News publishers have decreased disseminating news through conventional newspapers and have migrated to the use of digital means like websites and purpose-built mobile applications. It is observed that news recommendation systems can automatically process lengthy articles and identify similar articles for readers considering predefined criteria. The objectives of the current work are to identify and classify the challenges in news recommendation domain, to identify state-of-the-art approaches and classify on the application domain, to identify datasets used for evaluation and their sources, the evaluation approaches used and to highlight the challenges explicitly addressed. The literature is thoroughly studied over the time span of 2001-2019 and shortlisted 81 related studies, broadly classified into six categories and discussed. The analysis showed that 60% of news recommendation system adopted a hybrid approach, 66% studies little talk about datasets, and addresses a few challenges from a long list of challenges in the news domain. This article is the first in the field to draw a comprehensive big picture of news recommendation and explore different dimensions covered in the studies. The last section presents the future research opportunities that lead to improving the recommendation of news articles in the news domain.

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News Recommendation Systems - Accomplishments, Challenges & Future Directions

Survey of load balancing techniques for Grid

As the demand for computing resources grows, cloud computing becomes more and more popular as a pay-as-you-go model, in which the computing resources and services are provided to cloud users efficiently. For cloud providers, the typical goal is to maximize their profits. However, maximizing profits in a highly competitive cloud market is a huge challenge for cloud providers. In this article, a survey of profit optimization techniques is proposed to increase cloud provider profitability through service quality improvement, service pricing, energy consumption reduction, and virtual network function (VNF) deployment. The strategy of improving user service quality is discussed first, followed by the pricing strategy for cloud resources to maximize revenue. Then, this article summarizes the techniques for cloud data centers to reduce server power consumption. Finally, various heuristic algorithms for VNF deployment in the cloud are further described to reduce the cost of cloud providers while maintaining performance. We classify research works based on components of profit and methods used to demonstrate similarities and differences in these studies. We hope this survey will provide researchers with insights into cloud profit optimization techniques.

A Survey of Profit Optimization Techniques for Cloud Providers

In “Real-Time Dynamic Pricing for Revenue Management with Reusable Resources, Advance Reservation, and Deterministic Service Time Requirements,” Lei and Jasin consider a fundamental dynamic pricing...

Real-Time Dynamic Pricing for Revenue Management with Reusable Resources, Advance Reservation, and Deterministic Service Time Requirements

The DBSCAN algorithm is a prevalent method of density-based clustering algorithms, the most important feature of which is the ability to detect arbitrary shapes and varied clusters and noise data. Nevertheless, this algorithm faces a number of challenges, including failure to find clusters of varied densities. On the other hand, with the rapid development of the information age, plenty of data are produced every day, such that a single machine alone cannot process this volume of data; hence, new technologies are required to store and extract information from this volume of data. A large volume of data that is beyond the capabilities of existing software is called Big data. In this paper, we have attempted to introduce a new algorithm for clustering big data with varied density using a Hadoop platform running MapReduce. The main idea of this research is the use of local density to find each point’s density. This strategy can avoid the situation of connecting clusters with varying densities. The proposed algorithm is implemented and compared with other algorithms using the MapReduce paradigm and shows the best varying density clustering capability and scalability.

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Big data clustering with varied density based on MapReduce

The modern day advancement is increasingly digitizing our lives which has led to a rapid growth of data. Such multidimensional datasets are precious due to the potential of unearthing new knowledge and developing decision-making insights from them. Analyzing this huge amount of data from multiple sources can help organizations to plan for the future and anticipate changing market trends and customer requirements. While the Hadoop framework is a popular platform for processing larger datasets, there are a number of other computing infrastructures, available to use in various application domains. The primary focus of the study is how to classify major big data resource management systems in the context of cloud computing environment. We identify some key features which characterize big data frameworks as well as their associated challenges and issues. We use various evaluation metrics from different aspects to identify usage scenarios of these platforms. The study came up with some interesting findings which are in contradiction with the available literature on the Internet.

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Big Data in Cloud Computing: A Resource Management Perspective

Cloud computing has grasped the attention of scientific community and business industry towards the provisioning of computing resources as utility and software as a service over a network. Profitability and revenue maximization are the most important goals for any cloud service provider which can be employed through different pricing models. Historically, cloud providers were following the fixed pricing strategies while provisioning their services to the clients. However, these approaches have their own shortcomings resulting in resource wastage, lack of fairness and user satisfaction. With expansion of cloud users in market every day, provisioning of fair resource allocation with service differentiation and efficient pricing model is demand seeking, resulting in a shift from static to dynamic pricing models. In this paper, we discuss and investigate different pricing scheme with respect to their advantages, limitations and possible future directions. The study will open a way for vendors to seek new research directions in dynamic pricing schemes.

A survey of Cloud computing variable pricing models

Preserving news stories may be important because of various reasons like they provide detailed information about events and they may be used for research purposes in the long term. However, the news stories published online are in danger because of reasons like constant change in the technologies used to publish information and the formats for publication. Certain institutions or individuals may be interested in preserving news stories related to a particular event or topic. The stories should be collected from various online newspapers and preserved for the long term. The major issue in the preservation process is that newspapers use different formats for online publication of the stories. The paper presents a tool which is developed to addresses the issue. The tool facilitates users in the extraction of news stories from various online newspapers and migration to a normalized format.

Normalizing digital news-stories for preservation

Work load and resource management are two important factors that have to manage across the grid environment. To increase the overall efficiency of grid based infrastructure the work load across the grid environment has to manage. Hence the work load must be evenly scheduled across the grid nodes so that grid resources can be properly exploited. The technique that we have investigated in this paper is based upon the combination of genetic algorithms which is an evolutionary algorithm and artificial neural networks. Both of these techniques are applied for local grid load balancing. Genetic algorithm selects the optimal set of jobs for assigning to the grid nodes which overall minimizes the total execution time. Afterwards when optimal set of jobs is selected they are assigned to artificial neural network which selects the minimum loaded grid processor for further processing of this optimal set of jobs. We compare our proposed technique with the already existing strategies for load balancing like random algorithm, round robin algorithm, decreasing time algorithm and least connection algorithm. Results shows that our strategy gives optimal results in terms of overall time efficiency. So we can overall conclude that GA’s and ANN’s increase overall efficiency of job scheduling especially in case where the tasks coming for scheduling and processing nodes are continuously increasing.

Load Balancing in Grid Computing Using AI Techniques

One of the recently emerging areas in cloud computing is deployment of virtual machines across multiple clouds based on providers’ ranking. This involves benchmarking of different cloud providers, development of different techniques for selection of candidate providers and frameworks for ranking cloud providers. Existing benchmarking studies are mostly focused on selection of best-fit cloud provider among a set of cloud providers for a particular set of quality attributes based on industry best standard tools and techniques. However, most of the researches are focused on performance of IaaS cloud providers and price-performance analysis is normally ignored while benchmarking IaaS metrics. In this work, we propose a novel QoS based ranking methodology along with priceperformance analysis that can be used as an input for selecting candidate cloud providers. Our proposed mechanism allows cloud consumers to find the most cost effective virtual machines for a given set of user preferences. As a case study, we present performance evaluation and benchmarking results of three major cloud providers: Google, Amazon and Rackspace. c ©2016 All rights reserved.

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M. Daud Awan

Papers

Big Data in Cloud Computing: A Resource Management Perspective

A survey of Cloud computing variable pricing models

Normalizing digital news-stories for preservation

Load Balancing in Grid Computing Using AI Techniques

A price-performance analysis of EC2, Google Compute and Rackspace cloud providers for scientiﬁc computing