are stored into a predefined set of classes. The user's query is classified before the search, and searching is done only within the corresponding class. The performance of the proposed system is compared to the performance of standard IRS (that contains a unique inverted index) and IRS with cluster pruning (in which searching corpus is clustered and query is compared to the clusters' centroids first, then search is done only in the most similar cluster). Search time in the proposed system is 7.9 times shorter than in the standard IRS and 1.7 times shorter than in the system with cluster pruning. The precision of the proposed system is 2.59 times higher than the precision of the standard IRS, and 1.68 times better compared to the IRS with cluster pruning. The recall of the proposed system is 1.09 times smaller than the recall of the standard IRS, but it is 1.28 times better than the recall of the IRS with cluster pruning. Based on the above results, we can say that proposed approach reduces search time and increases search precision with a minimal reduction in recall

Open Hybrid Model: A New Ensemble Model for Software Development Cost Estimation

Given various features of a software project, it may face different administrative challenges requiring right decisions by software project managers. A major challenge is to estimate software development cost for which different methods have been proposed by many researchers. According to the literature, the capability of a proposed model or method is demonstrated in a specific set of software projects. Hence, the aim of this study is to present a model to take advantage of the capabilities of various software development cost estimation models and methods simultaneously. For this purpose, a new model called "open hybrid model" was proposed based on the firefly algorithm. The proposed model includes an extensible bank of estimation methods. The model also includes an extensible bank of rules to describe the relation between existing methods. Considering project conditions, the proposed model tries to find the best rule for combining estimation methods in the methods bank. Three datasets of real projects were used to evaluate the precision of the proposed model, and the results were compared with those of other 11 methods. The results were compared based on performance parmeters widely used to show the accuracy and stability of estimation models. According to the results, the open hybrid model was able to select the most appropriate methods present in the methods bank