A study on performance measures for auto-scaling CPU-intensive containerized applications

Autoscaling of containers can leverage performance measures from the different layers of the computational stack. This paper investigate the problem of selecting the most appropriate performance measure to activate auto-scaling actions aiming at guaranteeing QoS constraints. First, the correlation between absolute and relative usage measures and how a resource allocation decision can be influenced by them is analyzed in different workload scenarios. Absolute and relative measures could assume quite different values. The former account for the actual utilization of resources in the host system, while the latter account for the share that each container has of the resources used. Then, the performance of a variant of Kubernetes’ auto-scaling algorithm, that transparently uses the absolute usage measures to scale-in/out containers, is evaluated through a wide set of experiments. Finally, a detailed analysis of the state-of-the-art is presented

Resource management in a containerized cloud : status and challenges

Cloud computing heavily relies on virtualization, as with cloud computing virtual resources are typically leased to the consumer, for example as virtual machines. Efficient management of these virtual resources is of great importance, as it has a direct impact on both the scalability and the operational costs of the cloud environment. Recently, containers are gaining popularity as virtualization technology, due to the minimal overhead compared to traditional virtual machines and the offered portability. Traditional resource management strategies however are typically designed for the allocation and migration of virtual machines, so the question arises how these strategies can be adapted for the management of a containerized cloud. Apart from this, the cloud is also no longer limited to the centrally hosted data center infrastructure. New deployment models have gained maturity, such as fog and mobile edge computing, bringing the cloud closer to the end user. These models could also benefit from container technology, as the newly introduced devices often have limited hardware resources. In this survey, we provide an overview of the current state of the art regarding resource management within the broad sense of cloud computing, complementary to existing surveys in literature. We investigate how research is adapting to the recent evolutions within the cloud, being the adoption of container technology and the introduction of the fog computing conceptual model. Furthermore, we identify several challenges and possible opportunities for future research

Explorar kubernetes e devOps num contexto de IoT

Containerized solutions and container orchestration technologies have recently been of great interest to organizations as a way of accelerating both software development and delivery processes. However, adopting these is a rather complex shift that may impact an organization and teams that were already established. This is where development cultures such as DevOps emerge to ease such shift amongst teams, promoting collaboration and automation of development and deployment processes throughout. The purpose of the current dissertation is to illustrate the path that led to the use of DevOps and containerization as means to support the development and deployment of a proof of concept system, Firefighter Sync – an Internet of Things based solution applied to a firefighting monitoring scenario. The goal, besides implementing Firefighter Sync, was to propose and deploy a development and operations ecosystem based on DevOps practices to achieve a full automation pipeline for both the development and operations processes. Firefighter Sync enabled the exploration of such state-of-the-art solutions such as Kubernetes to support container-based deployment and Jenkins for a fully automated CI/CD pipeline. Firefighter Sync clearly illustrates that addressing the development of a system from a DevOps perspective from the very beginning, although it requires an accentuated learning curve due to the large range of concepts and technologies addressed throughout, has illustrated to effectively impact the development process as well as ease the solution for future evolution. A good example is the automation process pipeline, that whilst allowing an easy integration of new features within a DevOps process – implies addressing the development and operations as a whole – it abstracts specific technological concerns turning these transversals to the traditional stages from development to deployment.Soluções de contentores e orquestração de contentores têm vindo a tornar-se de grande interesse para as organizações como uma forma de acelerar os processos de desenvolvimento e entrega de software. No entanto, adotá-las é uma mudança bastante complexa que pode impactar uma organização e equipas já estabelecidas. É aqui que surgem culturas como o DevOps para facilitar essa mudança, promovendo a colaboração e a automação dos processos de desenvolvimento e deployment entre equipas. O objetivo desta dissertação é ilustrar o caminho que levou ao uso de DevOps e à conteinerização de modo a apoiar o desenvolvimento e o deployment de um sistema como prova de conceito, o Firefighter Sync – uma solução baseada na Internet das Coisas aplicada a um cenário de monitorização de combate a incêndios. Além de implementar o Firefighter Sync, o objetivo era também propor e implementar um ecossistema de desenvolvimento e operações com base nas práticas de DevOps para alcançar uma pipeline de automação completa para os processos de desenvolvimento e operações. O Firefighter Sync permitiu explorar soluções que constituem o estado da arte neste contexto, como o Kubernetes para apoiar o deployment baseado em contentores e o Jenkins para suportar a pipeline de CI/CD totalmente automatizada. O Firefighter Sync ilustra claramente que abordar o desenvolvimento de um sistema a partir da perspectiva de DevOps, embora exija uma curva de aprendizagem acentuada devido à grande variedade de conceitos e tecnologias inerentes ao longo do processo, demonstrou tornar mais eficiente o processo de desenvolvimento, bem como facilitar evolução futura. Um exemplo é a pipeline de automação, que permite uma fácil integração de novos recursos dentro de um processo de DevOps – que implica abordar o desenvolvimento e as operações como um todo – abstraindo assim preocupações tecnológicas específicas, transformando essas transversais nas fases tradicionais do desenvolvimento ao deployment.Mestrado em Engenharia Informátic

