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.

Secure Cloud-Edge Deployments, with Trust

Assessing the security level of IoT applications to be deployed to heterogeneous Cloud-Edge infrastructures operated by different providers is a non-trivial task. In this article, we present a methodology that permits to express security requirements for IoT applications, as well as infrastructure security capabilities, in a simple and declarative manner, and to automatically obtain an explainable assessment of the security level of the possible application deployments. The methodology also considers the impact of trust relations among different stakeholders using or managing Cloud-Edge infrastructures. A lifelike example is used to showcase the prototyped implementation of the methodology

Cloud Storage and Bioinformatics in a private cloud deployment: Lessons for Data Intensive research

This paper describes service portability for a private cloud deployment, including a detailed case study about Cloud Storage and bioinformatics services developed as part of the Cloud Computing Adoption Framework (CCAF). Our Cloud Storage design and deployment is based on Storage Area Network (SAN) technologies, details of which include functionalities, technical implementation, architecture and user support. Experiments for data services (backup automation, data recovery and data migration) are performed and results confirm backup automation is completed swiftly and is reliable for data-intensive research. The data recovery result confirms that execution time is in proportion to quantity of recovered data, but the failure rate increases in an exponential manner. The data migration result confirms execution time is in proportion to disk volume of migrated data, but again the failure rate increases in an exponential manner. In addition, benefits of CCAF are illustrated using several bioinformatics examples such as tumour modelling, brain imaging, insulin molecules and simulations for medical training. Our Cloud Storage solution described here offers cost reduction, time-saving and user friendliness

Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations

Towards an Automatic Microservices Manager for Hybrid Cloud Edge Environments

Cloud computing came to make computing resources easier to access thus helping a faster deployment of applications/services benefiting from the scalability provided by the service providers. It has been registered an exponential growth of the data volume received by the cloud. This is due to the fact that almost every device used in everyday life are connected to the internet sharing information in a global scale (ex: smartwatches, clocks, cars, industrial equipment’s). Increasing the data volume results in an increased latency in client applications resulting in the degradation of the QoS (Quality of service). With these problems, hybrid systems were born by integrating the cloud resources with the various edge devices between the cloud and edge, Fog/Edge computation. These devices are very heterogeneous, with different resources capabilities (such as memory and computational power), and geographically distributed. Software architectures also evolved and microservice architecture emerged to make application development more flexible and increase their scalability. The Microservices architecture comprehends decomposing monolithic applications into small services each one with a specific functionality and that can be independently developed, deployed and scaled. Due to their small size, microservices are adquate for deployment on Hybrid Cloud/Edge infrastructures. However, the heterogeneity of those deployment locations makes microservices’ management and monitoring rather complex. Monitoring, in particular, is essential when considering that microservices may be replicated and migrated in the cloud/edge infrastructure. The main problem this dissertation aims to contribute is to build an automatic system of microservices management that can be deployed in hybrid infrastructures cloud/fog computing. Such automatic system will allow edge enabled applications to have an adaptive deployment at runtime in response to variations inworkloads and computational resources available. Towards this end, this work is a first step on integrating two existing projects that combined may support an automatic system. One project does the automatic management of microservices but uses only an heavy monitor, Prometheus, as a cloud monitor. The second project is a light adaptive monitor. This thesis integrates the light monitor into the automatic manager of microservices.A computação na Cloud surgiu como forma de simplificar o acesso aos recursos computacionais, permitindo um deployment mais rápido das aplicações e serviços como resultado da escalabilidade suportada pelos provedores de serviços. Computação na cloud surgiu para facilitar o acesso aos recursos de computação provocando um facultamento no deployment de aplicações/serviços sendo benéfico para a escalabilidade fornecida pelos provedores de serviços. Tem-se registado um crescimento exponencial do volume de data que é recebido pela cloud. Este aumento deve-se ao facto de quase todos os dispositivos utilizados no nosso quotidiano estarem conectados à internet (exemplos destes são, relogios, maquinas industriais, carros). Este aumento no volume de dados resulta num aumento da latência para as aplicações cliente, resultando assim numa degradação na qualidade de serviço QoS. Com estes problemas, nasceram os sistemas híbridos, nascidos pela integração dos recursos de cloud com os variados dispositivos presentes no caminho entre a cloud e a periferia denominando-se computação na Edge/Fog (Computação na periferia). Estes dispositivos apresentam uma grande heterogeneidade e são geograficamente muito distribuídos. As arquitecturas dos sistemas também evoluíram emergindo a arquitectura de micro serviços que permitem tornar o desenvolvimento de aplicações não só mais flexivel como para aumentar a sua escalabilidade. A arquitetura de micro serviços consiste na decomposição de aplicações monolíticas em pequenos serviços, onde cada um destes possuí uma funcionalidade específica e que pode ser desenvolvido, lançado e migrado de forma independente. Devido ao seu tamanho os micro serviços são adequados para serem lançados em ambientes de infrastructuras híbridas (cloud e periferia). No entanto, a heterogeneidade da localização para serem lançados torna a gestão e monitorização de micro serviços bastante mais complexa. A monitorização, em particular, é essencial quando consideramos que os micro serviços podem ser replicados e migrados nestas infrastruturas de cloud e periferia (Edge). O problema abordado nesta dissertação é contribuir para a construção de um sistema automático de gestão de micro serviços que podem ser lançados em estruturas hibridas. Este sistema automático irá tornar possível às aplicações que estão na edge possuírem um deploy adaptativo enquanto estão em execução, como resposta às variações dos recursos computacionais disponíveis e suas cargas. Para chegar a este fim, este trabalho será o primeiro passo na integração de dois projectos já existentes que, juntos poderão suportar umsistema automático. Umdeles realiza a gestão automática de micro serviços mas utiliza apenas o Prometheus como monitor na cloud, enquanto o segundo projecto é um monitor leve adaptativo. Esta tese integra então um monitor leve com um gestor automático de micro serviços

