PhD Oral Exam - Hanieh Alipour, Electrical and Computer Engineering
When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.
Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.
Cloud provisioning of resources requires continuous monitoring and analysis of the workload on virtual computing resources. However, Cloud providers offer the rule- based and the 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. Such a reactive approach still lags behind 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 cov- ers the technique of learning multiple metrics for Cloud auto-scaling decision. This technique is used for continuous model training and workload forecasting. Further- more, the result of workload forecasting triggers the auto-scaling process automati- cally. 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 with microservices on AWS and Microsoft Azure and show the prediction results from machine learning on-the-fly. Re- sults show significant cost reductions by our proposed solution compared to a general threshold-based auto-scaling solution.
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, param- eters, and evaluation scores. We setup rules for validating machine learning entities.
The connection between the machine learning and auto-scaling system is presented by two level 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. Then, we integrate model-driven with 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.