Performance Models for Frost Prediction in Public Cloud Infrastructures

Sensor Clouds have opened new opportunities for agricultural monitoring. These infrastructures use Wireless Sensor Networks (WSNs) to collect data on-field and Cloud Computing services to store and process them. Among other applications of Sensor Clouds, frost prevention is of special interest among grapevine producers in the Province of Mendoza - Argentina, since frost is one of the main causes of economic loss in the province. Currently, there is a wide offer of public cloud services that can be used in order to process data collected by Sensor Clouds. Therefore, there is a need for tools to determine which instance is the most appropriate in terms of execution time and economic costs for running frost prediction applications in an isolated or cluster way. In this paper, we develop models to estimate the performance of different Amazon EC2 instances for processing frosts prediction applications. Finally, we obtain results that show which is the best instance for processing these applications

An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds

Many important computational applications in science, engineering, industry, and technology are represented by PSE (parameter sweep experiment) applications. Tese applications involve a large number of resource-intensive and independent computational tasks. Because of this, cloud autoscaling approaches have been proposed to execute PSE applications on public cloud environments that ofer instances of diferent VM (virtual machine) types, under a pay-per-use scheme, to execute diverse applications. One of the most recent approaches is the autoscaler MOEA (multiobjective evolutive algorithm), which is based on the multiobjective evolutionary algorithm NSGA-II (nondominated sorting genetic algorithm II). MOEA considers on-demand and spot VM instances and three optimization objectives relevant for users: minimizing the computing time, monetary cost, and spot instance interruptions of the application’s execution. However, MOEA’s performance regarding these optimization objectives depends signifcantly on the optimization algorithm used. It has been shown recently that MOEA’s performance improves considerably when NSGA-II is replaced by a more recent algorithm named NSGA-III. In this paper, we analyze the incorporation of other multiobjective optimization algorithms into MOEA to enhance the performance of this autoscaler. First, we consider three multiobjective optimization algorithms named E-NSGA-III (extreme NSGA-III), SMS-EMOA (S-metric selection evolutionary multiobjective optimization algorithm), and SMPSO (speed-constrained multiobjective particle swarm optimization), which have behavioral diferences with NSGA-III. Ten, we evaluate the performance of MOEA with each of these algorithms, considering the three optimization objectives, on four real-world PSE applications from the meteorology and molecular dynamics areas, considering diferent application sizes. To do that, we use the well-known CloudSim simulator and consider diferent VM types available in Amazon EC2. Finally, we analyze the obtained performance results, which show that MOEA with E-NSGA-III arises as the best alternative, reaching better and signifcant savings in terms of computing time (10%–17%), monetary cost (10%– 40%), and spot instance interruptions (33%–100%).Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Monge Bosdari, David Antonio. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Rodríguez, Guillermo Horacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Millán, Emmanuel Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Santos, Jorge Ruben. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentin

WSNs Data and Configuration Management in Sensor Clouds with Cloud File Synchronization Services

Sensor Clouds have opened new possibilities for researchers of disciplines such as environmental monitoring, precision agriculture and flood prevention. This technology uses Wireless Sensor Networks (WSNs) to collect real world data and Cloud Computing to store and process them. The remote management of WSNs setup and data in Sensor Clouds implies: real time access to collected data, sensor setup reconfiguration and sensor battery status monitoring. Currently there are different platforms for WSNs data and setup management in Sensor Clouds. Generally, these platforms require that scientists of the mentioned disciplines must have knowledge of WSNs and web services programming in order to reconfigure the sensors setup. Hence, these sensing resources can not be provided in a transparent way to end-users. In this paper, we propose the use of standard Cloud File Synchronization Services (CFSS) for carrying out the full management of WSNs in Sensor Clouds. In order to validate our proposal, we conduct experiments using a Sensor Cloud platform based on CFSS called Sensor Cirrus.Facultad de Informátic

WSNs Data and Configuration Management in Sensor Clouds with Cloud File Synchronization Services

Sensor Clouds have opened new possibilities for researchers of disciplines such as environmental monitoring, precision agriculture and flood prevention. This technology uses Wireless Sensor Networks (WSNs) to collect real world data and Cloud Computing to store and process them. The remote management of WSNs setup and data in Sensor Clouds implies: real time access to collected data, sensor setup reconfiguration and sensor battery status monitoring. Currently there are different platforms for WSNs data and setup management in Sensor Clouds. Generally, these platforms require that scientists of the mentioned disciplines must have knowledge of WSNs and web services programming in order to reconfigure the sensors setup. Hence, these sensing resources can not be provided in a transparent way to end-users. In this paper, we propose the use of standard Cloud File Synchronization Services (CFSS) for carrying out the full management of WSNs in Sensor Clouds. In order to validate our proposal, we conduct experiments using a Sensor Cloud platform based on CFSS called Sensor Cirrus.Facultad de Informátic

