Emerging Security Challenges in Cloud Computing, from Infrastructure-Based Security to Proposed Provisioned Cloud Infrastructure

Movahedisefat, Mohammad Reza; Farshchi, Seyyed Mohammad Reza; Mohammadpur, Davud

doi:10.1016/b978-0-12-411474-6.00023-2

Cited by 5 publications

(8 citation statements)

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“…is twofold. On one hand, we aim to understand if there are agreed architectural design patterns as there exists in the case of cloud native applications, either technology agnostic [56,90] or technology dependent [22]. On the other hand, we also aim to see which technologies, techniques and standards are used for designing multi-cloud applications.…”

Section: Multi-cloud By Design [Rq3]mentioning

confidence: 99%

“…Close to 11% of the analysed literature state that they have implemented a multi-cloud application using a loosely coupled architecture, based on microservices and implemented through containers [31,32,55,56,63,66,84,85,90,93,94]. To this end, loosely coupled architectures are applied to different domains, such as IoT [55], fog computing [32], or osmotic computing [66].…”

Section: Multi-cloud By Design [Rq3]mentioning

confidence: 99%

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Understanding the challenges and novel architectural models of multi-cloud native applications – a systematic literature review

et al. 2023

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The evolution of Cloud Computing into a service utility, along with the pervasive adoption of the IoT paradigm, has promoted a significant growth in the need of computational and storage services. The traditional use of cloud services, focused on the consumption of one provider, is not valid anymore due to different shortcomings being the risk of vendor lock-in a critical. We are assisting to a change of paradigm, from the usage of a single cloud provider to the combination of multiple cloud service types, affecting the way in which applications are designed, developed, deployed and operated over such heterogeneous ecosystems. The result is an effective heterogeneity of architectures, methods, tools, and frameworks, copying with the multi-cloud application concept. The goal of this study is manifold. Firstly, it aims to characterize the multi-cloud concept from the application development perspective by reviewing existing definitions of multi-cloud native applications in the literature. Secondly, we set up the basis for the architectural characterization of these kind of applications. Finally, we highlight several open research issues drawn up from the analysis carried out. To achieve that, we have conducted a systematic literature review (SLR), where, a large set of primary studies published between 2011 and 2021 have been studied and classified. The in-depth analysis has revealed five main research trends for the improvement of the development and operation DevOps lifecycle of “multi-cloud native applications”. The paper finishes with directions for future work and research challenges to be addressed by the software community.

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Section: Multi-cloud By Design [Rq3]mentioning

confidence: 99%

Section: Multi-cloud By Design [Rq3]mentioning

confidence: 99%

Understanding the challenges and novel architectural models of multi-cloud native applications – a systematic literature review

et al. 2023

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“…A special form of a product solution is the implementation of business analytics in the Cloud environment [48]. Cloud Computing in business analytics provides not only a fast and secure network service, but also software and hardware that is customizable according to a company's need, including server, network, storage, various software applications and services, etc.…”

Section: Cloud Computingmentioning

confidence: 99%

Factors Influencing Business Analytics Solutions and Views on Business Problems

Potančok

Pour

2021

Data

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The main aim of this paper is to identify and specify factors that influence business analytics. A factor in this context refers to any significant characteristic that defines the environment in which business analytics and business in general are conducted. Factors and their understanding are essential for the quality of final business analytics solutions, given their complexity and interconnectedness. Factors play an extremely important role in analytic thinking and business analysts’ skills and knowledge. These factors determine effective approaches and procedures for business analytics, and, in some cases, they also aid in the decision to delay a business analytics solution given a situation. This paper has used the case study method, a qualitative research method, due to the need to carry out investigation within the actual business (company) environment, in order to be able to fully understand and verify factors affecting analytics from the viewpoint of all stakeholders. This study provides a set of 15 factors from business, company, and market environments, including their importance in business analytics.

