A Review on mobile SMS Spam filtering techniques

Under short messaging service (SMS) spam is understood the unsolicited or undesired messages received on mobile phones. These SMS spams constitute a veritable nuisance to the mobile subscribers. This marketing practice also worries service providers in view of the fact that it upsets their clients or even causes them lose subscribers. By way of mitigating this practice, researchers have proposed several solutions for the detection and filtering of SMS spams. In this paper, we present a review of the currently available methods, challenges, and future research directions on spam detection techniques, filtering, and mitigation of mobile SMS spams. The existing research literature is critically reviewed and analyzed. The most popular techniques for SMS spam detection, filtering, and mitigation are compared, including the used data sets, their findings, and limitations, and the future research directions are discussed. This review is designed to assist expert researchers to identify open areas that need further improvement

A cloud-based conceptual framework for multi-objective virtual machine scheduling using whale optimization algorithm

Virtual machine scheduling in the cloud is considered one of the major issue to solve optimal resource allocation problem on the heterogeneous datacenters. With respect to that, the key concern is to map the virtual machines (VMs) with physical machines (PMs) in a way that maximum resource utilization can be achieved with minimum cost. Due to the fact that scheduling is an NP-hard problem, a metaheuristic approach is proven to achieve a better optimal solution to solve this problem. In a rapid changing heterogeneous environment, where millions of resources can be allocated and deallocate in a fraction of the time, modern metaheuristic algorithms perform well due to its immense power to solve the multidimensional problem with fast convergence speed. This paper presents a conceptual framework for solving multi-objective VM scheduling problem using novel metaheuristic Whale optimization algorithm (WOA). Further, we present the problem formulation for the framework to achieve multi-objective functions

Scheduling techniques in on-demand grid as a service cloud: a review

The Infrastructure as a service (IaaS) Cloud is a customer oriented cloud environment that offers user with computing infrastructures on-demand to be used based on the Cloud computing paradigm of pay-per-use. When the IaaS is now utilized to build a traditional Grid network within the cloud environment, it is now called an on-demand Grid as a service (GaaS) Cloud. In the on-demand GaaS Cloud, a user may use hundred of thousand of Grid nodes to implement a job, therefore manual scheduling is not a feasible scheduling solution. The main objective of this review is to study the various concepts and scheduling algorithms used for the on-demand GaaS Cloud in relation to the scheduling parameters used by existing researches. We also survey the Cloud infrastructures, Grid middlewares and the issues addressed by different researchers in the past within this domain of research. Our contribution will thus be of assistance in understanding the key scheduling algorithms and parameters for potential future enhancements in this evolving area of research

The Impact of Mitigation Strategies for Socio-Cultural Distance Issues in GSD: An Empirical Study

Global Software Development (GSD) offers several benefits to software development organizations, including reduced development costs, the availability of low-wage and highly skilled employees, and an improved marketplace. Meanwhile, it faces severe communication, coordination, and control issues. The most important of these is the communication issue which is further categorized into socio-cultural, temporal, and geographical issues. Among these issues, researchers believe the socio-cultural issue is the most critical factor and, if not mitigated properly, may lead to software project failure. Although, in the past, many studies have identified socio-cultural distance-related issues, and a few studies proposed mitigation strategies. However, studies have yet to be carried out to prioritize and empirically evaluate all mitigation strategies. Thus the main objectives of this study are: a) to identify socio-cultural distance issues and mitigation strategies through a Systematic Literature Review (SLR), b) to empirically evaluate the impact of identified mitigation strategies on identified socio-cultural distance issues through a survey, and c) to prioritize effective mitigation strategies through a recommended Analytical Hierarchy Process (AHP). A total of six socio-cultural issues and twenty-eight mitigation strategies are identified from the SLR and survey. Out of which, seven mitigation strategies are most effective. This study’s findings will help software organizations to overcome socio-cultural distance issues by using the highest priority mitigation strategies to reduce losses

SAppKG: Mobile App Recommendation Using Knowledge Graph and Side Information-A Secure Framework

Due to the rapid development of technology and the widespread usage of smartphones, the number of mobile applications is exponentially growing. Finding a suitable collection of apps that aligns with users’ needs and preferences can be challenging. However, mobile app recommender systems have emerged as a helpful tool in simplifying this process. But there is a drawback to employing app recommender systems. These systems need access to user data, which is a serious security violation. While users seek accurate opinions, they do not want to compromise their privacy in the process. We address this issue by developing SAppKG, an end-to- end user privacy-preserving knowledge graph architecture for mobile app recommendation based on knowledge graph models such as SAppKG-S and SAppKG-D, that utilized the interaction data and side information of app attributes. We tested the proposed model on real-world data from the Google Play app store, using precision, recall, mean absolute precision, and mean reciprocal rank. We found that the proposed model improved results on all four metrics. We also compared the proposed model to baseline models and found that it outperformed them on all four metrics

An efficient symbiotic organisms search algorithm with chaotic optimization strategy for multi-objective task scheduling problems in cloud computing environment

In Cloud Computing model, users are charged according to the usage of resources and desired Quality of Service (QoS). Multi-objective task scheduling problem based on desired QoS is an NP-Complete problem. Due to the NP-Complete nature of task scheduling problems and huge search space presented by large scale problem instances, many of the existing solution algorithms cannot effectively obtain global optimum solutions. In this paper, a chaotic symbiotic organisms search (CMSOS) algorithm is proposed to solve multi-objective large scale task scheduling optimization problem on IaaS cloud computing environment. Chaotic optimization strategy is employed to generate initial ecosystem (population), and random sequence based components of the phases of SOS are replaced with chaotic sequence to ensure diversity among organisms for global convergence. In addition, chaotic local search strategy is applied to Pareto Fronts generated by SOS algorithms to avoid entrapment in local optima. The performance of the proposed CMSOS algorithm is evaluated on CloudSim simulator toolkit, using both standard workload traces and synthesized workloads for larger problem instances of up to 5000. Moreover, the performance of the proposed CMSOS algorithm was found to be competitive with the existing with the existing multi-objective task scheduling optimization algorithms. The CMSOS algorithm obtained significant improved optimal trade-offs between execution time (makespan) and financial cost (cost) with no computational overhead. Therefore, the proposed algorithms have potentials to improve the performance of QoS delivery

Cloud Workloads

Workload traces including the Uniform, Normal, Left-Skewed, Right-Skewed, HPC2N and NASA in both homogeneous and heterogeneous cloud environment