AWS PredSpot: Machine Learning for Predicting the Price of Spot Instances in AWS Cloud

Elastic Cloud Compute (EC2) is one of the most well-known services provided by Amazon for provisioning cloud computing resources, also known as instances. Besides the classical on-demand scheme, where users purchase compute capacity at a fixed cost, EC2 supports so-called spot instances, which are offered following a bidding scheme, where users can save up to 90% of the cost of the on-demand instance. EC2 spot instances can be a useful alternative for attaining an important reduction in infrastructure cost, but designing bidding policies can be a difficult task, since bidding under their cost will either prevent users from provisioning instances or losing those that they already own. Towards this extent, accurate forecasting of spot instance prices can be of an outstanding interest for designing working bidding policies. In this paper, we propose the use of different machine learning techniques to estimate the future price of EC2 spot instances. These include linear, ridge and lasso regressions, multilayer perceptrons, K-nearest neighbors, extra trees and random forests. The obtained performance varies significantly between instances types, and root mean squared errors ranges between values very close to zero up to values over 60 in some of the most expensive instances. Still, we can see that for most of the instances, forecasting performance is remarkably good, encouraging further research in this field of study

Distributed network of meteostations with LoRa

Současná práce analyzuje možnosti nasazení distribuované sítě meteostanic v městském prostředí. Cílem této práce je návrh a implementace zařízení s důrazem na jednoduchost instalace, minimální spotřebu, bezdrátový přenos dat a využití alternativního zdroje energie. V rámci této práce byl také implementován algoritmus založený na architektuře neuronové sítě LSTM, schopný generovat předpověď měřených parametrů. Kromě toho na hostingu Amazon byla nasazena infrastruktura, která kombinuje centralizovaný sběr dat ze všech zařízení, předpovídání měřených parametrů, sdílení dat s komunitními projekty monitorování počasí a navíc bylo poskytnuto webové rozhraní pro zobrazování měřených a předpovězených dat. Vyvinutý systém byl úspěšně otestován v reálných klimatických podmínkách. Nakonec byla provedena srovnávací analýza vyvinutého zařízení a komerčních analogů ze stejné a vyšší cenové kategorie. Výsledkem této práce je systém, který má komerční potenciál a je schopen konkurovat populárním stávajícím řešením.The present work analyzes the possibilities of deploying a distributed network of meteostations in an urban environment. The aim of this work is the design and implementation of a device with an emphasis on the simplest possible installation, minimum power consumption, wireless data transmission and the use of alternative power source. Also, within the framework of this work, an algorithm based on the LSTM neural network architecture has been implemented, capable of generating a forecast of the measured parameters. In addition, an infrastructure was deployed on Amazon hosting, combining both centralized data collection from all devices, predicting measured parameters, sharing data with community weather monitoring projects, and, moreover, the web interface was implemented displaying both device data along with measured and predicted parameters. The developed system has been successfully tested in real climatic conditions. Finally, a comparative analysis of the developed device and commercial counterparts from the same and premium price segments was carried out. The result of the present work is a system with commercial potential and the ability to compete with popular existing solutions

Prediksi Indeks Harga Konsumen Komoditas Makanan Berbasis Cloud Computing Menggunakan Multilayer Perceptron

Teknik prediksi merupakan salah satu area dalam data mining dimana menemukan pola dari sekumpulan data yang mengarah pada prediksi di masa depan. Prediksi dalam bidang ekonomi merupakan prediksi yang mendominasi karena merupakan salah satu parameter berkembangnya suatu negara. Indeks Harga Konsumen menggambarkan tingkat konsumsi barang dan jasa pada masyarakat yang dapat dijadikan acuan nilai inflasi. Mayoritas penelitian yang melakukan prediksi nilai Indeks Harga Konsumen sebelumnya hanya melakukan prediksi menggunakan nilai Indeks Harga Konsumen itu sendiri sebagai nilai input dan output. Penelitian ini membangun model peramalan dengan memanfaatkan multi variabel input yaitu 28 jenis harga bahan pokok harian sebagai nilai input untuk meramal nilai Indeks Harga Konsumen di kota Surabaya periode 2014 sampai 2018 dimana keseluruhan pembangunan model prediksi dilakukan di lingkungan Amazon Cloud Services. Sistem prediksi dibangun dengan algoritma Multilayer Perceptron dengan variasi arsitektur jumlah neuron, epoch, dan hidden layer. Berdasarkan hasil pengujian, akurasi terbaik dengan nilai RMSE 3.380  dihasilkan oleh konfigurasi 2 hidden layer,  hidden layer pertama dan kedua mempunyai neuron masing-masing berjumlah 10 dengan epoch sebesar 1000

Reducing the price of resource provisioning using EC2 spot instances with prediction models

The increasing demand of computing resources has boosted the use of cloud computing providers. This has raised a new dimension in which the connections between resource usage and costs have to be considered from an organizational perspective. As a part of its EC2 service, Amazon introduced spot instances (SI) as a cheap public infrastructure, but at the price of not ensuring reliability of the service. On the Amazon SI model, hired instances can be abruptly terminated by the service provider when necessary. The interface for managing SI is based on a bidding strategy that depends on non-public Amazon pricing strategies, which makes complicated for users to apply any scheduling or resource provisioning strategy based on such (cheaper) resources. Although it is believed that the use of the EC2 SIs infrastructure can reduce costs for final users, a deep review of literature concludes that their characteristics and possibilities have not yet been deeply explored. In this work we present a framework for the analysis of the EC2 SIs infrastructure that uses the price history of such resources in order to classify the SI availability zones and then generate price prediction models adapted to each class. The proposed models are validated through a formal experimentation process. As a result, these models are applied to generate resource provisioning plans that get the optimal price when using the SI infrastructure in a real scenario. Finally, the recent changes that Amazon has introduced in the SI model and how this work can adapt to these changes is discussed

Scheduling Flexible Demand in Cloud Computing Spot Markets

The rapid standardization and specialization of cloud computing services have led to the development of cloud spot markets on which cloud service providers and customers can trade in near real-time. Frequent changes in demand and supply give rise to spot prices that vary throughout the day. Cloud customers often have temporal flexibility to execute their jobs before a specific deadline. In this paper, the authors apply real options analysis (ROA), which is an established valuation method designed to capture the flexibility of action under uncertainty. They adapt and compare multiple discrete-time approaches that enable cloud customers to quantify and exploit the monetary value of their short-term temporal flexibility. The paper contributes to the field by guaranteeing cloud job execution of variable-time requests in a single cloud spot market, whereas existing multi-market strategies may not fulfill requests when outbid. In a broad simulation of scenarios for the use of Amazon EC2 spot instances, the developed approaches exploit the existing savings potential up to 40 percent – a considerable extent. Moreover, the results demonstrate that ROA, which explicitly considers time-of-day-specific spot price patterns, outperforms traditional option pricing models and expectation optimization

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