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“…Several public and hybrid clouds deploy MapReduce paradigm. Amazon Elastic MaprReduce, Microsoft Azure [142,143], IBM's Clue cloud and Google App Engine [144,145] are some of the public clouds that enable users to perform MapReduce-based computations [146]. It is cost effective as users don't have to consider physical infrastructure or software installations.…”

Section: Mapreduce Paradigmmentioning

confidence: 99%

“…It works on top of HDFS and also handles real-time interactive processing and batch-processing of data. It is designed in a way that it is compatible with MapReduce API [145].…”

Section: Yet Another Resource Negotiator (Yarn)mentioning

confidence: 99%

Resource and Performance Modelling of Hadoop Clusters Using Machine Learning

Tariq¹

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<p>There is a huge and rapidly increasing amount of data being generated by social media, mobile applications and sensing devices. Big data is the term usually used to describe such data and is described in terms of the 3Vs - volume, variety and velocity. In order to process and mine such a massive amount of data, several approaches and platforms have been developed such as Hadoop. Hadoop is a popular open source distributed and parallel computing framework. It has a large number of configurable parameters which can be set before the execution of jobs to optimize the resource utilization and execution time of the clusters. These parameters have a significant impact on system resources and execution time. Optimizing the performance of a Hadoop cluster by tuning such a large number of parameters is a tedious task. Most current big data modeling approaches do not include the complex interaction between configuration parameters and the cluster environment changes such as use of different datasets or types of query. This makes it difficult to predict for example the execution time of a job or resource utilization of a cluster. Other attributes include configuration parameters, the structure of query, the dataset, number of nodes and the infrastructure used. Our first main objective was to design reliable experiments to understand the relationship between attributes. Before designing and implementing the actual experiment we applied Hazard and Operability (HAZOP) analysis to identify operational hazards. These hazards can affect normal working of cluster and execution of Hadoop jobs. This brainstorming activity improved the design and implementation of our experiments by improving the internal validity of the experiments. It also helped us to identify the considerations that must be taken into account for reliable results. After implementing our design, we characterized the relationship between different Hadoop configuration parameters, network and system performance measures. Our second main objective was to investigate the use of machine learning to model and predict the resource utilization and execution time of Hadoop jobs. Resource utilization and execution time of Hadoop jobs are affected by different attributes such as configuration parameters and structure of query. In order to estimate or predict either qualitatively or quantitatively the level of resource utilization and execution time, it is important to understand the impact of different combinations of these Hadoop job attributes. You could conduct experiments with many different combinations of parameters to uncover this but it is very difficult to run such a large number of jobs with different combinations of Hadoop job attributes and then interpret the data manually. It is very difficult to extract patterns from the data and give a model that can generalize for an unseen scenario. In order to automate the process of data extraction and modeling the complex behavior of different attributes of Hadoop job machine learning was used. Our decision tree based approach enabled us to systematically discover significant patterns in data. Our results showed that the decision tree models constructed for different resources and execution time were informative and robust. They were able to generalize over a wide range of minor and major environmental changes such as change in dataset, cluster size and infrastructure such as Amazon EC2. Moreover, the use of different correlation and regression techniques, such as M5P, Pearson's correlation and k-means clustering, confirmed our findings and provided further insight into the relationship of different attributes and with each other. M5P is a classification and regression technique that predicted the functional relationships among different job attributes. The use of k-means clustering allowed us to see the experimental runs that shows similar resource utilization and execution time. Statistical significance tests, were used to validate the significance of changes in results of different experimental runs, also showed the effectiveness of our resource and performance modelling and prediction method.</p>

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Emerging Security Challenges in Cloud Computing, from Infrastructure-Based Security to Proposed Provisioned Cloud Infrastructure

Cited by 5 publications

References 4 publications

Understanding the challenges and novel architectural models of multi-cloud native applications – a systematic literature review

Understanding the challenges and novel architectural models of multi-cloud native applications – a systematic literature review

Factors Influencing Business Analytics Solutions and Views on Business Problems

Resource and Performance Modelling of Hadoop Clusters Using Machine Learning

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