Model-Driven Machine Learning for Predictive Cloud Auto-scaling

Cloud provisioning of resources requires continuous monitoring and analysis of the workload on virtual computing resources. However, cloud providers offer the rule-based and schedule-based auto-scaling service. Auto-scaling is a cloud system that reacts to real-time metrics and adjusts service instances based on predefined scaling policies. The challenge of this reactive approach to auto-scaling is to cope with fluctuating load changes. For data management applications, the workload is changing and needs forecasting on historical trends and integrating with auto-scaling service. We aim to discover changes and patterns on multi metrics of resource usages of CPU, memory, and networking. To address this problem, the learning-and-inference based prediction has been adopted to predict the needs prior to provision action. First, we develop a novel machine learning-based auto-scaling process that covers the technique of learning multiple metrics for cloud auto-scaling decision. This technique is used for continuous model training and workload forecasting. Furthermore, the result of workload forecasting triggers the auto-scaling process automatically. Also, we build the serverless functions of this machine learning-based process, including monitoring, machine learning, model selection, scheduling as microservices and orchestrating these independent services by platform, language orthogonal APIs. We demonstrate this architectural implementation on AWS and Microsoft Azure and show the prediction results from machine learning on-the-fly. Results show significant cost reductions by our proposed solution compared to a general threshold-based auto-scaling. Still, there is a need to integrate the machine learning prediction with the auto-scaling system. So, the deployment effort of devising additional machine learning components is increased. So, we present a model-driven framework that defines first-class entities to represent machine learning algorithm types, inputs, outputs, parameters, and evaluation scores. We set up rules for validating machine learning entities. The connection between the machine learning and auto-scaling system is presented by two levels of abstraction models, namely cloud platform independent model and cloud platform specific model. We automate the model-to-model transformation and model-to-deployment transformation. We integrate model-driven with a DevOps approach to make models deployable and executable on a target cloud platform. We demonstrate our method with scaling configuration and deployment of two open source benchmark applications - Dell DVD store and Netflix (NDBench) on three cloud platforms, AWS, Azure, and Rackspace. The evaluation shows our inference-based auto-scaling with model-driven reduces approximately 27% of deployment effort compared to the ordinary auto-scaling

Improving Online Education Using Big Data Technologies

In a world in full digital transformation, where new information and communication technologies are constantly evolving, the current challenge of Computing Environments for Human Learning (CEHL) is to search the right way to integrate and harness the power of these technologies. In fact, these environments face many challenges, especially the increased demand for learning, the huge growth in the number of learners, the heterogeneity of available resources as well as the problems related to the complexity of intensive processing and real-time analysis of data produced by e-learning systems, which goes beyond the limits of traditional infrastructures and relational database management systems. This chapter presents a number of solutions dedicated to CEHL around the two big paradigms, namely cloud computing and Big Data. The first part of this work is dedicated to the presentation of an approach to integrate both emerging technologies of the big data ecosystem and on-demand services of the cloud in the e-learning field. It aims to enrich and enhance the quality of e-learning platforms relying on the services provided by the cloud accessible via the internet. It introduces distributed storage and parallel computing of Big Data in order to provide robust solutions to the requirements of intensive processing, predictive analysis, and massive storage of learning data. To do this, a methodology is presented and applied which describes the integration process. In addition, this chapter also addresses the deployment of a distributed e-learning architecture combining several recent tools of the Big Data and based on a strategy of data decentralization and the parallelization of the treatments on a cluster of nodes. Finally, this article aims to develop a Big Data solution for online learning platforms based on LMS Moodle. A course recommendation system has been designed and implemented relying on machine learning techniques, to help the learner select the most relevant learning resources according to their interests through the analysis of learning traces. The realization of this system is done using the learning data collected from the ESTenLigne platform and Spark Framework deployed on Hadoop infrastructure

Edu-Cloud: On-the-fly Employability Skills as a Service

21st Century global job market competition requires Science, Technology, Engineering and Mathematics (STEM) university curricula to support both state-of-the-art technical and soft skills learning to improve graduate employment. This necessitates the transformation of the current teaching and learning methodology powered by a social and col- laborative platform to provide a social co-learning environment. This social co-learning will provide students with opportunities for self-enrichment while supporting their technical skills and hands-on needs. The platform must also provide the required lab infrastructure for hands-on experimentation. This paper proposes the design and implementation of a cloud based platform called Edu-Cloud. The Edu-Cloud has been designed to provide automated resource provisioning and perform on-the-fly deployment of scalable virtual network functions to stream multimedia content closer to the global learners. This would help to meet the specific learning needs of a group of global interconnected students with similar learning skills and abilities. The benchmarking performance results show that the proposed framework works efficiently while reducing primary network traffic by deploying resources closer to the users and support scalability for a global deployment scenario

Guidelines to selection and use of electronic learning resources and tools

На сучасному етапі формування інформаційного суспільства стрімкий технологічний розвиток, що пов’язаний з появою нових освітніх інформаційно-комунікаційних технологій, зокрема на базі засобів хмарних обчислень, мобільного навчання, сервісів соціальних мереж, знаннє-орієнтованих систем, спрямований на досягнення нової якості освіти. Завдяки сучасним технологіям упорядковуються процеси накопичення і зберігання різних предметних колекцій електронних освітніх ресурсів, можливості надання доступу та функціональність яких значно зростають. Це сприяє реформуванню та розвитку інформаційного середовища навчання, поліпшенню якості засобів ІКТ, підвищенню загального рівня е-навчання. Створення умов рівного доступу до кращих зразків електронних ресурсів та засобів навчального призначення, безпеки і комфорту учнів та студентів при роботі із засобами ІКТ потребує удосконалення технологічних платформ, технічних засобів реалізації електронного навчання та методик їх застосування. У зв’язку з цим, проблеми покращення науково-методичного забезпечення процесу інформатизації освіти, зокрема в аспекті виявлення найбільш доцільних шляхів застосування електронних освітніхAt the present stage of information society development the rapid technological grouse that is associated with the emergence of new educational ICT, in particular on the basis of cloud computing, mobile learning, social networking services, knowledge-based systems aimed at achieving a new quality of education. Thanks to modern technology the processes of accumulation and storage of various subject collections of electronic learning resources are organized, the possibility of access and functionality of its use significantly increases. It promotes the transformation and development of information learning environment, improves the quality of ICT-based tools, increasing the overall level of e-learning. For the aims of equal access to the best examples of electronic learning resources and tools, safety and comfort of pupils and students when working with ICT tools there is a need of improvement of technological platforms, hardware and software e-learning tools and methods and their application. In this context, the problem of improving the scientific and methodological support for the process of informatization of education, particularly in terms of identifying the most appropriate ways of electronic educational resources wide spread adoption are relevant

What can AI do for you?

Simply put, most organizations do not know how to approach the incorporation of AI into their businesses, and few are knowledgeable enough to understand which concepts are applicable to their business models. Doing nothing and waiting is not an option: Mahidar and Davenport (2018) argue that companies that try to play catch-up will ultimately lose to those who invested and began learning early. But how do we bridge the gap between skepticism and adoption? We propose a toolkit, inclusive of people, processes, and technologies, to help companies with discovery and readiness to start their AI journey. Our toolkit will deliver specific and actionable answers to the operative question: What can AI do for you