Managing Event-Driven Applications in Heterogeneous Fog Infrastructures

The steady increase in digitalization propelled by the Internet of Things (IoT) has led to a deluge of generated data at unprecedented pace. Thereby, the promise to realize data-driven decision-making is a major innovation driver in a myriad of industries. Based on the widely used event processing paradigm, event-driven applications allow to analyze data in the form of event streams in order to extract relevant information in a timely manner. Most recently, graphical flow-based approaches in no-code event processing systems have been introduced to significantly lower technological entry barriers. This empowers non-technical citizen technologists to create event-driven applications comprised of multiple interconnected event-driven processing services. Still, today’s event-driven applications are focused on centralized cloud deployments that come with inevitable drawbacks, especially in the context of IoT scenarios that require fast results, are limited by the available bandwidth, or are bound by the regulations in terms of privacy and security. Despite recent advances in the area of fog computing which mitigate these shortcomings by extending the cloud and moving certain processing closer to the event source, these approaches are hardly established in existing systems. Inherent fog computing characteristics, especially the heterogeneity of resources alongside novel application management demands, particularly the aspects of geo-distribution and dynamic adaptation, pose challenges that are currently insufficiently addressed and hinder the transition to a next generation of no-code event processing systems. The contributions of this thesis enable citizen technologists to manage event-driven applications in heterogeneous fog infrastructures along the application life cycle. Therefore, an approach for a holistic application management is proposed which abstracts citizen technologists from underlying technicalities. This allows to evolve present event processing systems and advances the democratization of event-driven application management in fog computing. Individual contributions of this thesis are summarized as follows: 1. A model, manifested in a geo-distributed system architecture, to semantically describe characteristics specific to node resources, event-driven applications and their management to blend application-centric and infrastructure-centric realms. 2. Concepts for geo-distributed deployment and operation of event-driven applications alongside strategies for flexible event stream management. 3. A methodology to support the evolution of event-driven applications including methods to dynamically reconfigure, migrate and offload individual event-driven processing services at run-time. The contributions are introduced, applied and evaluated along two scenarios from the manufacturing and logistics domain

Big Data and Large-scale Data Analytics: Efficiency of Sustainable Scalability and Security of Centralized Clouds and Edge Deployment Architectures

One of the significant shifts of the next-generation computing technologies will certainly be in the development of Big Data (BD) deployment architectures. Apache Hadoop, the BD landmark, evolved as a widely deployed BD operating system. Its new features include federation structure and many associated frameworks, which provide Hadoop 3.x with the maturity to serve different markets. This dissertation addresses two leading issues involved in exploiting BD and large-scale data analytics realm using the Hadoop platform. Namely, (i)Scalability that directly affects the system performance and overall throughput using portable Docker containers. (ii) Security that spread the adoption of data protection practices among practitioners using access controls. An Enhanced Mapreduce Environment (EME), OPportunistic and Elastic Resource Allocation (OPERA) scheduler, BD Federation Access Broker (BDFAB), and a Secure Intelligent Transportation System (SITS) of multi-tiers architecture for data streaming to the cloud computing are the main contribution of this thesis study

Applying Modern Software System Design to Small Satellite Development and Operations

Small satellite development and operations can be dramatically improved using contemporary software practices and such technologies as system monitoring, containerization, and data analytics. By logging system metrics at all stages of satellite development and operation, as is common practice in current software development operations practices, satellite operators can implement new software more quickly, detect failures sooner, and analyze information more effectively. Michigan eXploration Laboratory (MXL) at the University of Michigan has created a system that aggregates millions of data points from a wide range of sources including our satellite development units, our servers, our ground stations, open source ground station networks, and our flight spacecraft. The system uses multiple data stores, distributed across a number of servers, to house hundreds of gigabytes of telemetry data. Our system then provides novel ways to access that data, including online APIs and GUIs. These tools have afforded MXL an unprecedented ability to rapidly assess and maintain the health of our spacecraft both in development and in orbit

