https://pureadmin.qub.ac.uk/ws/files/134629726/paper_v2.pdf

Next generation cloud computing

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.

Next Generation Cloud Computing: New Trends and Research Directions

LNBIP reports state-of-the-art results in areas related to business information systems and industrial application software development – timely, at a high level, and in both printed and electronic form.  The type of material published includes  • Proceedings (published in time for the respective event) • Postproceedings (consisting of thoroughly revised and/or extended final papers) • Other edited monographs (such as, for example, project reports or invited volumes) • Tutorials (coherently integrated collections of lectures given at advanced courses, seminars, schools, etc.) • Award-winning or exceptional theses LNBIP is abstracted/indexed in DBLP, EI and Scopus. LNBIP volumes are also submitted for the inclusion in ISI Proceedings.

Lecture notes in business information processing

We present the design of a novel performance-oriented serverless computing platform implemented in. NET, deployed in Microsoft Azure, and utilizing Windows containers as function execution environments. Implementation challenges such as function scaling and container discovery, lifecycle, and reuse are discussed in detail. We propose metrics to evaluate the execution performance of serverless platforms and conduct tests on our prototype as well as AWS Lambda, Azure Functions, Google Cloud Functions, and IBM's deployment of Apache OpenWhisk. Our measurements show the prototype achieving greater throughput than other platforms at most concurrency levels, and we examine the scaling and instance expiration trends in the implementations. Additionally, we discuss the gaps and limitations in our current design, propose possible solutions, and highlight future research.

Serverless Computing: Design, Implementation, and Performance

The accelerating progress of network speed, reliability and security creates an increasing demand to move software and services from being stored and processed locally on users' machines to being managed by third parties that are accessible through the network. This has created the need to develop new software development methods and software architectural styles that meet these new demands. One such example in software architectural design is the recent emergence of the microservices architecture to address the maintenance and scalability demands of online service providers. As microservice architecture is a new research area, the need for a systematic mapping study is crucial in order to summarise the progress so far and identify the gaps and requirements for future studies. In this paper we present a systematic mapping study of microservices architectures and their implementation. Our study focuses on identifying architectural challenges, the architectural diagrams/views and quality attributes related to microsevice systems.

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A Systematic Mapping Study in Microservice Architecture

Cloud computing provides new opportunities to deploy scalable application in an efficient way, allowing enterprise applications to dynamically adjust their computing resources on demand. In this paper we analyze and test the microservice architecture pattern, used during the last years by large Internet companies like Amazon, Netflix and LinkedIn to deploy large applications in the cloud as a set of small services that can be developed, tested, deployed, scaled, operated and upgraded independently, allowing these companies to gain agility, reduce complexity and scale their applications in the cloud in a more efficient way. We present a case study where an enterprise application was developed and deployed in the cloud using a monolithic approach and a microservice architecture using the Play web framework. We show the results of performance tests executed on both applications, and we describe the benefits and challenges that existing enterprises can get and face when they implement microservices in their applications.

Evaluating the monolithic and the microservice architecture pattern to deploy web applications in the cloud

Large Internet companies like Amazon, Netflix, and LinkedIn are using the microservice architecture pattern to deploy large applications in the cloud as a set of small services that can be developed, tested, deployed, scaled, operated and upgraded independently. However, aside from gaining agility, independent development, and scalability, infrastructure costs are a major concern for companies adopting this pattern. This paper presents a cost comparison of a web application developed and deployed using the same scalable scenarios with three different approaches: 1) a monolithic architecture, 2) a microservice architecture operated by the cloud customer, and 3) a microservice architecture operated by the cloud provider. Test results show that microservices can help reduce infrastructure costs in comparison to standard monolithic architectures. Moreover, the use of services specifically designed to deploy and scale microservices reduces infrastructure costs by 70% or more. Lastly, we also describe the challenges we faced while implementing and deploying microservice applications.1

Infrastructure cost comparison of running web applications in the cloud using AWS lambda and monolithic and microservice architectures

Large Internet companies like Amazon, Netflix, and LinkedIn are using the microservice architecture pattern to deploy large applications in the cloud as a set of small services that can be independently developed, tested, deployed, scaled, operated, and upgraded. However, aside from gaining agility, independent development, and scalability, how microservices affect the infrastructure costs is a major evaluation topic for companies adopting this pattern. This paper presents a cost comparison of a web application developed and deployed using the same scalable scenarios with three different approaches: 1) a monolithic architecture, 2) a microservice architecture operated by the cloud customer, and 3) a microservice architecture operated by the cloud provider. Test results show that microservices can help reduce infrastructure costs in comparison with standard monolithic architectures. Moreover, the use of services specifically designed to deploy and scale microservices, such as AWS Lambda, reduces infrastructure costs by 70% or more, and unlike microservices operated by cloud customers, these specialized services help to guarantee the same performance and response times as the number of users increases. Lastly, we also describe the challenges we faced while implementing and deploying microservice applications, and include a discussion on how to replicate the results on other cloud providers.

Cost comparison of running web applications in the cloud using monolithic, microservice, and AWS Lambda architectures

This paper presents UnaCloud: an opportunistic cloud computing Infrastructure as a Service (IaaS) model implementation, which provides at lower cost than dedicated cloud infrastructures, basic computing resources (processing, storage and networking) to run arbitrary software, including operating systems and applications. The IaaS model is provided through the opportunistic use of idle computing resources available in a university campus. UnaCloud deals with the problems associated to use commodity, non-dedicated, distributed, and heterogeneous computing resources that are part of different administrative domains. We propose an IaaS architecture based on two strategies: an opportunistic strategy that allows the use of idle computing resources in a nonintrusive manner, and a virtualization strategy to allow the ondemand deployment of customized execution environments. The proposed solution was implemented and tested through the provision of an opportunistic IaaS model, evidencing high efficiency in the deployment of virtual machines for academic and scientific projects. Keywords; grid computing; cloud computing; desktop grid; infrastructure as a service; unacloud; unagrid.

/pdf/unacloud-opportunistic-cloud-computing-infrastructure-as-a-8teytc5qlj.pdf

UnaCloud: Opportunistic Cloud Computing Infrastructure as a Service

Environmental analysts and researchers’ time is an expensive and scarce resource that should be used efficiently. Creating analysis products from remote sensing images involves several steps that take time and can be either automatized or centralized. Among all these steps, product’s lineage and reproducibility must be assured. We present CDCol, a geoscience data cube that addresses these concerns and fits the analysis needs of Colombian institutions, the forest and carbon monitoring system.

Mario Villamizar

Papers

Evaluating the monolithic and the microservice architecture pattern to deploy web applications in the cloud

Infrastructure cost comparison of running web applications in the cloud using AWS lambda and monolithic and microservice architectures

Cost comparison of running web applications in the cloud using monolithic, microservice, and AWS Lambda architectures

UnaCloud: Opportunistic Cloud Computing Infrastructure as a Service

CDCol: A Geoscience Data Cube that Meets Colombian Needs