System Abstractions for Scalable Application Development at the Edge

Recent years have witnessed an explosive growth of Internet of Things (IoT) devices, which collect or generate huge amounts of data. Given diverse device capabilities and application requirements, data processing takes place across a range of settings, from on-device to a nearby edge server/cloud and remote cloud. Consequently, edge-cloud coordination has been studied extensively from the perspectives of job placement, scheduling and joint optimization. Typical approaches focus on performance optimization for individual applications. This often requires domain knowledge of the applications, but also leads to application-specific solutions. Application development and deployment over diverse scenarios thus incur repetitive manual efforts. There are two overarching challenges to provide system-level support for application development at the edge. First, there is inherent heterogeneity at the device hardware level. The execution settings may range from a small cluster as an edge cloud to on-device inference on embedded devices, differing in hardware capability and programming environments. Further, application performance requirements vary significantly, making it even more difficult to map different applications to already heterogeneous hardware. Second, there are trends towards incorporating edge and cloud and multi-modal data. Together, these add further dimensions to the design space and increase the complexity significantly. In this thesis, we propose a novel framework to simplify application development and deployment over a continuum of edge to cloud. Our framework provides key connections between different dimensions of design considerations, corresponding to the application abstraction, data abstraction and resource management abstraction respectively. First, our framework masks hardware heterogeneity with abstract resource types through containerization, and abstracts away the application processing pipelines into generic flow graphs. Further, our framework further supports a notion of degradable computing for application scenarios at the edge that are driven by multimodal sensory input. Next, as video analytics is the killer app of edge computing, we include a generic data management service between video query systems and a video store to organize video data at the edge. We propose a video data unit abstraction based on a notion of distance between objects in the video, quantifying the semantic similarity among video data. Last, considering concurrent application execution, our framework supports multi-application offloading with device-centric control, with a userspace scheduler service that wraps over the operating system scheduler

Challenges for the comprehensive management of cloud services in a PaaS framework

The 4CaaSt project aims at developing a PaaS framework that enables flexible definition, marketing, deployment and management of Cloud-based services and applications. The major innovations proposed by 4CaaSt are the blueprint and its lifecycle management, a one stop shop for Cloud services and a PaaS level resource management featuring elasticity. 4CaaSt also provides a portfolio of ready to use Cloud native services and Cloud-aware immigrant technologies

Migrating to Cloud-Native Architectures Using Microservices: An Experience Report

Migration to the cloud has been a popular topic in industry and academia in recent years. Despite many benefits that the cloud presents, such as high availability and scalability, most of the on-premise application architectures are not ready to fully exploit the benefits of this environment, and adapting them to this environment is a non-trivial task. Microservices have appeared recently as novel architectural styles that are native to the cloud. These cloud-native architectures can facilitate migrating on-premise architectures to fully benefit from the cloud environments because non-functional attributes, like scalability, are inherent in this style. The existing approaches on cloud migration does not mostly consider cloud-native architectures as their first-class citizens. As a result, the final product may not meet its primary drivers for migration. In this paper, we intend to report our experience and lessons learned in an ongoing project on migrating a monolithic on-premise software architecture to microservices. We concluded that microservices is not a one-fit-all solution as it introduces new complexities to the system, and many factors, such as distribution complexities, should be considered before adopting this style. However, if adopted in a context that needs high flexibility in terms of scalability and availability, it can deliver its promised benefits