Innovative techniques for deployment of microservices in cloud-edge environment

PhD ThesisThe evolution of microservice architecture allows complex applications to be structured into independent modular components (microservices) making them easier to develop and manage. Complemented with containers, microservices can be deployed across any cloud and edge environment. Although containerized microservices are getting popular in industry, less research is available specially in the area of performance characterization and optimized deployment of microservices. Depending on the application type (e.g. web, streaming) and the provided functionalities (e.g. ltering, encryption/decryption, storage), microservices are heterogeneous with speci c functional and Quality of Service (QoS) requirements. Further, cloud and edge environments are also complex with a huge number of cloud providers and edge devices along with their host con gurations. Due to these complexities, nding a suitable deployment solution for microservices becomes challenging. To handle the deployment of microservices in cloud and edge environments, this thesis presents multilateral research towards microservice performance characterization, run-time evaluation and system orchestration. Considering a variety of applications, numerous algorithms and policies have been proposed, implemented and prototyped. The main contributions of this thesis are given below: Characterizes the performance of containerized microservices considering various types of interference in the cloud environment. Proposes and models an orchestrator, SDBO for benchmarking simple webapplication microservices in a multi-cloud environment. SDBO is validated using an e-commerce test web-application. Proposes and models an advanced orchestrator, GeoBench for the deployment of complex web-application microservices in a multi-cloud environment. GeoBench is validated using a geo-distributed test web-application. - i - Proposes and models a run-time deployment framework for distributed streaming application microservices in a hybrid cloud-edge environment. The model is validated using a real-world healthcare analytics use case for human activity recognition.

Design and implementation of serverless architecture for i2b2 on AWS cloud and Snowflake data warehouse

Informatics for Integrating Biology and the Beside (i2b2) is an open-source medical tool for cohort discovery that allows researchers to explore and query clinical data. The i2b2 platform is designed to adopt any patient-centric data models and used at over 400 healthcare institutions worldwide for querying patient data. The platform consists of a webclient, core servers and database. Despite having installation guidelines, the complex architecture of the system with numerous dependencies and configuration parameters makes it difficult to install a functional i2b2 platform. On the other hand, maintaining the scalability, security, availability of the application is also challenging and requires lot of resources. Our aim was to deploy the i2b2 for University of Missouri (UM) System in the cloud as well as reduce the complexity and effort of the installation and maintenance process. Our solution encapsulated the complete installation process of each component using docker and deployed the container in the AWS Virtual Private Cloud (VPC) using several AWS PaaS (Platform as a Service), IaaS (Infrastructure as a Service) services. We deployed the application as a service in the AWS FARGATE, an on-demand, serverless, auto scalable compute engine. We also enhanced the functionality of i2b2 services and developed Snowflake JDBC driver support for i2b2 backend services. It enabled i2b2 services to query directly from Snowflake analytical database. In addition, we also created i2b2-data-installer package to load PCORnet CDM and ACT ontology data into i2b2 database. The i2b2 platform in University of Missouri holds 1.26B facts of 2.2M patients of UM Cerner Millennium data.Includes bibliographical references

Deployment and Operation of Complex Software in Heterogeneous Execution Environments

This open access book provides an overview of the work developed within the SODALITE project, which aims at facilitating the deployment and operation of distributed software on top of heterogeneous infrastructures, including cloud, HPC and edge resources. The experts participating in the project describe how SODALITE works and how it can be exploited by end users. While multiple languages and tools are available in the literature to support DevOps teams in the automation of deployment and operation steps, still these activities require specific know-how and skills that cannot be found in average teams. The SODALITE framework tackles this problem by offering modelling and smart editing features to allow those we call Application Ops Experts to work without knowing low level details about the adopted, potentially heterogeneous, infrastructures. The framework offers also mechanisms to verify the quality of the defined models, generate the corresponding executable infrastructural code, automatically wrap application components within proper execution containers, orchestrate all activities concerned with deployment and operation of all system components, and support on-the-fly self-adaptation and refactoring