Wiki-health: from quantified self to self-understanding

Today, healthcare providers are experiencing explosive growth in data, and medical imaging represents a significant portion of that data. Meanwhile, the pervasive use of mobile phones and the rising adoption of sensing devices, enabling people to collect data independently at any time or place is leading to a torrent of sensor data. The scale and richness of the sensor data currently being collected and analysed is rapidly growing. The key challenges that we will be facing are how to effectively manage and make use of this abundance of easily-generated and diverse health data. This thesis investigates the challenges posed by the explosive growth of available healthcare data and proposes a number of potential solutions to the problem. As a result, a big data service platform, named Wiki-Health, is presented to provide a unified solution for collecting, storing, tagging, retrieving, searching and analysing personal health sensor data. Additionally, it allows users to reuse and remix data, along with analysis results and analysis models, to make health-related knowledge discovery more available to individual users on a massive scale. To tackle the challenge of efficiently managing the high volume and diversity of big data, Wiki-Health introduces a hybrid data storage approach capable of storing structured, semi-structured and unstructured sensor data and sensor metadata separately. A multi-tier cloud storage system—CACSS has been developed and serves as a component for the Wiki-Health platform, allowing it to manage the storage of unstructured data and semi-structured data, such as medical imaging files. CACSS has enabled comprehensive features such as global data de-duplication, performance-awareness and data caching services. The design of such a hybrid approach allows Wiki-Health to potentially handle heterogeneous formats of sensor data. To evaluate the proposed approach, we have developed an ECG-based health monitoring service and a virtual sensing service on top of the Wiki-Health platform. The two services demonstrate the feasibility and potential of using the Wiki-Health framework to enable better utilisation and comprehension of the vast amounts of sensor data available from different sources, and both show significant potential for real-world applications.Open Acces

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

Low Latency Geo-distributed Data Analytics

Low latency analytics on geographically distributed dat-asets (across datacenters, edge clusters) is an upcoming and increasingly important challenge. The dominant approach of aggregating all the data to a single data-center significantly inflates the timeliness of analytics. At the same time, running queries over geo-distributed inputs using the current intra-DC analytics frameworks also leads to high query response times because these frameworks cannot cope with the relatively low and variable capacity of WAN links. We present Iridium, a system for low latency geo-distri-buted analytics. Iridium achieves low query response times by optimizing placement of both data and tasks of the queries. The joint data and task placement op-timization, however, is intractable. Therefore, Iridium uses an online heuristic to redistribute datasets among the sites prior to queries ’ arrivals, and places the tasks to reduce network bottlenecks during the query’s ex-ecution. Finally, it also contains a knob to budget WAN usage. Evaluation across eight worldwide EC2 re-gions using production queries show that Iridium speeds up queries by 3 × − 19 × and lowers WAN usage by 15% − 64 % compared to existing baselines

Benchmarking Eventually Consistent Distributed Storage Systems

Cloud storage services and NoSQL systems typically offer only "Eventual Consistency", a rather weak guarantee covering a broad range of potential data consistency behavior. The degree of actual (in-)consistency, however, is unknown. This work presents novel solutions for determining the degree of (in-)consistency via simulation and benchmarking, as well as the necessary means to resolve inconsistencies leveraging this information

MACHS: Mitigating the Achilles Heel of the Cloud through High Availability and Performance-aware Solutions

Cloud computing is continuously growing as a business model for hosting information and communication technology applications. However, many concerns arise regarding the quality of service (QoS) offered by the cloud. One major challenge is the high availability (HA) of cloud-based applications. The key to achieving availability requirements is to develop an approach that is immune to cloud failures while minimizing the service level agreement (SLA) violations. To this end, this thesis addresses the HA of cloud-based applications from different perspectives. First, the thesis proposes a component’s HA-ware scheduler (CHASE) to manage the deployments of carrier-grade cloud applications while maximizing their HA and satisfying the QoS requirements. Second, a Stochastic Petri Net (SPN) model is proposed to capture the stochastic characteristics of cloud services and quantify the expected availability offered by an application deployment. The SPN model is then associated with an extensible policy-driven cloud scoring system that integrates other cloud challenges (i.e. green and cost concerns) with HA objectives. The proposed HA-aware solutions are extended to include a live virtual machine migration model that provides a trade-off between the migration time and the downtime while maintaining HA objective. Furthermore, the thesis proposes a generic input template for cloud simulators, GITS, to facilitate the creation of cloud scenarios while ensuring reusability, simplicity, and portability. Finally, an availability-aware CloudSim extension, ACE, is proposed. ACE extends CloudSim simulator with failure injection, computational paths, repair, failover, load balancing, and other availability-based modules