http://dlib.iit.ac.lk/xmlui/handle/123456789/2302 2026-04-06T23:49:38Z http://dlib.iit.ac.lk/xmlui/handle/123456789/2279 A Review of Dynamic Scalability and Dynamic Scheduling in Cloud-Native Distributed Stream Processing Systems Senthuran, Ambalavanar; Hettiarachchi, Saman Scalability is one of the common goals addressed by distributed stream processing systems. Distributed stream processing systems execute streaming applications that are segmented and distributed among several nodes across clusters, in order to cater heavy and growing stream processing use cases. Due to the tremendous benefits provided by cloud infrastructures, distributed stream processing systems are often adapted to be deployed on cloud-native environments. Container orchestrators tend to provide modularity and ease of management for containerised applications. Dynamic scalability and dynamic scheduling of streaming applications become the key points, in addressing the efficiency of cloud-native distributed stream processing systems. In this paper, the author attempts to summarise the researches that have been conducted within the domain of dynamic scaling and dynamic scheduling of stream processing systems, related to cloud-nativeness. 2020-01-01T00:00:00Z http://dlib.iit.ac.lk/xmlui/handle/123456789/2277 A Survey of Attack Instances of Cryptojacking Targeting Cloud Infrastructure Jayasinghe, Keshani; Poravi, Guhanathan Cryptojacking is the act of using an individual's or an organization's computational power in order to mine cryptocurrency. In some scenarios, this can be considered as a monetization strategy, very much similar to advertisements. But to do so without the explicit consent of the computer owners is considered illegitimate. During previous years, attackers' focus was heavily laid on browser-based cryptojacking. However, it was noted that the attackers are now shifting their attention to more robust, more superior targets, such as cloud servers and cloud infrastructure. This paper analyses 11 forms of practical scenarios of cryptojacking attacks that are targeted towards cloud infrastructure. We carefully look at their similarities and properties, comparing those features with the limitations of existing literature regarding the detection systems. In this paper, we survey the attack forms, and we also survey the limitations of existing literature as an attempt to outline the research gap between the practical scenarios and existing work. 2020-01-01T00:00:00Z http://dlib.iit.ac.lk/xmlui/handle/123456789/2276 A Review of Techniques for Image Classification to Enhance Online Animal Adoption Speed Jeyaraj, Pradeepa; Aponso, Achala Overpopulation of stray dogs and cats is a serious threat to human community since they transmit a dangerous viral infection called rabies. Therefore overpopulation of strays needs to be controlled. Strategies to control overpopulation include sterilizing, euthanizing and adopting the strays. The ethical and popular method of acquisition in controlling strays is adoption. Adoptions are mostly carried out by animal shelters. The increase in usage of internet has provided platforms for shelters to advertise adoptions online by sharing pictures. Although there have been past researches on analyzing photo traits affecting the adoption speed, yet simpler improvements are required to guide shelters in posting influential photos. Hence it is essential to use an automated approach to guide shelters in posting influential photos to increase adoption speed. This paper presents the analysis of photo traits affecting adoption speed and computerized automated approaches of image classification. The classification approaches will be reviewed and used in designing an application of the ongoing research to predict the adoption speed of an input animal's image. 2020-01-01T00:00:00Z http://dlib.iit.ac.lk/xmlui/handle/123456789/2275 Machine Learning-Based Approaches for Location Based Dengue Prediction: Review Kalansuriya, Chamalka Seneviratne; Aponso, Achala Chathuranga; Basukoski, Artie Dengue is a fast-spreading viral disease which has no preventive medicine. Due to this infectious disease, almost half of the global population is at risk. Consequently, much research has been conducted using various medical as well as computational methods in order to prevent this menace. The main aim of this paper is to review machine learning approaches to this problem and to identify the most suitable method to predict the spread of this disease for distinctive geographical areas of countries like Sri Lanka. We consider environmental factors such as climate and vegetation data, dengue case data along with the population of a specific geographic area for the disease outbreak predictions. Specifically, this paper consists of the following sections: (i) A brief description of the disease and the factors affecting the spread; (ii) review the pattern of the environmental and population factors affecting the spread; (iii) a review and comparison of machine learning algorithms for prediction of the spread of the disease (SVM, decision tree, neural network, and random forest). 2020-01-01T00:00:00Z